Through optical equipment such as the ophthalmoscope, it is possible to visualize and image the inner surface of the eye, where the main structures of the retina can be observed. The visual analysis of the retinal vasculature is widely used by ophthalmologists for prevention, diagnosis, and monitoring of retinal diseases. Nevertheless, derived from pathologies that generate an opacity in the crystalline lens (such as cataracts), the task of visualize blood vessels becomes difficult, since there is a lack of contrast in the fundus image. In this work, a multiscale decomposition method based on the Weighted Least Squares (WLS) optimization is applied to cataractous eye fundus images, with the aim of obtaining a better blood-vessel to background contrast. The proposed scheme is implemented over a publicly-available cataract eye fundus dataset. The experimental results provide a notorious visual improvement in contrast and restoration of blood vessels pixels and, in addition, maintains adequate saturation and lighting for visual analysis. The visual improvement of the vasculature represents a potential benefit in the ophthalmic analysis of patients with cataracts, since it is possible to observe the vascular morphology in greater detail while keeping relevant image features.
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.
We introduce an alternative hybrid swarm algorithm for image segmentation that employs multilevel thresholding techniques. For the hybridization, we have combined the whale optimization algorithm (WOA) and the particle swarm optimization (PSO). The proposed method is called WOAPSO, and it operates in a cooperative environment, where the initial population is divided into two subpopulations (the first subpopulation is assigned for WOA and the other is assigned for PSO). Then, the WOA and the PSO operate in parallel during the iterative process to update the solutions and the best solution is selected from the union of the updated subpopulations according to the objective function. Here, two objective functions are used, the Otsu’s method and the fuzzy entropy method. These functions evaluate the quality of the thresholds generated by the WOAPSO considering the variance and the entropy of the classes where the pixels are cataloged. The experimental results and comparisons provide evidence of the ability of the proposed WOAPSO algorithm to reduce the time complexity without affecting the accuracy of the solutions.
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