Hyperspectral imaging (HSI) has gained prominence in medical diagnostics due to its ability to capture and analyse detailed spectral information beyond human visual capabilities. Processing of HSI data is essential to enhance subsequent analysis and ensure the accuracy of results by reducing noise and unwanted artifacts. This paper provides an overview of state-of-the-art processing methods for HSI data, focusing on smoothing, normalization, and spectral derivatives. The efficacy of these methods is evaluated using root mean square error (RMSE) to compare pre-processed data with wavelength reference standard, alongside execution time considerations. Results indicate that certain algorithms, such as smoothing based on moving average, standard normal variate, and first spectral derivatives, yield superior performance across different medical HSI systems. Additionally, combining these processing techniques further improves data fidelity to the wavelength reference standard. Overall, this study offers insights into optimal processing strategies for enhancing the accuracy and reliability of HSI data.

The purpose of this paper is to outline the advances in hyperspectral (HS) image fusion for intraoperative delineation of brain tumor tissue. HS images were acquired using an intraoperative acquisition system based on two push-broom HS cameras, one covering the visible and near-infrared (VNIR) [400–1000 nm] and the other the near-infrared (NIR) [900–1700 nm] spectral range. A dataset of a wide range of in-vivo brain cancer acquired during neurosurgical procedures with both HS cameras was used to compare the performance results of using the VNIR and NIR data, independently and combining the VNIR-NIR data. Classification maps obtained using the fused VNIR-NIR images provide more accurate classification, removing false positives that appear when the VNIR and NIR images are processed independently.

Chemotherapy-induced peripheral neuropathy (CIPN) is a common adverse reaction produced by chemotherapy drugs used to treat cancer. A few of the most common symptoms are pain, discomfort, tingling, numbness, and weakness in the hands, feet, and other parts of the body. The use of ozone therapy (O3T) is a novel therapy which aims to reduce these side effects. This study focuses on the visualization of oxygen saturation (StO2) in the extremities in a non-contact fashion employing hyperspectral (HS) imaging (HSI) with the goal of using HSI as a predictive value in the objective assessment of pain after ozone therapy application. A customized acquisition system composed of an HS camera (covering the 470–900 nm spectral range) and a halogen illumination system was developed to capture images of the extremities in a non-contact approach. An experimental clinical procedure was established to measure the evolution of StO2in hands and feet using a mathematical model based on information related to two wavelengths (660 and 880 nm). The preliminary results show that, in general, when analyzing the extremities of all unified patients, StO2slightly improves but significatively in the peripheral tissues after ozone therapy (O3T). HS technology allows the estimation of StO2values and allows the quantification of the effect of O3T.

The increasing prevalence of chronic non-communicable diseases makes it a priority to develop tools for enhancing their management. On this matter, Artificial Intelligence algorithms have proven to be successful in early diagnosis, prediction and analysis in the medical field. Nonetheless, two main issues arise when dealing with medical data: lack of high-fidelity datasets and maintenance of patient's privacy. To face these problems, different techniques of synthetic data generation have emerged as a possible solution. In this work, a framework based on synthetic data generation algorithms was developed. Eight medical datasets containing tabular data were used to test this framework. Three different statistical metrics were used to analyze the preservation of synthetic data integrity and six different synthetic data generation sizes were tested. Besides, the generated synthetic datasets were used to train four different supervised Machine Learning classifiers alone, and also combined with the real data. F1-score was used to evaluate classification performance. The main goal of this work is to assess the feasibility of the use of synthetic data generation in medical data in two ways: preservation of data integrity and maintenance of classification performance.

Skin cancer is one of the most frequent type of cancer, which is tipically divided in two types: melanoma and non-melanoma. Melanoma is the least common, but also the deadliest of them if left untreated in early stages. Thus, skin cancer monitoring is key for early detection, which could be done with the help of mobile devices and artificial intelligence solutions. In this sense, local deployment is suggested to embrace simplicity and avoid data privacy and security issues. However, current high-performance neural networks are extremely challenging to be deployed in mobile devices due to resource constraint, so lighter but effective models are required to make local deployment possible. In this work, simplifying an already light model, such as MobileNetV2, is pursued, combining it with an attention mechanism to enhance the network's capability to learn and compensate for the lack of information that simplifying the original architecture might cause. Fine-tuning was applied, using an autoencoder to pre-train the model on the CIFAR100 dataset. Experiments covering four scenarios were carried out using HAM10000 dataset. Promising results were obtained, reaching the best performance using a simplified MobileNetV2 combined with Coordinate Attention mechanism with less than a million parameters in total and up to a 83.93 % of accuracy.

Today, hyperspectral (HS) imaging has become a powerful tool to identify remotely the composition of an interest area through the joint acquisition of spatial and spectral information. However, like in most imaging techniques, unwanted effects may occur during data acquisition, such as noise, changes in light intensity, temperature differences, or optical variations. In HS imaging, these problems can be attenuated using a reflectance calibration stage and optical filtering. Nevertheless, optical filtering might induce some distortion that could complicate the posterior image processing stage. In this work, we present a new proposal for reflectance calibration that compensates for optical alterations during the acquisition of an HS image. The proposed methodology was evaluated on an HS image of synthetic squares of various materials with specific spectral responses. The results of our proposal show high performance in two classification tests using the K-means algorithm with 97% and 88% accuracy; in comparison with the standard reflectance calibration from the literature that obtained 77% and 64% accuracy. These results illustrate the performance gain of the proposed formulation, which besides maintaining the characteristic features of the compounds within the HS image, keeps the resulting reflectance into fixed lower and upper bounds, which avoids a post-calibration normalization step.

As program chairs of the 2022 edition of DSD, the EUROMICRO Conference on Digital System Design we would like to welcome you and to wish all of you a pleasant and fruitful participation in the conference and a wonderful stay in Gran Canaria.

Glioblastoma is considered an aggressive tumor due to its rapid growth rate and diffuse pattern in various parts of the brain. Current in-vivo classification procedures are executed under the supervision of an expert. However, this methodology could be subjective and time-consuming. In this work, we propose a classification method for in-vivo hyperspectral brain images to identify areas affected by glioblastomas based on nonlinear spectral unmixing. This methodology follows a semi-supervised approach for the estimation of the end-members in a multi-linear model. To improve the classification results, we vary the number of end-members per-class to address spectral variability of each studied type of tissue. Once the set of end-members is obtained, the classification map is generated according to the end-member with the highest abundance in each pixel, followed by morphological operations to smooth the resulting maps. The classification results demonstrate that the proposed methodology generates high performance in the regions of interest, with an accuracy above 0.75 and 0.96 in the inter and intra-patient strategies, respectively. These results indicate that the proposed methodology has the potential to be used as an assistant tool in the diagnosis of glioblastoma in hyperspectral imaging.

In recent years, hyperspectral imaging has been employed in several medical applications, targeting automatic diagnosis of different diseases. These images showed good performance in identifying different types of cancers. Among the methods used for classification, machine learning and deep learning techniques emerged as the most suitable algorithms to handle these data. In this paper, we propose a novel hyperspectral image classification architecture exploiting Vision Transformers. We validated the method on a real hyperspectral dataset containing 76 skin cancer images. Obtained results clearly highlight that the Vision Transforms are a suitable architecture for this task. Measured results outperform the state-of-the-art both in terms of false negative rates and of processing times. Finally, the attention mechanism is evaluated for the first time on medical hyperspectral images.

The early detection of precancerous cervical lesions is essential to improve patient treatment and prognosis. Current methods of screening and diagnosis have improved the detection of these lesions but still present some critical limitations. Hyperspectral (HS) imaging is emerging as a new non-invasive and label-free imaging technique in the medical field for performing quick diagnosis of different diseases. This work describes the first step in the research and development process to present to the gynaecologist a new non-invasive tool to detect cervical neoplasia during routine medical procedures. This tool is based on a HS camera coupled to a colposcope, a primary tool already used in cervical examinations. The developed HS colposcope was validated by comparing the HS images obtained against the captures obtained with conventional optics. Results show the feasibility of the developed system to start a data acquisition campaign of cervical lesions targeting future developments of algorithms based on artificial intelligence.

Oxygen saturation (StO2) measurement allows to detect different clinical conditions related with the low oxygenation of tissues or is used to monitor the quality and safety of organ transplantation. This study is focused on the visualization and measurement of StO2using hyperspectral imaging (HSI) through non-contact skin captures, targeting a potential real-time monitoring application. A customized acquisition system composed by a hyperspectral camera (covering the 470-900 nm spectral range) and a thermal camera was developed to capture images of hands in a non-contact fashion. An experimental procedure was established to measure the evolution of StO2in healthy hands where a compression of the index finger or brachial artery were performed. StO2measurements were performed in normal, compression, and reperfusion states. Two mathematical models with different sets of wavelengths were evaluated. The results show the proposed models, which employed two wavelengths (660 and 880 nm), obtain reliable StO2values, providing a potential non-contact imaging tool for StO2measurement.

This paper describes the challenge of real-time tumor tissue identification dealt with by the HypErspectraL Imaging Cancer Detection (HELICoiD) European project. This project was funded by the Research Executive Agency, through the Future and Emerging Technologies (FET-Open) programme, under the 7th Framework Programme of the European Union. It involved four universities, three industrial partners and two hospitals. In this paper, we focused on the activity performed by the University of Las Palmas de Gran Canaria, in collaboration with the University of Pavia, concerning the parallel implementation of Support Vector Machine (SVM) classification for tumor tissue identification during surgery. Obtained results show that this classification is real-time compliant when performed using Graphic Processing Units (GPUs).