Roadmap to Biomedical Image Segmentation and Processing – Background and Approaches

Abstract

The exponential growth in digital imaging and computer vision in ophthalmology has enhanced the ability to implement progress in the processing of images. In the production of medical diagnostic systems, image processing tools are used in regular clinical procedures. Retinal images contain valuable information regarding the status of the visual system sensory component. The retinal image can lead to a decrease in blindness, as an object, in retinal diseases such as glaucoma, diabetic retinopathy, age-related macular degeneration, Stargardt disease, and premature retinopathy. An automated system can be used to obtain standardized, low-cost, huge-scale screening that can take human mistakes, provide remote areas with services, as well as safe bias and exhaustion of availability. Retinal diseases are being therapized but an extremely complex, cost-effective solution to identify those at risk at the early stages of the disease can be delivered quickly to larger populations. The realistic account of all challenges and opportunities of medical imaging from engineering perspectives requires a complete study of available methods. Objective: In this chapter, huge potential efforts are made to study the complete background of medical imaging segmentation with the detail of how machine learning contributed to biomedical science. Methods: Machine learning models. Findings Roadmap of machine learning to image segmentation. Novelty Methods are described by appropriate methods of processing.