Overview of Medical Image Segmentation Techniques through Artificial Intelligence and Computer Vision

Abstract

Medical image segmentation is a crucial task in computer vision, playing a pivotal role in applications such as diagnostics, treatment planning, and medical research. The present study explores a wide range of methodologies employed in the field of medical research to achieve image segmentation. These techniques range from traditional approaches based on thresholding, edge detection, region-based and clustering, to modern artificial intelligence methods, particularly deep learning techniques. The strengths and limitations of each method are thoroughly examined. This paper focuses on analyzing various architectures used for medical image segmentation, specifically evaluating their performance. It aims to delve deeply into the different segmentation methods, offering a comparative perspective on their effectiveness. Furthermore, This document delves into the most recent technological progress in segmentation, emphasizing major breakthroughs capable of transforming the precision and productivity of analyzing medical images. Through an exhaustive compilation and detailed critique of the results obtained by employing a range of segmentation strategies, the study presents the outcomes of multiple approaches, accompanied by an in-depth analysis of the strengths and weaknesses inherent to the various techniques applied to medical image segmentation. This research enhances the comprehension of how these methods can be applied within the medical sector, especially in the area of computer vision.