Segmentation of Brain Images for Medical Diagnostic Assistance

Abstract

Segmentation of brain images plays a crucial role in medical diagnostics, aiding clinicians in the accurate detection and analysis of various neurological conditions. This paper presents an overview of recent advancements and methodologies in brain image segmentation techniques, focusing on their application in medical diagnostic assistance. Various image-processing methods, such as region-based, boundary-based, and clustering-based approaches, are discussed, along with their advantages and limitations. We have developed an innovative algorithm based in particular on the segmentation by the region-growing method, are explored for their effectiveness in brain image segmentation tasks. The paper highlights the challenges and future directions in this field, including the need for robust algorithms capable of handling diverse image modalities and anatomical variations, as well as the integration of segmentation results into clinical workflows for improved diagnostic accuracy and patient care.