Special issue on signal processing and machine learning for biomedical data

This Special Issue is focused on advanced techniques in signal processing, analysis, modelling, and classification, applied to a variety of medical diagnostic problems. Biomedical data play a fundamental role in many fields of research and clinical practice. Very often the complexity of these data and their large volume makes it necessary to develop advanced analysis techniques and systems. Furthermore, the introduction of new techniques and methodologies for diagnostic purposes, especially in the field of medical imaging, requires new signal processing and machine learning methods. The recent progress in machine learning techniques, and in particular deep learning, revolutionized various fields of artificial vision, significantly pushing the state of the art of artificial vision systems into a wide range of high-level tasks. Such progress can help address problems in the analysis of biomedical data.This Special Issue placed particular emphasis on contributions dealing with practical, applications-led research, on the use of methods and devices in clinical diagnosis. The works that make up this special issue show a remarkable variety of applications for the detection and classification of medical imaging problems. In particular, the aforementioned works can be divided on the basis of types of techniques used, into three categories—signal processing (SP) methods, traditional machine learning (ML) methods, and deep learning (DL) methods

Comparative study on the detection of early dental caries using thermo-photonic lock-in imaging and optical coherence tomography

Early detection of dental caries is known to be the key to the effectiveness of therapeutic and preventive approaches in dentistry. However, existing clinical detection techniques, such as radiographs, are not sufficiently sensitive to detect and monitor the progression of caries at early stages. As such, in recent years, several optics-based imaging modalities have been proposed for the early detection of caries. The majority of these techniques rely on the enhancement of light scattering in early carious lesions, while a few of them are based on the enhancement of light absorption at early caries sites. In this paper, we report on a systemic comparative study on the detection performances of optical coherence tomography (OCT) and thermophotonic lock-in imaging (TPLI) as representative early caries detection modalities based on light scattering and absorption, respectively. Through controlled demineralization studies on extracted human teeth and µCT validation experiments, several detection performance parameters of the two modalities such as detection threshold, sensitivity and specificity have been qualitatively analyzed and discussed. Our experiment results suggests that both modalities have sufficient sensitivity for the detection of well-developed early caries on occlusal and smooth surfaces; however, TPLI provides better sensitivity and detection threshold for detecting very early stages of caries formation, which is deemed to be critical for the effectiveness of therapeutic and preventive approaches in dentistry. Moreover, due to the more specific nature of the light absorption contrast mechanism over light scattering, TPLI exhibits better detection specificity, which results in less false positive readings and thus allows for the proper differentiation of early caries regions from the surrounding intact areas. The major shortcoming of TPLI is its inherent depth-integrated nature, prohibiting the production of depth-resolved/B-mode like images. The outcomes of this research justify the need for a light-absorption based imaging modality with the ability to produce tomographic and depth-resolved images, combining the key advantages of OCT and TPLI.York University Librarie