Monitoring of advanced cancer patients' health, treatment, and supportive care is essential for improving cancer survival outcomes. Traditionally, oncology has relied on clinical metrics such as survival rates, time to disease progression, and clinician-assessed toxicities. In recent years, patient-reported outcome measures (PROMs) have provided a complementary perspective, offering insights into patients' health-related quality of life (HRQoL). However, collecting PROMs consistently requires frequent clinical assessments, creating important logistical challenges. Wearable devices combined with artificial intelligence (AI) present an innovative solution for continuous, real-time HRQoL monitoring. While deep learning models effectively capture temporal patterns in physiological data, most existing approaches are unimodal, limiting their ability to address patient heterogeneity and complexity. This study introduces a multimodal deep learning approach to estimate HRQoL in advanced cancer patients. Physiological data, such as heart rate and sleep quality collected via wearable devices, are analyzed using a hybrid model combining convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) networks with an attention mechanism. The BiLSTM extracts temporal dynamics, while the attention mechanism highlights key features, and CNNs detect localized patterns. PROMs, including the Hospital Anxiety and Depression Scale (HADS) and the Integrated Palliative Care Outcome Scale (IPOS), are processed through a parallel neural network before being integrated into the physiological data pipeline. The proposed model was validated with data from 204 patients over 42 days, achieving a mean absolute percentage error (MAPE) of 0.24 in HRQoL prediction. These results demonstrate the potential of combining wearable data and PROMs to improve advanced cancer care.

Over the past five years, artificial intelligence (AI) has introduced new models and methods for addressing the challenges associated with the broader adoption of AI models and systems in medicine. This paper reviews recent advances in AI for medical image and video analysis, outlines emerging paradigms, highlights pathways for successful clinical translation, and provides recommendations for future work. Hybrid Convolutional Neural Network (CNN) Transformer architectures now deliver state-of-the-art results in segmentation, classification, reconstruction, synthesis, and registration. Foundation and generative AI models enable the use of transfer learning to smaller datasets with limited ground truth. Federated learning supports privacy-preserving collaboration across institutions. Explainable and trustworthy AI approaches have become essential to foster clinician trust, ensure regulatory compliance, and facilitate ethical deployment. Together, these developments pave the way for integrating AI into radiology, pathology, and wider healthcare workflows.

Although a plethora of research has been published in the literature, providing both qualitative and quantitative analyses of cardiovascular risk, there remains a need to improve interpretability, explainability, and accuracy in the assessment of cardiovascular disease risk. To achieve this, the present study proposes a methodology that extracts knowledge from data to assess cardiovascular disease risk while also offering both local and global explanations that justify the underlying theory made and why in some cases no definite decision can be taken.

Emergency departments (EDs) require effective approaches for quickly identifying patients at risk of critical outcomes (inpatient mortality or ICU admission within 12 hours). This study developed interpretable machine learning models using Gradient Boosting (GB) and Logistic Regression (LR) with 13 initial triage variables from the MIMIC-IV-ED database. GB performed slightly better than LR (Accuracy: 78.21% vs. 77.27%, AUROC: 0.887 vs. 0.863, AUPRC: 0.445 vs. 0.370). The Te2Rules method was used to extract 43 clinically interpretable rules from the GB model, with a overall fidelity of 98.90%. The use of the Rule Coverage Index (RCI) to categorize rules into high, medium, and low levels futher enhanced clinical utility. This study aims to strike a balance between predictive accuracy and interpretability, facilitating clinicians in early detection of critically ill presenting to the ED.

• Non-pharmacological interventions for people with dementia are of great importance. • Virtual Reality found to enhance symptom management in people with dementia. • Virtual Reality found to improve the quality of life of people with dementia. • Virtual Reality was found to be a possible solution for pain management. Emerging research supports that institutionalisation may contribute to the development of the behavioural and psychological symptoms of dementia. Many studies have documented that Virtual Reality can enhance symptom management in people diagnosed with dementia. We design a Virtual Reality system to improve the symptom management of people diagnosed with dementia residing in long-term care services. Twenty people with dementia were enrolled in the study to evaluate the developed solution. Following semi-structured interviews and observations, a thematic analysis was conducted to analyse the results of the system. Heart Rate and Eye-tracking data were recorded to enhance the reliability of the findings. Our findings indicate that Virtual Reality might be able to improve the quality of life of people diagnosed with dementia, as it is highly effective in reducing behavioral and psychological symptoms associated with dementia, including aggression, agitation, anxiety, apathy, and depression. Additionally, Virtual Reality was found to be a possible solution for pain management, reminiscence therapy, and dementia diagnosis.

Abstract Virtual reality has long existed, but its wider adoption in education is recent. Studies informed by theoretical underpinned co-creation frameworks and utilization of theoretical informed evaluations are scarce in literature. Thus, this study internationally evaluated the efficacy of three virtual reality reusable e-resources (VRReRs), co-created based on the ASPIRE framework, for teaching clinical skills to university students. The study followed a mixed-methods approach, combining SUS, SUS Presence Questionnaire, TAM, and UTAUT2 with a focus group discussion. Additionally, for one VRReR, a quantitative pre/post evaluation of knowledge and comparison with lecture notes followed. Results demonstrated moderately to highly usability, effectively facilitated a strong sense of presence, confidence while using them, and willingness to continue using VRReRs in the future, while increased knowledge of the learners, highlighted their effectiveness. Although some usability issues were identified, these were considered easy to address. This work evidence, in an international context, that co-created VR resources are highly acceptable and effective, similar to other types of digital or traditional resources developed through participatory inquiry paradigm. By leveraging the benefits of VR technology, VRReRs have the potential to transform and enhance the learning experience in the field of clinical skills, ultimately advancing the digitalization of higher education.

Artificial Intelligence (AI) advances are pushing the boundaries across research domains with AI-driven solutions in healthcare claiming a significant share. A key objective of these studies concerns the timely prediction of various pathological conditions. Sepsis is a life-threatening syndrome and one of the main causes of death in intensive care unit (ICU) patients. As it becomes a major health problem worldwide, sepsis early prediction could assist healthcare professionals towards making informed clinical decisions, and thereby, significantly reducing the sepsis' morbidity and mortality. A notable body of literature involving the use of AI for sepsis prediction exists. However, to the best of our knowledge, only a handful of studies focus on performing a systematic review of the AI enabled solutions for sepsis prediction in ICUs. In this context, the present paper aims to identify knowledge gaps, stimulate interest and yield motivations for future research. Moreover, to discuss ethical and explainability aspects and associated challenges. The literature search was conducted between February 2023 and April 2023 and considered eligible articles published within the last five years.

The papers in this special section focus on large-scale medical imaging and video analytics for clinical decision support systems. Biomedical images and videos are ubiquitous and overwhelming in volume, amounting to a database that can be measured in zettabytes.With increased access to open image and video datasets and the recent development of effective image and video analysis systems, there is a unique opportunity for the development of artificial intelligence (AI) systems that can be trained and tested on large-scale biomedical image and video databases. The special issue summarizes emerging methods associated with the development of computer-aided diagnostic systems. More specifically, the special issue discusses the development of methods for dealing with small or large or creating new datasets, biomedical image segmentation, and image classification. The development of new dataset methods allows us to develop methods for specific diseases, employ meta-learning for training on small datasets, or develop methods for reducing larger video datasets. Biomedical image segmentation is a primary focus of the special issue.

There is growing need for intelligent digital support in healthcare, business, industry, indeed all domains. Learners in any domain need educators/experts to provide knowledge and guidance by relaying relevant and timely information. Artificial Intelligence (AI) systems such as chatbots or conversational agents, can support information delivery. However, user-centric frameworks for designing chatbot learning applications and collaborative user-centric design methodologies are lacking. This paper describes a chatbot design tool to promote co-creation through crowd-based data collection and design. This prototype has the capacity to be used as a structured method of design and data collection through cocreation for healthcare, education, and beyond.

People living with Dementia (PwD) are amongst the most vulnerable populations in society, often depending on caregivers for their quality of life (QoL). Emerging research confirms the need for technological solutions to support non-pharmacological interventions that can enhance the QoL of PwD. This paper posits that Virtual Reality (VR) is a useful technology for boosting the physical training of PwD. In a study with PwD, we compared the conventional physical training PwD receive at a nursing home, against Semi Immersive VR (SIVR) and Fully Immersive VR (FIVR) training paradigms. We recorded the emotional behaviour, task-specific metrics, and level of independence of PwD during the training. The presence and usability of the systefm by both medical staff and PwD was also assessed. Results indicated that FIVR can improve the training of PwD leading to more accurate execution of the exercises while preventing external distractions. Beyond the findings, this article discusses the opportunities, challenges, along with the feasibility and acceptability of VR, to facilitate physical training for PwD who reside in restricted health care environments.

This article presents a study on the validation of gamified versions of three established cognitive tasks, namely the Corsi task, the Visual Search task, and the Whack a Mole task. Short versions of these tasks were created using gamification techniques and tested on healthy adults. Results showed that these new gamified versions produce similar patterns of results with those obtained from the traditional versions. Concluding, game-based cognitive assessment for visuospatial tasks is promising, however, this needs to be further evaluated on larger scale studies as well as under different cognitive conditions.

The current adoption of Medical Artificial Intelligence (AI) solutions in clinical practice suggest that despite its undeniable potential AI is not achieving this potential. A major barrier to its adoption is the lack of transparency and interpretability, and the inability of the system to explain its results. Explainable AI (XAI) is an emerging field in AI that aims to address these barriers, with the development of new or modified algorithms to enable transparency, provide explanations in a way that humans can understand and foster trust. Numerous XAI techniques have been proposed in the literature, commonly classified as model-agnostic or model-specific. In this study, we examine the application of four model-agnostic XAI techniques (LIME, SHAP, ANCHORS, inTrees) to an XGBoost classifier trained on real-life medical data for the prediction of high-risk asymptomatic carotid plaques based on ultrasound image analysis. We present and compare local explanations for selected observations in the test set. We also present global explanations generated from these techniques that explain the behavior of the entire model. Additionally, we assess the quality of the explanations, using suggested properties in the literature. Finally, we discuss the results of this comparative study and suggest directions for future work.

A plethora of machine learning and deep learning methods are used for the assessment of Alzheimer's Disease (AD) from brain structural changes as seen in Magnetic Resonance Imaging (MRI) with highly satisfactory results. However, these models are black-box and lack an explicit declarative knowledge representation and thus there is a difficulty in generating the underlying explanatory imaging structures. The objective of this study was to investigate the usefulness of rule extraction in the assessment of AD using decision trees (DT) and random forests (RF) algorithms and integrating the extracted rules within an argumentation-based reasoning framework in order to make the results easy to interpret and explain. The DT and RF algorithms were applied on brain MRI images acquired from normal controls (NC) and AD subjects. The KNIME analytics platform was used to compute the DT and the R project was used for the RF. The argumentation model implemented in the Gorgias framework achieved an average accuracy of 91%, exhibiting improved results compared to the models of DT and RF. The overall performance of all models in this study is in agreement with other studies. In addition, the explanations given by our approach for the various possible predictions provide a more useful and complete assessment of the state of the patient/case at hand. This study demonstrated the usefulness of rule extraction in the assessment of AD based on MRI features and the positive results of the use of the argumentation based symbolic reasoning for composing and interpreting the ML results.

3D medical imaging provides an invaluable tool to the radiologist in visualizing normal and abnormal tissue and structure for the assessment of disease and treatment planning. Moreover, in difficult image disease assessment cases, as well as in the assessment of the early stages of the disease the need exists for a real time 3D collaborative platform. The objective of this paper was to develop a multi-user Virtual Reality (VR) application for visualization and analysis in medical imaging. The proposed platform is based on the Unity VR platform that is integrated with the very well-known and popular image Visualization Toolkit- VTK. The platform was evaluated successfully in the 3D visualization of images of the ADNI dataset. Future work will focus in integrating in the platform the visualization of quantitative imaging analytics as well as the evaluation of the platform in a more-wide spectrum of imaging cases.

This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine.

Despite the recent recognition of the value of Artificial Intelligence and Machine Learning in healthcare, barriers to further adoption remain, mainly due to their "black box" nature and the algorithm's inability to explain its results. In this paper we present and propose a methodology of applying argumentation on top of machine learning to build explainable AI (XAI) models. We compare our results with Random Forests and an SVM classifier that was considered best for the same dataset in [1].

Precision medicine promises better healthcare delivery by improving clinical practice. Using evidence-based substratification of patients, the objective is to achieve better prognosis, diagnosis, and treatment that will transform existing clinical pathways toward optimizing care for the specific needs of each patient. The wealth of today's healthcare data, often characterized as big data, provides invaluable resources toward new knowledge discovery that has the potential to advance precision medicine. The latter requires interdisciplinary efforts that will capitalize the information, know-how, and medical data of newly formed groups fusing different backgrounds and expertise. The objective of this paper is to provide insights with respect to the state-of-the-art research in precision medicine. More specifically, our goal is to highlight the fundamental challenges in emerging fields of radiomics and radiogenomics by reviewing the case studies of Cancer and Alzheimer's disease, describe the computational challenges from a big data analytics perspective, and discuss standardization and open data initiatives that will facilitate the adoption of precision medicine methods and practices.

The seven papers in this special section examine the latest advances in the field of integrated precision medicine technologies. In the majority of medical conditions common therapeutic approaches are usually effective in only a small percentage of the patient population. Recent scientific discoveries implicate as possible causes of this lack of effect the multifactorial nature of most diseases and the patient variability in disease expression, genetic disposition, and environmental exposures. It has become apparent that in order to improve the response to therapy and long term prognosis, treatment must be specifically tailored to the disease and the patient. Precision medicine is an attempt to maximize effectiveness by taking into account individual variability in clinical presentation, medical history, genes, environment, and lifestyle. It is a leap beyond the promise of “personalization” empowered by recent technological advances. However, progress in precision medicine has been slow due to the lack of “precision” in the traditional research, translation, and clinical practice. Current approaches are largely empirical, fragmented, lack integration, and rely on population statistics, with inadequate feedback between disciplines. In addition, most of the information required for personalization is either missing or unutilized. New technological developments can help overcome these hurdles of imprecision to achieve the full promise of precision medicine.

The wider adoption of mobile Health video communication systems in standard clinical practice requires real-time control to provide for adequate levels of clinical video quality to support reliable diagnosis. The latter can only be achieved with real-time adaptation to time-varying wireless networks' state to guarantee clinically acceptable performance throughout the streaming session, while conforming to device capabilities for supporting real-time encoding. We propose an adaptive video encoding framework based on multi-objective optimization that jointly maximizes the encoded video's quality and encoding rate (in frames per second) while minimizing bitrate demands. For this purpose, we construct a dense encoding space and use linear regression to estimate forward prediction models for quality, bitrate, and computational complexity. The prediction models are then used in an adaptive control framework that can fine-tune video encoding based on real-time constraints. We validate the system using a leave-one-out algorithm applied to ten ultrasound videos of the common carotid artery. The prediction models can estimate structural similarity quality with a median accuracy error of less than 1%, bitrate demands with deviation error of 10% or less, and encoding frame rate within a 6% margin. Real-time adaptation at a group of pictures level is demonstrated using the high efficiency video coding standard. The effectiveness of the proposed framework compared to static, nonadaptive approaches is demonstrated for different modes of operation, achieving significant quality gains, bitrate demands reductions, and performance improvements, in real-life scenarios imposing time-varying constraints. Our approach is generic and should be applicable to other medical video modalities with different applications.

Self-adaptation is an efficient way to control the strategy parameters of an Evolutionary Algorithm automatically during optimization. It is based on implicit evolutionary search in the space of strategy parameters, and has been proven to work well as on-line parameter control method for a variety of strategy parameters, from local to global ones. Our proposed Self-Adaptive Multi-Objective Evolutionary Algorithm is a two level algorithm. The proposed solution is applied on the problem of de novo molecular design. The outer level is the algorithm that is responsible for the self adaptive techniques and is based on Multi-Objective Genetic Algorithm. The inner level is based on the elite Multi-Objective Evolutionary Graph Algorithm. Both the outer and inner algorithms are variations of our previously proposed Multi-Objective Evolutionary Graph Algorithm framework. The outer Multi-Objective Genetic Algorithm operates on a chromosome of elements, while the inner elite Multi-Objective Evolutionary Graph Algorithm operates on molecular graph chromosomes. In general, the proposed solution: (i) searches a larger space, (ii) generates far more solutions per iteration, (iii) evaluates different sets of parameter options for the given problem, and (iv) proposes the fittest parameter sets that should be used for the given problem.

Through this study we are presenting the initial steps towards a real time motion analysis system to predict the stability of carotid bifurcation plaques. The analysis is performed on B-mode video loops. Loops are analyzed in order to follow systole and diastole sections of the cardiac cycle and trace the motion of plaques during these periods. We had created a system that applies Farnebacks optical flow estimation method in order to estimate the flow between consecutive frames or frames at a predefined interval. Over each pair of video frames we measure velocities, orientation and magnitude of movement. The goal is to identify if a plaque has movement spread to different angles or at nearby angles. This can help us identify discordant or concordant movement. In order to verify our system we had created a set of simulated videos that have structures moving in a similar way as done in a cardiac cycle and videos that move and appear as an atherosclerotic artery. Following these tests the system has been tested and results are presented on two carotid plaques videos classified visually as having concordant and discordant plaque movement.

During a human brain MRI acquisition the resulting image is formed out of 2D slices. The slices must then be aligned and reconstructed to provide a 3-dimensional (3D) visualization of the brain volume. We propose in this work, an integrated system for the register ion and 3D reconstruction of DICOM MRI images and lesions of the brain acquired from multiple sclerosis (MS) subjects at two different time intervals (time 0 (T0) and time 1 (T1)). The system facilitates the follow up of the MS disease development and will aid the doctor to accurately manage the follow up of the disease. It involves a 6-stage analysis (preprocessing, lesion segmentation, registration, 3D reconstruction, volume estimation and method evaluation), as well as module quantitative evaluation of the method. The system was evaluated based on one MRI phantom and one DICOM MRI image of the brain. The accuracy of the proposed registration and reconstruction (- / -) method was 78.5%/97.2% and 95.4%/95.8% for the phantom and the MRI images respectively. These preliminary results provide evidence that the proposed system could be applied in future in the clinical practice.

The paper presents the development of a computer-aided diagnostic (CAD) system for the early detection of endometrial cancer. The proposed CAD system supports reproducibility through texture feature standardization, standardized multifeature selection, and provides physicians with comparative distributions of the extracted texture features. The CAD system was validated using 516 regions of interest (ROIs) extracted from 52 subjects. The ROIs were equally distributed among normal and abnormal cases. To support reproducibility, the RGB images were first gamma corrected and then converted into HSV and YCrCb. From each channel of the gamma-corrected YCrCb, HSV, and RGB color systems, we extracted the following texture features: 1) statistical features (SFs), 2) spatial gray-level dependence matrices (SGLDM), and 3) gray-level difference statistics (GLDS). The texture features were then used as inputs with support vector machines (SVMs) and the probabilistic neural network (PNN) classifiers. After accounting for multiple comparisons, texture features extracted from abnormal ROIs were found to be significantly different than texture features extracted from normal ROIs. Compared to texture features extracted from normal ROIs, abnormal ROIs were characterized by lower image intensity, while variance, entropy, and contrast gave higher values. In terms of ROI classification, the best results were achieved by using SF and GLDS features with an SVM classifier. For this combination, the proposed CAD system achieved an 81% correct classification rate.

The recent emergence of the high-efficiency video coding (HEVC) standard promises to deliver significant bitrate savings over current and prior video compression standards, while also supporting higher resolutions that can meet the clinical acquisition spatiotemporal settings. The effective application of HEVC to medical ultrasound necessitates a careful evaluation of strict clinical criteria that guarantee that clinical quality will not be sacrificed in the compression process. Furthermore, the potential use of despeckle filtering prior to compression provides for the possibility of significant additional bitrate savings that have not been previously considered. This paper provides a thorough comparison of the use of MPEG-2, H.263, MPEG-4, H.264/AVC, and HEVC for compressing atherosclerotic plaque ultrasound videos. For the comparisons, we use both subjective and objective criteria based on plaque structure and motion. For comparable clinical video quality, experimental evaluation on ten videos demonstrates that HEVC reduces bitrate requirements by as much as 33.2% compared to H.264/AVC and up to 71% compared to MPEG-2. The use of despeckle filtering prior to compression is also investigated as a method that can reduce bitrate requirements through the removal of higher frequency components without sacrificing clinical quality. Based on the use of three despeckle filtering methods with both H.264/AVC and HEVC, we find that prior filtering can yield additional significant bitrate savings. The best performing despeckle filter (DsFlsmv) achieves bitrate savings of 43.6% and 39.2% compared to standard nonfiltered HEVC and H.264/AVC encoding, respectively.

Applying engineering precepts to biological systems has spawn the field of systems biology to investigate a network of interacting components, including the coordination of internal systems of living organisms such as endocrine, nervous, and respiratory with gene and gene product expression, and behavior and environmental factors, and understand how these components together contribute to the disease initiation and progression, biological development, and health. Proceeding from systems biology, systems medicine incorporates complex and dynamic biochemical, physiological, and environmental interactions between all components of disease and health that sustain living organisms. The current special issue includes a selected number of papers presented at the 12th IEEE International Conference on BioInformatics and BioEngineering (BIBE 2012), Nov. 11-13, 2012 under a special session with the same theme, in addition to papers submitted following an open call for papers. The Special Issue presents experiences as well as technological and scientific developments stemming from some flagship projects funded by the EU under the FP7 framework programme aiming to bring together researchers working in the fields of infrastructures and technologies for integrative biomedical research, ICT for predictive and translational medicine and the VPH community at large. A total of 15 papers are included under the following scientific subdomains: 1) mHealth,Wearable Systems and Telemonitoring Services (five papers), 2) Medical Imaging (four papers), and 3) Computational Biology (six papers).

Quantitative color tissue analysis in endoscopy examinations requires color standardization procedures to be applied, so as to enable compatibility among computer aided diagnosis application from different endoscopy labs. The objective of this study was to examine the usefulness of different color correction algorithms (thus facilitating color standardization), evaluated on four different endoscopy cameras. The following five color correction algorithms were investigated: two gamma correction based algorithms (the classical and a modified one), and three (2nd, 3rd, and 4thorder) polynomial based correction algorithms. The above algorithms were applied to four different endoscopy cameras: (a) Circon, (b) Karl-Stortz, (c) Olympus, and (d) Snowden-Pencer. The color correction algorithms and the endoscopic cameras evaluation, was carried out using the testing color palette (24 colors of known digital values) provided by the Edmund Industrial Optics Company. In summary, we have that: (a) the modified gamma correction algorithm gave significantly smaller mean square error compared to the other four algorithms, and (b) the smallest mean square error was obtained for the Circon camera. Future work will focus on evaluating the proposed color correction algorithm in different endoscopy clinics and compare their tissue characterization results.

In this paper, an architecture is presented that allows the extraction of argumentation clauses that might exist in publications, in order to perform molecular clustering on referenced molecules. Grammar rules are defined and used to identify sentences corresponding to argumentation being present in publications. The references of those molecules are then compiled as lists that include their structure definition in SMILES format. These lists are given as input to virtual screening tools and then to a molecular clustering tool, with the ultimate goal to classify molecules that are known to be prone to specific diseases, thus leading to the discovery of new drugs.

The intima-media thickness (IMT) of the common carotid artery (CCA) is an established indicator of cardiovascular disease (CVD). There have been reports about the difference between the left and the right sides of the CCA IMT and its importance when evaluated with various risk factors as well as their association with the risk of stroke. In this study, we use an automated system based on snakes, for segmenting the CCA and perform measurements of the IMT of the carotid artery and provide their differences between the left and right sides. The study was performed on 205 longitudinal-section ultrasound images acquired from 87 men and 118 women at a mean±SD age of 63±10.47 years, out of which 51 had cardiovascular symptoms. A cardiovascular expert manually measured the IMT on the left CCA side (mean±standard deviation = 0.79±0.21 mm) and the right CCA side (0.76±0.33 mm). The left and right IMT automated measurements were 0.70±0.15 mm and 0.66±0.15 mm, respectively. We found no statistical significant differences: 1) between the left and right IMT measurements, for both the manual and automated measurements, and 2) between the manual and automated measurements for both sides. These findings suggest that the measurement of the CCA IMT on one side only is enough (and this is in agreement with other studies), as well as automated measurements can be used.

Breasts are composed of a mixture of fibrous and glandular tissue as well as adipose tissue and breast density describes the prevalence of fibroglandular tissue as it appears on a mammogram. Over the past few years, evaluation and reporting of breast density as it appears on mammograms has received a lot of attention because it impacts one's risk of developing breast cancer but also the capability of detecting breast cancer on mammograms. In addition, mammography fails in the identification of breast cancer in almost half of the women with dense breasts. Different image analysis methods have been investigated for automatic breast density classification. The presented method investigates the use of AmplitudeModulation Frequency-Modulation (AM-FM) multi-scale feature sets for characterization of breast density as the first step in the development of a density specific Computer Aided Detection System. AM-FM decompositions use different scales and bandpass filters to extract the instantaneous frequencies (IF), instantaneous amplitude (IA) and instantaneous phase (IP) components from an image. Normalized histograms of the maximum IA across all frequencies and scales are used to model the different breast density classes. Classification of a new mammogram into one of the breast density classes is achieved using the k-nearest neighbor method with k =5 and the euclidean distance metric. The method is evaluated on the Medical Image Analysis Society (MIAS) mammographic database and the results are presented. The presented method allows breast density classification accuracy reaching over 84%. Future work will involve a new AM-FM methodology approach based on adaptive filterbank design and performance index decision.

In this study, we describe an effective video communication framework for the wireless transmission of H.264/AVC medical ultrasound video over mobile WiMAX networks. Medical ultrasound video is encoded using diagnostically-driven, error resilient encoding, where quantization levels are varied as a function of the diagnostic significance of each image region. We demonstrate how our proposed system allows for the transmission of high-resolution clinical video that is encoded at the clinical acquisition resolution and can then be decoded with low-delay. To validate performance, we perform OPNET simulations of mobile WiMAX Medium Access Control (MAC) and Physical (PHY) layers characteristics that include service prioritization classes, different modulation and coding schemes, fading channels conditions, and mobility. We encode the medical ultrasound videos at the 4CIF (704 × 576) resolution that can accommodate clinical acquisition that is typically performed at lower resolutions. Video quality assessment is based on both clinical (subjective) and objective evaluations.

A simple yet powerful programming tool enabling in silico experimentation, end-to-end data management through web services as well as use of grid and cloud processing power is scientific workflows. This technology is receiving considerable interest in recent years primarily due to its ability to promote and support scientific collaboration among large distributed research teams. The paper reviews the Scientific Workflows Management Systems (SWMS) field and investigates in detail popular open source workflow systems used commonly in life sciences informatics. Emphasis is placed on features which make these systems attractive for scientific use, e.g. user friendliness, use of distributed resources, reusability, provenance, collaboration, data integration, etc. Our conclusions indicate that although SWMS, including open source ones, have several open issues, their unique features and strong momentum clearly suggest that it is only a matter of time before they are adopted in even more scientific fields.

Electronic Health Records (EHRs) contain an increasing wealth of medical information. They have the potential to help significantly in advancing medical research, as well as improve health policies, providing society with additional benefits. However, the European healthcare information space is fragmented due to the lack of legal and technical standards, cost effective platforms, and sustainable business models. The vision of Linked2Safety is to advance clinical practice and accelerate medical research, by providing pharmaceutical companies, healthcare professionals and patients with an innovative secure semantic interoperability framework facilitating the efficient and homogenized access to anonymised distributed EHRs in an aggregate form that enables merging multiple data sources into a single analyses. In this paper a first public introduction to the project is provided along with a clear definition of the problems, and proposed architecture. Three usage scenarios are used to demonstrate the potential impact of the outcomes of the project.

The key test for confidence in any association discovered within the medical domain is replication testing. That is, the ability of the association to be detected in independent populations. At the same time, in order to increase the likelihood of discovering statistically significant associations there is a clear need to increase the statistical power of any given study. A key methodology for increasing statistical power is through the use of as many subjects as possible that match a study's inclusion criteria. Thus many have attempted to merge data from multiple independent sources/sites/studies that contain the same inclusion criteria for subjects as a way of creating a much larger study with significantly more statistical power. For these approaches to work though data from multiple sites need to be made available to a single analysis. This practice is significantly limited by the need to respect legal and ethical requirements that are often complicated, ambiguous and inconsistent across different countries. The common approach to achieve merging of data is by sharing aggregated data rather than subject's personal data. Aggregated data however may still in some cases be reverse engineered, therefore traditionally cells within the aggregated data with small values were suppressed, and some or all of the aggregated data were perturbed in order to add noise inhibiting any attempts at identifying personal information of a specific person or sub-group in the original data. In this paper we study the effects of cell-suppression and perturbation on the results of the data analysis. Each approach is looked at by itself as well as in combination using the typical settings documented in the literature. The tests are based on a real dataset that looks for associations between phenotypes and genetic markers. This work is part of the Linked2Safety project that aims to dynamically interconnect distributed patients' data to better enable medical research efforts, whilst respecting patients' anonymity, as well as European and national legislation.

Several machine learning techniques have been applied for finding multi-loci associations among Single Nucleotide Polymorphisms (SNPs) and a disease. In this paper it is investigated whether Self Organizing Maps (SOMs) can generate clusters associated with a disease based on the genetic patterns of subjects. A batch categorical SOM that can handle missing data was used on Genome Wide Association (GWA) data on Multiple Sclerosis (MS). The association of the clusters generated with the disease were initially tested using the Pearson's chi square test and then the weights of the top clusters were used for investigating for SNP patterns. The results of the analyses reveal statistically significant associations between the generated clusters and the disease, indicating that SOMs can be used for multi-loci associations.

The use of multiscale AM-FM analysis systems has been recently demonstrated in a variety of applications in medical image analysis. In all of these applications, a fixed filter-bank is used as a preprocessing step for estimating different AM-FM components from different scales. In this paper, for the first time, we introduce the use of an adaptive, multiscale AM-FM approach that searches for the optimal filter-bank specification for use in image classification. We demonstrate an example application in hysteroscopy imaging, for identification of gynaecological cancer, where the optimal filter-bank turns out to be circularly symmetric.

This paper proposes an MPEG-7-based ultrasound image retrieval system which can assist in studying and analyzing images from atherosclerotic carotid plaques. The proposed system is able to retrieve and sort ultrasound images of the atherosclerotic carotid plaques using image texture analysis features and the KNN classification algorithm. The prototype system was evaluated on a database of 274 images of symptomatic and asymptomatic plaques and the results show the efficiency and efficacy of the proposed approach. The best percentage success rate of correct retrievals was obtained for the NGTDM, followed by the SGLDM (range) feature sets with the corresponding scorings of 70%, and 68%, respectively. The proposed system can be very useful to physicians who, in order to select the best treatment strategy and therapy for a patient, need to find and analyze related cases.

Computer-aided drug discovery techniques have been widely used in recent years to support the development of new pharmaceuticals. Virtual screening, the computational counterpart of experimental screening, attempts to replicate the results from in vitro and in vivo methods through the use of in silico models and tools. This paper presents the LISIs platform; a web based scientific workflow system for virtual screening that has been implemented primarily for the discovery of chemoprevention agents. We describe the overall design of the system as well as the implementation of its various components. Indicative results from early applications of the system are also presented to illustrate its potential uses and functionalities.

Mammographic breast density refers to the prevalence of fibroglandular tissue as it appears on a mammogram. Breast density is not only an important risk for developing breast cancer but can also mask abnormalities. Breast density information can be used for planning individualized screening and treatment. In this work, statistical distributions of different texture descriptors and their combination are investigated with Support Vector Machines (SVMs) for objective breast density classification: Scale Invariant Feature Transforms (SIFT), Local Binary Patterns (LBP) and texton histograms. SIFT is an approach for detecting and extracting local feature descriptors that are reasonably invariant to changes in illumination, image noise, rotation, scaling and small changes in viewpoint. The SIFT descriptor is a coarse descriptor of the edges found in the keypoints. LBPs provide a robust and computationally simple way for describing pure local binary patterns in a texture. They provide information regarding the prevalence of different edge patterns and uniformity. Textons are defined under the operational definition of clustered filter responses and provide a statistical and structural unifying approach for texture characterization. The breast density classification accuracy of the SVM classifiers modeled on the histograms of the three different sets of texture features separately and their combination is evaluated on the Medical Image Analysis Society (MIAS) mammographic database and the results are presented. The combination of the statistical distributions of all the different texture features allows for the highest classification accuracy, reaching over 93%.

Noise reduction is essential for increasing the visual quality or as a preprocessing step for further automated analysis in video sequences and video coding. The objective of this work was to investigate four different video despeckle filtering techniques and evaluate them using visual assessment by two medical experts, texture features analysis, and video quality evaluation metrics. The four proposed video despeckle filtering techniques were evaluated on 10 ultrasound videos of the common carotid artery (CCA). The filters were applied on the whole video frame and in a selected by the user region of interest (ROI) which included the atherosclerotic carotid plaque. The despeckle filters were based on linear filtering (DsFlsmv), hybrid median filtering (DsFhmedian), nonlinear filtering (DsFkuwahara) and speckle reducing anisotropic diffusion (DsFsrad) filtering. Our results showed that, the best video despeckle filtering methods were the linear filter DsFlsmv, followed by the hybrid median filter DsFhmedian. Both filters improved the visual perception evaluation by experts and gave better texture and video quality metrics. Further work on a larger number of videos and by employing additional despeckle filtering techniques is required for the evaluation of video despeckle filtering methods on ultrasound videos of the CCA.

The correct identification of the intima-media thickness (IMT) of the common carotid artery (CCA) walls has a high clinical relevance as it represents one of the most reliable predictor for future cardiovascular events. In this work we propose and evaluate an integrated system for the segmentation of the intima-media complex (IMC) and the lumen diameter in longitudinal ultrasound video of the CCA based on normalization, speckle reduction filtering (with a first order statistics filter) and snakes segmentation. The algorithm is initialized in the first video frame of the cardiac cycle by an automated initialization procedure and the borders of the far wall and near wall of the CCA are estimated. The IMC and the carotid diameter are then segmented automatically in the consecutive video frames for one cardiac cycle. The proposed algorithm was evaluated on 10 longitudinal ultrasound B-mode videos of the CCA and is compared with the manual tracings of a neurovascular expert, for every 20 frames in a time span of 3-5 seconds, covering in general 1-2 cardiac cycles. The algorithm estimated an IMTmean± standard deviation of (0.72±0.22) mm while the manual results were (0.70±0.19). The mean maximum and minimum diameter was (7.08±1.37) mm and (6.53±1.13) mm respectively. The results were validated based on statistical measures and univariate statistical analysis. It was shown that there was no significant difference between the snakes segmentation measurements and the manual measurements. The proposed integrated system could successfully segment the IMC in ultrasound CCA video sequences thus complementing manual measurements.

Video loops of B-mode ultrasound images of 35 carotid bifurcation plaques were obtained (4 symptomatic and 31 asymptomatic) from patients with carotid bifurcation atherosclerosis. Video loops were classified visually as showing concordant (n=22) or discordant motion (n=13). Concordant plaques were characterized by uniform orientation of motion throughout the cardiac cycle. Discordant plaques exhibited significant spread in motion orientation at different parts of the cardiac cycle, especially at systole. We developed a real-time motion analysis system that applies Farneback's method to estimate velocities between consecutive video frames. For our purposes, we allow a 100msec time interval between the video frames used in the analysis. This approach allows us to analyze significant motions associated with a larger time interval. Over each video frame, we measure the spread of the motion orientation around the dominant orientation. For each video, we look at the spreads of the motion orientations for different motion magnitudes. Using these motion-spread measurements, we can quantify discordant movement. The sum of maximum fan widths for the median pixel motions 5 to 3 (SMFW5to3) had a median value of 100 degrees and interquartile range (IQR) of (80, 110) degrees for the concordant plaques and 270, (230, 430) for the discordant plaques (P < 0.001). Thus, we have a new tool to differentiate between concordant and discordant plaques.

The aim of this paper is to describe a novel and completely automated technique for carotid artery (CA) recognition, far (distal) wall segmentation, and intima-media thickness (IMT) measurement, which is a strong clinical tool for risk assessment for cardiovascular diseases. The architecture of completely automated multiresolution edge snapper (CAMES) consists of the following two stages: 1) automated CA recognition based on a combination of scale-space and statistical classification in a multiresolution framework and 2) automated segmentation of lumen-intima (LI) and media-adventitia (MA) interfaces for the far (distal) wall and IMT measurement. Our database of 365 B-mode longitudinal carotid images is taken from four different institutions covering different ethnic backgrounds. The ground-truth (GT) database was the average manual segmentation from three clinical experts. The mean distance ± standard deviation of CAMES with respect to GT profiles for LI and MA interfaces were 0.081 ± 0.099 and 0.082 ± 0.197 mm, respectively. The IMT measurement error between CAMES and GT was 0.078 ± 0.112 mm. CAMES was benchmarked against a previously developed automated technique based on an integrated approach using feature-based extraction and classifier (CALEX). Although CAMES underestimated the IMT value, it had shown a strong improvement in segmentation errors against CALEX for LI and MA interfaces by 8% and 42%, respectively. The overall IMT measurement bias for CAMES improved by 36% against CALEX. Finally, this paper demonstrated that the figure-of-merit of CAMES was 95.8% compared with 87.4% for CALEX. The combination of multiresolution CA recognition and far-wall segmentation led to an automated, low-complexity, real-time, and accurate technique for carotid IMT measurement. Validation on a multiethnic/multi-institutional data set demonstrated the robustness of the technique, which can constitute a clinically valid IMT measurement for assistance in atherosclerosis disease management.

Despite enormous efforts to prevent cardiovascular disease (CVD) in the past, it remains the leading cause of death in most countries worldwide. Around two-thirds of these deaths are due to acute events, which frequently occur suddenly and are often fatal before medical care can be given. New strategies for screening and early intervening CVD, in addition to the conventional methods, are therefore needed in order to provide personalized and pervasive healthcare. In this special issue, selected emerging technologies in health informatics for screening and intervening CVDs are reported. These papers include reviews or original contributions on 1) new potential genetic biomarkers for screening CVD outcomes and high-throughput techniques for mining genomic data; 2) new imaging techniques for obtaining faster and higher resolution images of cardiovascular imaging biomarkers such as the cardiac chambers and atherosclerotic plaques in coronary arteries, as well as possible automatic segmentation, identification, or fusion algorithms; 3) new physiological biomarkers and novel wearable and home healthcare technologies for monitoring them in daily lives; 4) new personalized prediction models of plaque formation and progression or CVD outcomes; and 5) quantifiable indices and wearable systems to measure them for early intervention of CVD through lifestyle changes. It is hoped that the proposed technologies and systems covered in this special issue can result in improved CVD management and treatment at the point of need, offering a better quality of life to the patient.

Carotid plaques have been associated with ipsilateral neurological symptoms. High-resolution ultrasound can provide information not only on the degree of carotid artery stenosis but also on the characteristics of the arterial wall including the size and consistency of atherosclerotic plaques. The aim of this study is to determine whether the addition of ultrasonic plaque texture features to clinical features in patients with asymptomatic internal carotid artery stenosis (ACS) improves the ability to identify plaques that will produce stroke. 1121 patients with ACS have been scanned with ultrasound and followed for a mean of 4 years. It is shown that the combination of texture features based on second-order statistics spatial gray level dependence matrices (SGLDM) and clinical factors improves stroke prediction (by correctly predicting 89 out of the 108 cases that were symptomatic). Here, the best classification results of 77 ±1.8% were obtained from the use of the SGLDM texture features with support vector machine classifiers. The combination of morphological features with clinical features gave slightly worse classification results of 76 ±2.6% . These findings need to be further validated in additional prospective studies.

The intima-media thickness (IMT) of the common carotid artery (CCA) is widely used as an early indicator of cardiovascular disease (CVD). Clinically, there is strong interest in identifying how the composition and texture of the media layer (ML) can be associated with the risk of stroke. In this study, we use 2-D amplitude-modulation frequency-modulation (AM-FM) analysis of the intima-media complex (IMC), the ML, and intima layer (IL) of the CCA to detect texture changes as a function of age and sex. The study was performed on 100 ultrasound images acquired from asymptomatic subjects at risk of atherosclerosis. To investigate texture variations associated with age, we separated them into three age groups: 1) patients younger than 50; 2) patients aged between 50 and 60 years old; and 3) patients over 60 years old. We also separated the patients by sex. The IMC, ML, and IL were segmented manually by a neurovascular expert and also by a snake-based segmentation system. To reject strong edge artifacts, we prefilter with an AM-FM filterbank that is centered along the horizontal frequency axis (parallel to the long axis of the IMC, ML, and IL), while removing the low-pass filter estimates and frequency bands with large, vertical frequency components. To investigate significant texture changes, we extract the instantaneous amplitude (IA) and the magnitude of the instantaneous frequency (IF) over each layer component, for low-, medium-, and high-frequency AM-FM components. We detected significant texture differences between the higher risk age group of >60 years versus the lower risk age group of <50 and the 50-60 group. In particular, between the <50 and >60 groups, we found significant differences in the medium-scale IA extracted from the IMC. Between the >60 and the 50-60 groups, we found significant texture changes in the low-scale IA and high-scale IF magnitude extracted from the IMC, and the low-scale IA extracted from the IL. Also, we noted that the IA for the ML showed significant differences between males and females for all age groups. The AM--FM features provide complimentary information to classical texture analysis features like the gray-scale median, contrast, and coarseness. These findings provide evidence that AM--FM texture features can be associated with the progression of cardiovascular risk for disease and the risk of stroke with age. However, a larger scale study is needed to establish the application in clinical practice.

This study introduces the use of multiscale amplitude modulation-frequency modulation (AM-FM) texture analysis of multiple sclerosis (MS) using magnetic resonance (MR) images from brain. Clinically, there is interest in identifying potential associations between lesion texture and disease progression, and in relating texture features with relevant clinical indexes, such as the expanded disability status scale (EDSS). This longitudinal study explores the application of 2-D AM-FM analysis of brain white matter MS lesions to quantify and monitor disease load. To this end, MS lesions and normal-appearing white matter (NAWM) from MS patients, as well as normal white matter (NWM) from healthy volunteers, were segmented on transverse T2-weighted images obtained from serial brain MR imaging (MRI) scans (0 and 6-12 months). The instantaneous amplitude (IA), the magnitude of the instantaneous frequency (IF), and the IF angle were extracted from each segmented region at different scales. The findings suggest that AM-FM characteristics succeed in differentiating 1) between NWM and lesions; 2) between NAWM and lesions; and 3) between NWM and NAWM. A support vector machine (SVM) classifier succeeded in differentiating between patients that, two years after the initial MRI scan, acquired an EDSS ≤ 2 from those with EDSS > 2 (correct classification rate = 86%). The best classification results were obtained from including the combination of the low-scale IA and IF magnitude with the medium-scale IA. The AM-FM features provide complementary information to classical texture analysis features like the gray-scale median, contrast, and coarseness. The findings of this study provide evidence that AM-FM features may have a potential role as surrogate markers of lesion load in MS.

We propose a unifying framework for efficient encoding, transmission, and quality assessment of atherosclerotic plaque ultrasound video. The approach is based on a spatially varying encoding scheme, where video-slice quantization parameters are varied as a function of diagnostic significance. Video slices are automatically set based on a segmentation algorithm. They are then encoded using a modified version of H.264/AVC flexible macroblock ordering (FMO) technique that allows variable quality slice encoding and redundant slices (RSs) for resilience over error-prone transmission channels. We evaluate our scheme on a representative collection of ten ultrasound videos of the carotid artery for packet loss rates up to 30%. Extensive simulations incorporating three FMO encoding methods, different quantization parameters, and different packet loss scenarios are investigated. Quality assessment is based on a new clinical rating system that provides independent evaluations of the different parts of the video (subjective). We also use objective video-quality assessment metrics and estimate their correlation to the clinical quality assessment of plaque type. We find that some objective quality assessment measures computed over the plaque video slices gave very good correlations to mean opinion scores (MOSs). Here, MOSs were computed using two medical experts. Experimental results show that the proposed method achieves enhanced performance in noisy environments, while at the same time achieving significant bandwidth demands reductions, providing transmission over 3G (and beyond) wireless networks.

The 20 papers in this special issue were originally presented in the International Special Topic Conference on Information Technology in Biomedicine, held in October 2009, in Larnaka, Cyprus.

Coronary heart disease (CHD) is one of the major causes of disability in adults as well as one of the main causes of death in the developed countries. Although significant progress has been made in the diagnosis and treatment of CHD, further investigation is still needed. The objective of this study was to develop a data-mining system for the assessment of heart event-related risk factors targeting in the reduction of CHD events. The risk factors investigated were: 1) before the event: a) nonmodifiable-age, sex, and family history for premature CHD, b) modifiable-smoking before the event, history of hypertension, and history of diabetes; and 2) after the event: modifiable-smoking after the event, systolic blood pressure, diastolic blood pressure, total cholesterol, high-density lipoprotein, low-density lipoprotein, triglycerides, and glucose. The events investigated were: myocardial infarction (MI), percutaneous coronary intervention (PCI), and coronary artery bypass graft surgery (CABG). A total of 528 cases were collected from the Paphos district in Cyprus, most of them with more than one event. Data-mining analysis was carried out using the C4.5 decision tree algorithm for the aforementioned three events using five different splitting criteria. The most important risk factors, as extracted from the classification rules analysis were: 1) for MI, age, smoking, and history of hypertension; 2) for PCI, family history, history of hypertension, and history of diabetes; and 3) for CABG, age, history of hypertension, and smoking. Most of these risk factors were also extracted by other investigators. The highest percentages of correct classifications achieved were 66%, 75%, and 75% for the MI, PCI, and CABG models, respectively. It is anticipated that data mining could help in the identification of high and low risk subgroups of subjects, a decisive factor for the selection of therapy, i.e., medical or surgical. However, further investigation with larger datasets is still needed.

Noninvasive ultrasound imaging of carotid plaques allows for the development of plaque-image analysis methods associated with the risk of stroke. This paper presents several plaque-image analysis methods that have been developed over the past years. The paper begins with a review of clinical methods for visual classification that have led to standardized methods for image acquisition, describes methods for image segmentation and denoising, and provides an overview of the several texture-feature extraction and classification methods that have been applied. We provide a summary of emerging trends in 3-D imaging methods and plaque-motion analysis. Finally, we provide a discussion of the emerging trends and future directions in our concluding remarks.

For the first time, a complete wireless and mobile emergency TeleOrthoPaedics system with field trials and expert opinion is presented. The system enables doctors in a remote area to obtain a second opinion from doctors in the hospital using secured wireless telecommunication networks. Doctors can exchange securely medical images and video as well as other important data, and thus perform remote consultations, fast and accurately using a user friendly interface, via a reliable and secure telemedicine system of low cost. The quality of the transmitted compressed (JPEG2000) images was measured using different metrics and doctors opinions. The results have shown that all metrics were within acceptable limits. The performance of the system was evaluated successfully under different wireless communication links based on real data.

The five papers in this special section provide a snapshot of emerging technologies in biomedical informatics. The selected papers were originally presented at the International Special Topic Conference on Information Technology in Biomedicine, held in October 2006, in Ioannina, Epirus, Greece.

The six papers in this special section focus on the most recent applications of computational intelligent systems in medicine. Some papers accepted for this issue (10 in total) were published earlier by mistake.

Recent advances in genotyping technology have enabled large studies with data from thousands of subjects to contain half a million or more of single nucleotide polymorphisms (SNPs) marker per subject. This rapid increase in the size of data has generated the need to compress the data in order to reduce the storage capacity requirements and the memory required at run time to perform analysis on the data. The availability of so many markers across the whole genome has created opportunities for new methodologies to be implemented that take advantage of the relatively high density of the markers to perform analyses that take into account the Linkage Disequilibrium (LD), an effect where some combinations of genetic markers are non-randomly associated. Classical techniques for transforming genotypic data into a binary format are already in use by several applications however we demonstrate in this paper that the traditional transformations are not adequate for certain types of analyses as some information key to new methodologies of analyses is lost. We propose a new protocol for formatting binary genotypic data that can be used in all types of analyses while still offering a very high compression rate.

Coronary heart disease (CHD) is a major cause of morbidity and mortality in the western world. Although significant progress has been made in the diagnosis and treatment of CHD, further investigation is still needed. The objective of this study was to develop a data mining system for the assessment of heart event related risk factors. The risk factors investigated were: i. clinical: sex, age, smoking, systolic blood pressure, family history for premature CHD, history of hypertension, and diabetes; and ii. biochemical: cholesterol, triglycerides, and glucose. The events investigated were: myocardial infarction (MI), percutaneous coronary intervention (PCI), and coronary artery bypass graft surgery (CABG). A total of 620 cases were collected from the Paphos district in Cyprus, most of them with more than one event. Data mining analysis was carried out using the C4.5 decision trees algorithms. The most important risk factors, as extracted from the classification rules analysis were: sex, age, smoking, blood pressure, and cholesterol. Most of these risk factors were also extracted by other investigators. It is anticipated that data mining could help in the identification of high and low risk subgroups of patients, a decisive factor for the selection of therapy, i.e. medical or surgical. However, further investigation with larger data sets is still needed.

The intima-media thickness (IMT) of the common carotid artery (CCA) is widely used as an early indicator of cardiovascular disease (CVD). It was proposed but not thoroughly investigated that the media layer (ML), its composition and texture, may be indicative for identifying the risk of stroke and differentiating between patients of high and low risk. In this study we investigate the usefulness of texture analysis of the ML of the CCA. The study was performed on 100 longitudinal ultrasound images acquired from asymptomatic subjects at risk of atherosclerosis. The images were separated into three different age groups, namely below 50, 50 to 60, and above 60 years old. A total of 61 different texture features were extracted from the intima-media complex (IMC), ML and the intima layer (IL). The IMC and ML were segmented manually by a neurovascular expert and automatically by a snakes segmentation system. It was shown that texture features extracted from the IL, ML and IMC are significantly different (mean, gray scale median (GSM), standard deviation, contrast, difference variance, periodicity) and that some of them can be associated with the increase (difference variance, entropy) or decrease (GSM) of patientpsilas age. It was also shown that the GSM of the ML falls linearly with increasing ML thickness (MLT) and with increasing age. Further research on more subjects is required for estimating other features that may provide information for patients at risk of stroke.

A variety of approaches have been proposed in the literature for region-of-interest (ROI) estimation. In most of them the ROI definition is highly subjective, i.e., lacks scientific evidence in supporting the claim that the areas defined as ROIs are indeed perceptually interesting. In this paper we attempt to model ROIs as the visually attended areas indicated by a saliency map in order to lower as much as possible the subjectivity of selection. For evaluation purposes we follow a ROI-based video compression setup and present comparisons with state-of-the-art algorithms in terms of perceived visual quality and video compression improvement. Extended experiments concerning both MPEG-1 as well as low bit-rate MPEG-4 video encoding were conducted showing significant improvement in video compression efficiency without perceived deterioration in visual quality.

In this paper we propose a novel saliency-based computational model for visual attention. This model processes both top-down (goal directed) and bottom-up information. Processing in the top-down channel creates the so called skin conspicuity map and emulates the visual search for human faces performed by humans. This is clearly a goal directed task but is generic enough to be context independent. Processing in the bottom-up information channel follows the principles set by Itti et al. but it deviates from them by computing the orientation, intensity and color conspicuity maps within a unified multi-resolution framework based on wavelet subband analysis. In particular, we apply a wavelet based approach for efficient computation of the topographic feature maps. Given that wavelets and multiresolution theory are naturally connected the usage of wavelet decomposition for mimicking the center surround process in humans is an obvious choice. However, our implementation goes further. We utilize the wavelet decomposition for inline computation of the features (such as orientation angles) that are used to create the topographic feature maps. The bottom-up topographic feature maps and the top-down skin conspicuity map are then combined through a sigmoid function to produce the final saliency map. A prototype of the proposed model was realized through the TMDSDMK642-0E DSP platform as an embedded system allowing real-time operation. For evaluation purposes, in terms of perceived visual quality and video compression improvement, a ROI-based video compression setup was followed. Extended experiments concerning both MPEG-1 as well as low bit-rate MPEG-4 video encoding were conducted showing significant improvement in video compression efficiency without perceived deterioration in visual quality.

In this paper, we propose and evaluate an integrated system for the segmentation of atherosclerotic plaque in ultrasound imaging of the carotid artery based on normalization, speckle reduction filtering, and four different snakes segmentation methods. These methods are the Williams and Shah, Balloon, Lai and Chin, and the gradient vector flow (GVF) snake. The performance of the four different plaque snakes segmentation methods was tested on 80 longitudinal ultrasound images of the carotid artery using receiver operating characteristic (ROC) analysis and the manual delineations of an expert. All four methods were very satisfactory and similar in all measures evaluated, with no significant differences between them; however, the Lai and Chin snakes segmentation method gave slightly better results. Concluding, it is proposed that the integrated system investigated in this study could be used successfully for the automated segmentation of the carotid plaque.

The present paper proposes a methodology for engineering requirements in a precise, comprehensive, understandable and time-saving way based on concepts and principles of natural language syntax and semantics (NLSS). The NLSS methodology copes with the following phases: (i) requirements discovery, by emphasizing on the creation and application of specific questions sets; (ii) requirements analysis, by stressing issues such as classification, decomposition and terminology of requirements; and (iii) requirements specification, by focusing on the writing formalization of the requirements.

Weather radars are used to measure the electromagnetic radiation backscattered by cloud raindrops. Clouds that backscatter more electromagnetic radiation consist of larger droplets of rain and therefore they produce more rain. The idea is to predict rainfall rate by using weather radar instead of rain-gauges measuring rainfall on the ground. In an experiment during two days in June and August 1997 over the Italian-Swiss Alps, data from a weather radar and surrounding rain-gauges were collected at the same time. The neural SOM and the statistical KNN classifier were implemented for the classification task using the radar data as input and the rain-gauge measurements as output. The rainfall rate on the ground was predicted based on the radar reflections with an average error rate of 23%. The results in this work show that the prediction of rainfall rate based on weather radar measurements is possible.

A three-layer neural network (NN) with novel adaptive architecture has been developed. The hidden layer of the network consists of slabs of single neuron models, where neurons within a slab--but not between slabs--have the same type of activation function. The network activation functions in all three layers have adaptable parameters. The network was trained using a biologically inspired, guided-annealing learning rule on a variety of medical data. Good training/testing classification performance was obtained on all data sets tested. The performance achieved was comparable to that of SVM classifiers. It was shown that the adaptive network architecture, inspired from the modular organization often encountered in the mammalian cerebral cortex, can benefit classification performance.

The aim of this work was to develop a system based on multifeature texture analysis and modular neural networks that will facilitate the automated interpretation of satellite cloud images. Such a system will provide a standardized and efficient way for classifying cloud types that can be used as an operational tool in weather analysis. A series of 98 infrared satellite images from the geostationary satellite METEOSAT7 were employed, and 366 cloud segments were labeled into six cloud types after combined agreed observations from ground and satellite. From the segmented cloud images, nine different texture feature sets (a total of 55 features) were extracted, using the following algorithms: statistical features, spatial gray-level dependence matrices, gray-level difference statistics, neighborhood gray tone difference matrix, statistical feature matrix, Laws' texture energy measures, fractals, and Fourier power spectrum. The neural network self-organizing feature map (SOFM) classifier and the statistical K-nearest neighbor (KNN) classifier were used for the classification of the cloud images. Furthermore, the classification results of the nine different feature sets were combined, improving the classification yield for the six classes, for the SOFM classifier to 61% and for the KNN classifier to 64%.

There are indications that the morphology of atherosclerotic carotid plaques, obtained by high-resolution ultrasound imaging, has prognostic implications. The objective of this study was to develop a computer-aided system that will facilitate the characterization of carotid plaques for the identification of individuals with asymptomatic carotid stenosis at risk of stroke. A total of 230 plaque images were collected which were classified into two types: symptomatic because of ipsilateral hemispheric symptoms, or asymptomatic because they were not connected with ipsilateral hemispheric events. Ten different texture feature sets were extracted from the manually segmented plaque images using the following algorithms: first-order statistics, spatial gray level dependence matrices, gray level difference statistics, neighborhood gray tone difference matrix, statistical feature matrix, Laws texture energy measures, fractal dimension texture analysis, Fourier power spectrum and shape parameters. For the classification task a modular neural network composed of self-organizing map (SOM) classifiers, and combining techniques based on a confidence measure were used. Combining the classification results of the ten SOM classifiers inputted with the ten feature sets improved the classification rate of the individual classifiers, reaching an average diagnostic yield (DY) of 73.1%. The same modular system was implemented using the statistical k-nearest neighbor (KNN) classifier. The combined DY for the KNN system was 68.8%. The results of this paper show that it is possible to identify a group of patients at risk of stroke based on texture features extracted from ultrasound images of carotid plaques. This group of patients may benefit from a carotid endarterectomy whereas other patients may be spared from an unnecessary operation.

A method for scanning and displaying chest radiographs (X-rays) is presented. The new method treats the scanning as independent of the image display, allowing for maximum information content to be captured during the scanning process, and then for this information to be optimally displayed using a new function for maximizing the contrast variation throughout the image. The quality of the digitized X-ray images was compared against the original X-ray films and was found to be of comparable visualization quality. The rib parenchyma is then segmented by an active shape model.

The aim of this work was to develop a system based on modular neural networks and multi-feature texture analysis that facilitates the automated interpretation of cloud images. This speeds up the interpretation process and provides continuity in the application of satellite imagery for weather forecasting. A series of infrared satellite images from the geostationary satellite METEOSAT7 were employed. Nine different texture feature sets (a total of 55 features) were extracted from the segmented cloud images using the following algorithms: first order statistics, spatial gray level dependence matrices, gray level difference statistics, neighborhood gray tone difference matrix, statistical feature matrix, Laws texture energy measures, fractals and Fourier power spectrum. The neural network SOFM (self organising feature map) classifier and the statistical KNN (Kohonen neural network) classifier were used for the classification of the cloud images. Furthermore, the classification results of the different feature sets were combined improving the classification yield to 91%.

The objective of this work is to develop a system that will facilitate the automated characterization of ultrasonic imaging carotid plaques for the identification of individuals with asymptomatic carotid stenosis at risk of stroke. A total of 166 images were collected which were classified into: symptomatic because of ipsilateral hemispheric symptoms, or asymptomatic because they were not connected with ipsilateral hemispheric events. Ten different texture feature sets were extracted: first order statistics, spatial gray level dependence matrices, gray level difference statistics, neighbourhood gray tone difference matrix, statistical feature matrix, Laws texture energy measures, fractal dimension texture analysis, Fourier power spectrum and shape parameters. A modular neural network classifier was developed composed of self-organizing map (SOM) classifiers, achieving an overall diagnostic yield of 76.4%. The results of this work show that it is possible to identify a group of patients at risk of stroke based on texture features.

We investigate the performance of a classical constrained optimisation (CCO) algorithm and a constrained optimisation genetic algorithm (GA) for solving the bandwidth allocation for virtual paths (BAVP) problem. We compare throughput, fairness and time complexity of GA-BAVP and CCO-BAVP for several node topologies. The results on maximising the throughput obtained with GA-BAVP and CCO-BAVP are in close agreement, however when considering fairness GA-BAVP outperforms CCO-BAVP, especially for more complex topologies, like the 7-node network, without abundant link capacity. Convergence of the two algorithms appears similar, with GA-BAVP outperforming CCO-BAVP in initial stages, and vice-versa for longer time scales. However as the problem complexity increases the solution time for the genetic algorithm does not increase as fast as the classical constrained optimisation algorithm. A hybrid scheme is also introduced, combining the benefits of both algorithms. It exhibited better overall convergence rate but the same solution as CCO-BAVP.

This paper describes the application of an amplitude modulation-frequency modulation (AM-FM) image representation in segmenting electron micrographs of skeletal muscle for the recognition of: 1) normal sarcomere ultrastructural pattern and 2) abnormal regions that occur in sarcomeres in various myopathies. A total of 26 electron micrographs from different myopathies were used for this study. It is shown that the AM-FM image representation can identify normal repetitive structures and sarcomeres, with a good degree of accuracy. This system can also detect abnormalities in sarcomeres which alter the normal regular pattern, as seen in muscle pathology, with a recognition accuracy of 75%-84% as compared to a human expert.

We segment the structural units of electron microscope muscle images using a novel AM-FM image representation. This novel AM-FM approach is shown to be effective in describing sarcomeres and mitochondrial regions of the electron microscope muscle images.

We develop a computer aided system which will facilitate the automated characterisation of carotid plaques recorded from high resolution ultrasound images for the identification of individuals with asymptomatic carotid stenosis at risk of stroke. The plaques were classified into: symptomatic or asymptomatic. Ten different texture feature sets were extracted from the segmented plaque image. Although the statistics of all features extracted for the two classes indicated a high degree of overlap, a classification of the plaques was possible using the unsupervised self-organizing feature map (SOFM) classifier and combining techniques. The classification results of the different feature sets were combined using the majority voting and weighted averaging based on a confidence measure derived from the SOFM. Combining the classification results of the ten different feature sets improved significantly the classification results obtained by the individual feature sets, reaching an average diagnostic yield of 75%.

A study for breast cancer nuclei detection is presented. The proposed algorithm determines the centers of nuclei in biopsy images using block-based processing of the images followed by singular value decomposition of each block. The normalized singular value vector, which consists of the normalized singular values of the block in decreasing order, is fed as the input to an appropriate neural network which classifies the block into nuclei and background ones. Examples are presented which illustrate the ability of the proposed technique to model the knowledge provided by experts in the nuclei detection task.

A computer-aided detection system for tissue cell nuclei in histological sections is introduced and validated as part of the Biopsy Analysis Support System (BASS). Cell nuclei are selectively stained with monoclonal antibodies, such as the anti-estrogen receptor antibodies, which are widely applied as part of assessing patient prognosis in breast cancer. The detection system uses a receptive field filter to enhance negatively and positively stained cell nuclei and a squashing function to label each pixel value as belonging to the background or a nucleus. In this study, the detection system assessed all biopsies in an automated fashion. Detection and classification of individual nuclei as well as biopsy grading performance was shown to be promising as compared to that of two experts. Sensitivity and positive predictive value were measured to be 83% and 67.4%, respectively. One major advantage of BASS stems from the fact that the system simulates the assessment procedures routinely employed by human experts; thus it can be used as an additional independent expert. Moreover, the system allows the efficient accumulation of data from large numbers of nuclei in a short time span. Therefore, the potential for accurate quantitative assessments is increased and a platform for more standardized evaluations is provided.

Clinical electromyography (EMG) provides useful information for the diagnosis of neuromuscular disorders. The utility of artificial neural networks (ANN's) in classifying EMG data trained with backpropagation or Rohonen's self-organizing feature maps algorithm has recently been demonstrated. The objective of this study is to investigate how genetics-based machine learning (GBML) can be applied for diagnosing certain neuromuscular disorders based on EMG data. The effect of GBML control parameters on diagnostic performance is also examined. A hybrid diagnostic system is introduced that combines both neural network and GBML models. Such a hybrid system provides the end-user with a robust and reliable system, as its diagnostic performance relies on more than one learning principle. GBML models demonstrated similar performance to neural-network models, but with less computation. The diagnostic performance of neural network and GBML models is enhanced by the hybrid system.

Normals and patients from three disorders have been selected for investigation: motor neurone disease (MND); Becker muscular dystrophy (BMD); and spinal muscular atrophy. The data from 36 macroelectromyograms were used for analysis. The results suggest that unsupervised learning neural networks generally produce better results than those produced by the supervised learning neural networks. No conclusion about the optimum size of the output grid can be reached from the results since the examined models for the 10*10 and 8*8 cases produced similar results. It is expected, however, that an optimum grid size should exist. This size will depend on the size and the variability of the training set. More epochs can improve the performance of a model up to a certain level, beyond which the number of epochs will have no positive effect.>