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

Abstract

Medical image segmentation is a crucial task in computer vision with significant implications in diagnostics, treatment planning, and medical research.This study comprehensively explores various methodologies employed for image segmentation within the medical field, ranging from traditional techniques such as thresholding, edge detection, region-based methods, and clustering, to advanced artificial intelligence strategies, particularly deep learning.Each method's strengths and limitations are thoroughly examined to provide a clear perspective on their effectiveness.The paper focuses on analyzing different architectures specifically used for medical image segmentation, evaluating their performance meticulously.It aims to delve deeply into the varied segmentation techniques, providing a comparative analysis that highlights their effectiveness across different scenarios.Additionally, the study addresses the latest technological advancements in segmentation, emphasizing breakthroughs that have the potential to transform the accuracy and efficiency of medical image analysis.An exhaustive compilation and detailed critique of results from employing various segmentation strategies are presented, offering insights into the outcomes of diverse approaches.This includes an in-depth discussion of the inherent strengths and weaknesses of the techniques used in medical image segmentation.The research enhances understanding of how these methodologies can be applied effectively within the medical sector, particularly in areas leveraging computer vision.By advancing knowledge in this field, the study paves the way for future research that could further improve the capabilities and applications of image segmentation technology in medicine, potentially leading to better patient outcomes and more efficient medical practices.This research enhances the comprehension of how these methods can be applied within the medical sector, especially in the area of computer vision.