Depression, a prevalent mental disorder, can have severe consequences if left untreated, including self-harm and suicide. Selective Serotonin Reuptake Inhibitors (SSRI) therapy is the first course of treatment for depression disorder. Accurate prediction of SSRI therapy outcomes could significantly assist medical professionals in tailoring treatment plans to individual subjects. Electroencephalography (EEG) signals, which reflect the brain's neural activity, offer a non-invasive avenue for such predictions. However, visual analysis of EEG signals is laborious and time-consuming, given their complex, nonlinear, and nonstationary nature. EEG signals are complex, nonlinear, and nonstationary. Consequently, EEG signals need to be decomposed into several sub-bands to extract detailed and representative information. Traditional manual filter bank design for decomposition risks information loss. To address this challenge, this study proposes an automated tunable-Q wavelet transform (ATQWT) framework for automatic signal decomposition, which aims to preserve critical information during analysis. The Starfish optimization algorithm (SFOA), a bio-inspired metaheuristic approach, is employed to optimize the parameters of ATQWT, facilitating the automatic selection of optimal tuning parameters to extract meaningful sub-bands and enhance signal reconstruction during synthesis. Centered correntropy is utilized to compute features from the sub-bands, and the most discriminative features are identified using a nearest neighbor algorithm. These features are then classified using a feedforward neural network, and a 10-fold cross-validation strategy is implemented to mitigate potential bias in the results. The proposed method achieves an outstanding classification accuracy of 99.36% in predicting SSRI therapy outcomes. Results show F4, P4, C4, Fp2, F8 and Fz are the most informative channels for predicting SSRI therapy outcomes. So, the right-lateralized prefrontal and parietal lobes are more involved in depression therapy. This approach holds significant potential for assisting medical teams in clinical settings to develop more personalized and effective therapy plans for subjects with depression.

Depression is a mental disorder that can lead to self-harm or suicidal thoughts if left untreated. Clinicians face the challenge of determining the most effective therapeutic approach to depression. Selective Serotonin Reuptake Inhibitors are widely prescribed depression therapies, but their response rate is only around 50 %, which is relatively low compared to the treatment success of other mental diseases. To address this issue, a novel classification framework is introduced to build a computer-aided decision system that predicts the outcome of depression therapies. This proposed system utilizes novel systematic extraction and selection of time-domain features. Our methodology is not only effective for EEG subject classification but also widely applicable to similar EEG studies benefitting researchers working in cognitive, affective, and clinical neuroscience. In this 30 -subject pilot study, the multi-channel EEG signals are denoised using low and high-pass filters. Subsequently, feature extraction models are applied to the channels to extract the generalized pattern of the EEG data. Overlap coverage for moving segments was varied from$\mathbf{0} \boldsymbol{\%}$to$\mathbf{7 5 \%}$, and four feature selection algorithms were evaluated. To avoid bias in the results, the classification models are trained and validated using a leave-one-subject-out (LOSO) crossvalidation strategy. To prevent data leakage during feature selection, the test subject in each fold was excluded from the dataset prior to feature ranking. Randomized experiments repeated 30 times indicate that models utilizing under 50 features consistently reach an average balanced accuracy of 95%. In conclusion, this novel approach demonstrates its effectiveness through highly accurate and nonbiased classification results. The top-ranking features, dominated by energy and variability metrics from right frontal (F8), central (Cz), and occipital (O2) channels, highlight the critical role of fronto-central and posterior cortical dynamics in characterizing depression-related EEG biomarkers.

Accurate detection of muscle-invasive bladder cancer is critical for effective management and improved outcomes. This study aimed to characterize radiomic features from magnetic resonance images (MRI) to identify muscle invasive bladder cancer and to compare the predictive accuracy of various classification models trained on these features. We included a cohort of fifty-two bladder cancer subjects who underwent multiparametric magnetic resonance imaging. We manually segmented three-dimensional lesions on the T2-weighted images, Apparent Diffusion Coefficient images, and post-contrast T1-weighted images. From each segmented three-dimensional lesion for each image type, 105 radiomic features were extracted. Using selected radiomic features, we trained and tested five machine learning models: Decision Tree, Random Forest, Logistic Regression, Support Vector Machine, and Extreme Gradient Boosting. We selected the key radiomic features from each type of image, using Leave-One-Out cross-validation. Additionally, we trained the classifiers using the features selected from the combined images, including T2-weighted, Apparent Diffusion Coefficient, and postcontrast T1-weighted images. Pathology results served as the gold standard for the classification of muscle invasive bladder cancer. We measured the performance of the classifiers, calculating the accuracy, area under the ROC curve, sensitivity, and specificity. The radiomic features of the T2-weighted images yielded the highest average accuracy among individual image types. The features extracted from the post-contrast T1-weighted images alone yielded the lowest average accuracy. However, when combining all three image types, the combination produced the highest area under the curve, reaching 0.77, and an accuracy of 0.77. The Decision Tree model trained with the radiomic features from a combination of T2-weighted, Apparent Diffusion Coefficient, and post-contrast T1-weighted images provides improved accuracy in predicting muscle invasive bladder cancer.

COVID-19 pandemic has brought immense attention to SARS-CoV-2 and related microbiology studies. To defeat this deadly virus, its RNA is being studied by many researchers around the globe. This study primarily aims to compile a large RNA dataset to analyze RNA secondary structure of SARS-CoV-2 efficiently. We propose improvements on database creation and maintenance, and structure analysis tools. As a continuation of our previous works, we automate the creation of RNA secondary structures database in a new format by converting data collected from publicly available online resources. We present new secondary structure analysis algorithms that improve performance of existing tools. Results of GPU-based implementation are also presented for RNA search operations. We also introduce tools with new objectives, which answer fundamental RNA secondary structure queries. Our tools on the current database have been tested with SARS-CoV-2 related RNA secondary structures. A novel RNA secondary structure search-based multiple RNA comparison is introduced and tested too. Structural-only and structure-with-nucleotide search results particularly related to SARS-CoV-2 are presented in details. As a successful case study, the framework presented here offers some unique capabilities and is shown as a useful exploratory tool for future RNA analysis studies.

This study reports results of a pilot study, in which pigmented skin lesions are automatically classified into four classes: benign, dysplastic nevus with mild atypia, dysplastic nevus with severe atypia, and melanoma. The pilot study enrolled subjects from dermatology clinic at Baylor University Medical Center at Dallas from June 2016 to August 2017. 30 high-quality dermoscopic images were randomly selected from an image bank of 96 to obtain a statistically balanced dataset. Melanoma samples were histologically verified. A dermoscopy-based automated image analyzer with quaternary classification of pigmented skin lesions was proposed. The image analyzer automatically extracts five lesion features, most used in clinical practise, applying an active contour, and pairwise classification employing six Support Vector Machines. The pairwise accuracy of classifications are reported between 92% and 94% and used to determine the corresponding confidence intervals. Through the pairwise classification results maximum hits and acyclic tree decisions were utilized to reach the final classification of a lesion. Using leave-one-out validation, accuracy of the quaternary dermoscopy-based image analyzer were determined as 90% using the histopathologic diagnoses as the ground truth. This novel, dermoscopy-based image classifier accurately classifies pigmented skin lesions small data-sets into benign, two types of dysplastic nevi, and malignant lesions. To the best of our knowledge, there is no other automated skin lesion classification framework, in the literature, which distinguishes between different nevus lesion.

BACKGROUND: Dermoscopy is one of the common and effective imaging techniques in diagnosis of skin cancer, especially for pigmented lesions. Accurate skin lesion border detection is the key to extract important dermoscopic features of the skin lesion. In current clinical settings, border delineation is performed manually by dermatologists. Operator based assessments lead to intra- and inter-observer variations due to its subjective nature. Moreover it is a tedious process. Because of aforementioned hurdles, the automation of lesion boundary detection in dermoscopic images is necessary. In this study, we address this problem by developing a novel skin lesion border detection method with a robust edge indicator function, which is based on a meshless method. RESULT: Our results are compared with the other image segmentation methods. Our skin lesion border detection algorithm outperforms other state-of-the-art methods. Based on dermatologist drawn ground truth skin lesion borders, the results indicate that our method generates reasonable boundaries than other prominent methods having Dice score of 0.886 ±0.094 and Jaccard score of 0.807 ±0.133. CONCLUSION: We prove that smoothed particle hydrodynamic (SPH) kernels can be used as edge features in active contours segmentation and probability map can be employed to avoid the evolving contour from leaking into the object of interest.

Melanoma is a potentially deadly form of skin cancer, however, if detected early, it is curable. A dysplastic nevus (atypical mole) is not cancerous but may represent a precursor to malignancy as nearly 40% of melanomas arise from a preexisting mole. In this study, we propose a system to classify a skin lesion image as melanoma (M), dysplastic nevus (D), and benign (B). For this purpose we develop a new two layered-system. The first layer consists of three binary Support Vector Machine (SVM) classifiers, one for each pair of classes, M vs B, M vs D, and B vs D. The second layer is a novel decision maker function, which uses probability memberships derived from the first layer. Each lesion is characterized with five features, which mostly overlaps with the ABCD rule of dermatology. The dataset we used have 112 lesions with 54 M, 38 D, and 20 B cases. In the experiments of melanoma detection, we obtained 98% specificity, 76% sensitivity, and 85% F-measure accuracy.

BACKGROUND: Automated skin lesion border examination and analysis techniques have become an important field of research for distinguishing malignant pigmented lesions from benign lesions. An abrupt pigment pattern cutoff at the periphery of a skin lesion is one of the most important dermoscopic features for detection of neoplastic behavior. In current clinical setting, the lesion is divided into a virtual pie with eight sections. Each section is examined by a dermatologist for abrupt cutoff and scored accordingly, which can be tedious and subjective. METHODS: This study introduces a novel approach to objectively quantify abruptness of pigment patterns along the lesion periphery. In the proposed approach, first, the skin lesion border is detected by the density based lesion border detection method. Second, the detected border is gradually scaled through vector operations. Then, along gradually scaled borders, pigment pattern homogeneities are calculated at different scales. Through this process, statistical texture features are extracted. Moreover, different color spaces are examined for the efficacy of texture analysis. RESULTS: The proposed method has been tested and validated on 100 (31 melanoma, 69 benign) dermoscopy images. Analyzed results indicate that proposed method is efficient on malignancy detection. More specifically, we obtained specificity of 0.96 and sensitivity of 0.86 for malignancy detection in a certain color space. The F-measure, harmonic mean of recall and precision, of the framework is reported as 0.87. CONCLUSIONS: The use of texture homogeneity along the periphery of the lesion border is an effective method to detect malignancy of the skin lesion in dermoscopy images. Among different color spaces tested, RGB color space's blue color channel is the most informative color channel to detect malignancy for skin lesions. That is followed by YCbCr color spaces Cr channel, and Cr is closely followed by the green color channel of RGB color space.

BACKGROUND: Neuroimaging studies have yielded significant advances in the understanding of neural processes relevant to the development and persistence of addiction. However, these advances have not explored extensively for diagnostic accuracy in human subjects. The aim of this study was to develop a statistical approach, using a machine learning framework, to correctly classify brain images of cocaine-dependent participants and healthy controls. In this study, a framework suitable for educing potential brain regions that differed between the two groups was developed and implemented. Single Photon Emission Computerized Tomography (SPECT) images obtained during rest or a saline infusion in three cohorts of 2-4 week abstinent cocaine-dependent participants (n = 93) and healthy controls (n = 69) were used to develop a classification model. An information theoretic-based feature selection algorithm was first conducted to reduce the number of voxels. A density-based clustering algorithm was then used to form spatially connected voxel clouds in three-dimensional space. A statistical classifier, Support Vectors Machine (SVM), was then used for participant classification. Statistically insignificant voxels of spatially connected brain regions were removed iteratively and classification accuracy was reported through the iterations. RESULTS: The voxel-based analysis identified 1,500 spatially connected voxels in 30 distinct clusters after a grid search in SVM parameters. Participants were successfully classified with 0.88 and 0.89 F-measure accuracies in 10-fold cross validation (10xCV) and leave-one-out (LOO) approaches, respectively. Sensitivity and specificity were 0.90 and 0.89 for LOO; 0.83 and 0.83 for 10xCV. Many of the 30 selected clusters are highly relevant to the addictive process, including regions relevant to cognitive control, default mode network related self-referential thought, behavioral inhibition, and contextual memories. Relative hyperactivity and hypoactivity of regional cerebral blood flow in brain regions in cocaine-dependent participants are presented with corresponding level of significance. CONCLUSIONS: The SVM-based approach successfully classified cocaine-dependent and healthy control participants using voxels selected with information theoretic-based and statistical methods from participants' SPECT data. The regions found in this study align with brain regions reported in the literature. These findings support the future use of brain imaging and SVM-based classifier in the diagnosis of substance use disorders and furthering an understanding of their underlying pathology.

BACKGROUND: Dermoscopy is a highly effective and noninvasive imaging technique used in diagnosis of melanoma and other pigmented skin lesions. Many aspects of the lesion under consideration are defined in relation to the lesion border. This makes border detection one of the most important steps in dermoscopic image analysis. In current practice, dermatologists often delineate borders through a hand drawn representation based upon visual inspection. Due to the subjective nature of this technique, intra- and inter-observer variations are common. Because of this, the automated assessment of lesion borders in dermoscopic images has become an important area of study. METHODS: Fast density based skin lesion border detection method has been implemented in parallel with a new parallel technology called WebCL. WebCL utilizes client side computing capabilities to use available hardware resources such as multi cores and GPUs. Developed WebCL-parallel density based skin lesion border detection method runs efficiently from internet browsers. RESULTS: Previous research indicates that one of the highest accuracy rates can be achieved using density based clustering techniques for skin lesion border detection. While these algorithms do have unfavorable time complexities, this effect could be mitigated when implemented in parallel. In this study, density based clustering technique for skin lesion border detection is parallelized and redesigned to run very efficiently on the heterogeneous platforms (e.g. tablets, SmartPhones, multi-core CPUs, GPUs, and fully-integrated Accelerated Processing Units) by transforming the technique into a series of independent concurrent operations. Heterogeneous computing is adopted to support accessibility, portability and multi-device use in the clinical settings. For this, we used WebCL, an emerging technology that enables a HTML5 Web browser to execute code in parallel for heterogeneous platforms. We depicted WebCL and our parallel algorithm design. In addition, we tested parallel code on 100 dermoscopy images and showed the execution speedups with respect to the serial version. Results indicate that parallel (WebCL) version and serial version of density based lesion border detection methods generate the same accuracy rates for 100 dermoscopy images, in which mean of border error is 6.94%, mean of recall is 76.66%, and mean of precision is 99.29% respectively. Moreover, WebCL version's speedup factor for 100 dermoscopy images' lesion border detection averages around ~491.2. CONCLUSIONS: When large amount of high resolution dermoscopy images considered in a usual clinical setting along with the critical importance of early detection and diagnosis of melanoma before metastasis, the importance of fast processing dermoscopy images become obvious. In this paper, we introduce WebCL and the use of it for biomedical image processing applications. WebCL is a javascript binding of OpenCL, which takes advantage of GPU computing from a web browser. Therefore, WebCL parallel version of density based skin lesion border detection introduced in this study can supplement expert dermatologist, and aid them in early diagnosis of skin lesions. While WebCL is currently an emerging technology, a full adoption of WebCL into the HTML5 standard would allow for this implementation to run on a very large set of hardware and software systems. WebCL takes full advantage of parallel computational resources including multi-cores and GPUs on a local machine, and allows for compiled code to run directly from the Web Browser.

Skin cancer is on the rise. Hence the accurate detection of cancerous lesions is of paramount importance in the treatment of this health condition. In this study, we present a computer vision framework that studies 10 skin lesion features including newly introduced morphological and texture features along with established in the literature. All features are extracted automatically from 90 lesion images. A two-class classification problem was applied to determine the most significant features of disease using three features selection methods, Support Vector Machines Recursive Feature Elimination (SVMRFE), Information Gain, and Correlation-based Feature Subset Selection. We found that the five features selected by SVMRFE provides the highest accuracy readings of 100% model, 84% leave-one-out, and 89% 10-fold cross validation (10×CV) than the other two methods. Comparing the selected features with those used by the Total Dermoscopy Score, we report consistencies, disagreements, and the contributions of the present work.

This paper develops an approach and tool that automatically extracts skin lesion's boundary used for symmetry and area calculation. An image enhancement approach prepares every image for active contour (AC) evolution. Further, the AC automatically extracts the lesion's boundary used to measure symmetry applying minimal boundary box. Next, the lesion's area is calculated. Thus, the lesions are mapped as points onto area - symmetry 2D space to determine the distribution of the lesions with cancer. To validate the theoretical concepts experiments were performed with 51 skin lesion images. A statistics measures the accuracy of boundary extraction with respect to a ground truth. The advantages, disadvantages and the contribution of this study are reported at the end of the paper.

BACKGROUND: Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. In current practice, dermatologists determine lesion area by manually drawing lesion borders. Therefore, automated assessment tools for dermoscopy images have become an important research field mainly because of inter- and intra-observer variations in human interpretation. One of the most important steps in dermoscopy image analysis is automated detection of lesion borders. To our knowledge, in our 2010 study we achieved one of the highest accuracy rates in the automated lesion border detection field by using modified density based clustering algorithm. In the previous study, we proposed a novel method which removes redundant computations in well-known spatial density based clustering algorithm, DBSCAN; thus, in turn it speeds up clustering process considerably. FINDINGS: Our previous study was heavily dependent on the pre-processing step which creates a binary image from original image. In this study, we embed a new distance measure to the existing algorithm. This provides twofold benefits. First, since new approach removes pre-processing step, it directly works on color images instead of binary ones. Thus, very important color information is not lost. Second, accuracy of delineated lesion borders is improved on 75% of 100 dermoscopy image dataset. CONCLUSION: Previous and improved methods are tested within the same dermoscopy dataset along with the same set of dermatologist drawn ground truth images. Results revealed that the improved method directly works on color images without any pre-processing and generates more accurate results than existing method.

MOTIVATION: The medical imaging and image processing techniques, ranging from microscopic to macroscopic, has become one of the main components of diagnostic procedures to assist dermatologists in their medical decision-making processes. Computer-aided segmentation and border detection on dermoscopic images is one of the core components of diagnostic procedures and therapeutic interventions for skin cancer. Automated assessment tools for dermoscopic images have become an important research field mainly because of inter- and intra-observer variations in human interpretations. In this study, a novel approach-graph spanner-for automatic border detection in dermoscopic images is proposed. In this approach, a proximity graph representation of dermoscopic images in order to detect regions and borders in skin lesion is presented. RESULTS: Graph spanner approach is examined on a set of 100 dermoscopic images whose manually drawn borders by a dermatologist are used as the ground truth. Error rates, false positives and false negatives along with true positives and true negatives are quantified by digitally comparing results with manually determined borders from a dermatologist. The results show that the highest precision and recall rates obtained to determine lesion boundaries are 100%. However, accuracy of assessment averages out at 97.72% and borders errors' mean is 2.28% for whole dataset.

BACKGROUND: Computer-aided segmentation and border detection in dermoscopic images is one of the core components of diagnostic procedures and therapeutic interventions for skin cancer. Automated assessment tools for dermoscopy images have become an important research field mainly because of inter- and intra-observer variations in human interpretation. In this study, we compare two approaches for automatic border detection in dermoscopy images: density based clustering (DBSCAN) and Fuzzy C-Means (FCM) clustering algorithms. In the first approach, if there exists enough density--greater than certain number of points--around a point, then either a new cluster is formed around the point or an existing cluster grows by including the point and its neighbors. In the second approach FCM clustering is used. This approach has the ability to assign one data point into more than one cluster. RESULTS: Each approach is examined on a set of 100 dermoscopy images whose manually drawn borders by a dermatologist are used as the ground truth. Error rates; false positives and false negatives along with true positives and true negatives are quantified by comparing results with manually determined borders from a dermatologist. The assessments obtained from both methods are quantitatively analyzed over three accuracy measures: border error, precision, and recall. CONCLUSION: As well as low border error, high precision and recall, visual outcome showed that the DBSCAN effectively delineated targeted lesion, and has bright future; however, the FCM had poor performance especially in border error metric.

BACKGROUND: Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Automated assessment tools for dermoscopy images have become an important field of research mainly because of inter- and intra-observer variations in human interpretation. One of the most important steps in dermoscopy image analysis is the detection of lesion borders, since many other features, such as asymmetry, border irregularity, and abrupt border cutoff, rely on the boundary of the lesion. RESULTS: To automate the process of delineating the lesions, we employed Active Contour Model (ACM) and boundary-driven density-based clustering (BD-DBSCAN) algorithms on 50 dermoscopy images, which also have ground truths to be used for quantitative comparison. We have observed that ACM and BD-DBSCAN have the same border error of 6.6% on all images. To address noisy images, BD-DBSCAN can perform better delineation than ACM. However, when used with optimum parameters, ACM outperforms BD-DBSCAN, since ACM has a higher recall ratio. CONCLUSION: We successfully proposed two new frameworks to delineate suspicious lesions with i) an ACM integrated approach with sharpening and ii) a fast boundary-driven density-based clustering technique. ACM shrinks a curve toward the boundary of the lesion. To guide the evolution, the model employs the exact solution 27 of a specific form of the Geometric Heat Partial Differential Equation 28. To make ACM advance through noisy images, an improvement of the model's boundary condition is under consideration. BD-DBSCAN improves regular density-based algorithm to select query points intelligently.

Many systems in sciences, engineering and nature can be modeled as networks. Examples include the Internet, WWW and social networks. Finding hidden structures is important for making sense of complex networked data. In this paper we present a new network clustering method that can find clusters in an agglomerative fashion using structural similarity of vertices in the given network. Experiments conducted on real datasets demonstrate promising performance of the new method.

Color is the most critical information for assessing histological images. However, in literature, there is no standard color space in which a particular color points are represented for computer vision tasks. In this paper, we evaluated 11 color models with three different learning schemas for their performance in classifying tumor-related colors. The color models we studied are CIELAB, CIELUV, CIEXYZ, CMY, CMYK, HSL, HSV, Hunter-LAB, NRGB, RGB, and SCT. With 11 color models, prediction accuracies of three well-known classifiers, namely SVMs, C4.5, and Naive Bayes, are statistically compared on a large dataset of 3494 Hematoxylin and Eosin (HE) stained histopathologic images. Surprisingly, experiment results show that in contrast to general assumptions, there is no single model that is better than others in every case. However, C4.5 outperformed other two classifiers by obtaining average F-measure of 0.9989. Of 11 color models, we suggest the pair of C4.5-SCT as the most accurate classification framework for tumor identification in HE stained histological images.

BACKGROUND: To grow beyond certain size and reach oxygen and other essential nutrients, solid tumors trigger angiogenesis (neovascularization) by secreting various growth factors. Based on this fact, several researches proposed that density of newly formed vessels correlate with tumor malignancy. Vessel density is known as a true prognostic indicator for several types of cancer. However, automated quantification of angiogenesis is still in its primitive stage, and deserves more intelligent methods by taking advantages accruing from novel computer algorithms. RESULTS: The newly introduced characteristics of subimages performed well in identification of region-of-angiogenesis. The proposed technique was tested on 522 samples collected from two high-resolution tissues. Having 0.90 overall f-measure, the results obtained with Support Vector Machines show significant agreement between automated framework and manual assessment of microvessels. CONCLUSION: This study introduces a new framework to identify angiogenesis to measure microvessel density (MVD) in digitalized images of liver cancer tissues. The objective is to recognize all subimages having new vessel formations. In addition to region based characteristics, a set of morphological features are proposed to differentiate positive and negative incidences.

BACKGROUND: Biological systems can be modeled as complex network systems with many interactions between the components. These interactions give rise to the function and behavior of that system. For example, the protein-protein interaction network is the physical basis of multiple cellular functions. One goal of emerging systems biology is to analyze very large complex biological networks such as protein-protein interaction networks, metabolic networks, and regulatory networks to identify functional modules and assign functions to certain components of the system. Network modules do not occur by chance, so identification of modules is likely to capture the biologically meaningful interactions in large-scale PPI data. Unfortunately, existing computer-based clustering methods developed to find those modules are either not so accurate or too slow. RESULTS: We devised a new methodology called SCAN (Structural Clustering Algorithm for Networks) that can efficiently find clusters or functional modules in complex biological networks as well as hubs and outliers. More specifically, we demonstrated that we can find functional modules in complex networks and classify nodes into various roles based on their structures. In this study, we showed the effectiveness of our methodology using the budding yeast (Saccharomyces cerevisiae) protein-protein interaction network. To validate our clustering results, we compared our clusters with the known functions of each protein. Our predicted functional modules achieved very high purity comparing with state-of-the-art approaches. Additionally the theoretical and empirical analysis demonstrated a linear running-time of the algorithm, which is the fastest approach for networks. CONCLUSION: We compare our algorithm with well-known modularity based clustering algorithm CNM. We successfully detect functional groups that are annotated with putative GO terms. Top-10 clusters with minimum p-value theoretically prove that newly proposed algorithm partitions network more accurately then CNM. Furthermore, manual interpretations of functional groups found by SCAN show superior performance over CNM.

Squamous cell carcinoma is the most common type of head and neck cancer affecting about 30,000 Americans each year [1]. Diagnosis of tumor is backed by histopathologic examination of excised tissue in which lesion is speckled. Computer vision systems have yet to contribute significantly to the investigation of tumor areas in terms of histological slide analysis. Recently the improvements in imaging techniques led to the discovery of virtual histological slides. Virtual slides are of sufficiently high quality to generate immense interest within the research community. We describe a novel method to tackle automatic delineation of head and neck squamous cell carcinoma problem in virtual histological slides. A density-based clustering algorithm improved in this study plays a key role in the determination of the proliferative cell nuclei. The experimental results on high-resolution head and neck slides show that the proposed algorithm performed well, obtaining an average of 96% accuracy.

BACKGROUND: Histopathology, which is one of the most important routines of all laboratory procedures used in pathology, is decisive for the diagnosis of cancer. Experienced histopathologists review the histological slides acquired from biopsy specimen in order to outline malignant areas. Recently, improvements in imaging technologies in terms of histological image analysis led to the discovery of virtual histological slides. In this technique, a computerized microscope scans a glass slide and generates virtual slides at a resolution of 0.25 mum/pixel. As the recognition of intrinsic cancer areas is time consuming and error prone, in this study we develop a novel method to tackle automatic squamous cell carcinoma of the head and neck detection problem in high-resolution, wholly-scanned histopathological slides. RESULTS: A density-based clustering algorithm improved for this study plays a key role in the determination of the corrupted cell nuclei. Using the Support Vector Machines (SVMs) Classifier, experimental results on seven head and neck slides show that the proposed algorithm performs well, obtaining an average of 96% classification accuracy. CONCLUSION: Recent advances in imaging technology enable us to investigate cancer tissue at cellular level. In this study we focus on wholly-scanned histopathological slides of head and neck tissues. In the context of computer-aided diagnosis, delineation of malignant regions is achieved using a powerful classification algorithm, which heavily depends on the features extracted by aid of a newly proposed cell nuclei clustering technique. The preliminary experimental results demonstrate a high accuracy of the proposed method.