Does a Previous Segmentation Improve the Automatic Detection of Basal Cell Carcinoma Using Deep Neural Networks?

This article belongs to the Special Issue "Image Processing and Analysis for Preclinical and Clinical Applications"Basal Cell Carcinoma (BCC) is the most frequent skin cancer and its increasing incidence is producing a high overload in dermatology services. In this sense, it is convenient to aid physicians in detecting it soon. Thus, in this paper, we propose a tool for the detection of BCC to provide a prioritization in the teledermatology consultation. Firstly, we analyze if a previous segmentation of the lesion improves the ulterior classification of the lesion. Secondly, we analyze three deep neural networks and ensemble architectures to distinguish between BCC and nevus, and BCC and other skin lesions. The best segmentation results are obtained with a SegNet deep neural network. A 98% accuracy for distinguishing BCC from nevus and a 95% accuracy classifying BCC vs. all lesions have been obtained. The proposed algorithm outperforms the winner of the challenge ISIC 2019 in almost all the metrics. Finally, we can conclude that when deep neural networks are used to classify, a previous segmentation of the lesion does not improve the classification results. Likewise, the ensemble of different neural network configurations improves the classification performance compared with individual neural network classifiers. Regarding the segmentation step, supervised deep learning-based methods outperform unsupervised onesMinisterio de Economía y Competitividad DPI2016-81103-RFEDER-US, Junta de Andalucía US-1381640Fondo Social Europeo Iniciativa de Empleo Juvenil EJ3-83-

Convex Histogram-Based Joint Image Segmentation with Regularized Optimal Transport Cost

We investigate in this work a versatile convex framework for multiple image segmentation, relying on the regularized optimal mass transport theory. In this setting, several transport cost functions are considered and used to match statistical distributions of features. In practice, global multidimensional histograms are estimated from the segmented image regions and are compared to reference models that are either fixed histograms given a priori, or directly inferred in the non-supervised case. The different convex problems studied are solved efficiently using primal--dual algorithms. The proposed approach is generic and enables multiphase segmentation as well as co-segmentation of multiple images.Generalized Optimal Transport Models for Image processin

Image Processing and Analysis for Preclinical and Clinical Applications

Radiomics is one of the most successful branches of research in the field of image processing and analysis, as it provides valuable quantitative information for the personalized medicine. It has the potential to discover features of the disease that cannot be appreciated with the naked eye in both preclinical and clinical studies. In general, all quantitative approaches based on biomedical images, such as positron emission tomography (PET), computed tomography (CT) and magnetic resonance imaging (MRI), have a positive clinical impact in the detection of biological processes and diseases as well as in predicting response to treatment. This Special Issue, “Image Processing and Analysis for Preclinical and Clinical Applications”, addresses some gaps in this field to improve the quality of research in the clinical and preclinical environment. It consists of fourteen peer-reviewed papers covering a range of topics and applications related to biomedical image processing and analysis