In recent years, the segmentation and projection techniques of different structures of medical interest have had significant growth due to its usefulness; doctors have been using them as tools for the diagnosis and evolution of different diseases. The segmentation of the Inner Limiting Membrane (ILM) in retinal scans acquired using the Optical Coherence Tomography (OCT) imaging technique has generated particular interest in the medical area since it provides clinically relevant information about diseases such as Glaucoma, Diabetic Macular Edema (DME) or Multiple Sclerosis. Furthermore, the generation of a surface that shows the current morphological situation of the scanned retinal area is a tool that complements the medical analysis. In this paper, a new methodology for the ILM segmentation on OCT retinal images and a surface projection from different axially spaced scans acquired over the macular and peripapillary zone is presented. The proposed scheme consists of a wavelet-based denoising step and a contrast enhancement stage using Eigenvalues of Hessian matrix, while the segmentation process is based on the Canny edge detection algorithm; these stages are applied to each image of a C-scan for a later surface generation using Cubic spline interpolation. This method is applied to a publicly available OCT data-set composed of 22 patients with several retinal diseases obtaining a correct individual segmentation of each image, while the surface generation results demonstrate high performance in the visualization of the ILM morphology, which can be used for dimensional analysis of this membrane.
Breast cancer represents the most common type of cancer worldwide among women. One of the most important diagnostic methods of this disease are mammograms, however, the high prevalence of breast cancer has not been reduced due to the incorrect diagnosis of these images, since they can be complex to interpret. An approach that represents a fundamental process for the improvement of this diagnosis is digital image processing, since it can facilitate the interpretation of the images for the specialists. In this work is proposed the implementation of a new multilevel segmentation approach based on the minimum cross-entropy threshold - Harris Hawks Optimization (MCET-HHO) metaheuristic algorithm, identifying regions within the breast that have abnormal tissue. Then, these regions are subjected to an automatic classification system based on a bag-of-visual-words (BoVW) approach to identify healthy tissue, benign tumors, and malignant tumors. According to the results, the classifier reached an average accuracy of 0.86 in the training stage and 0.73 in the testing, proving to be statistically significant in the automatic classification of mammograms, presenting a preliminary tool for the support of specialists in the diagnosis of mammography images.
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