AN EFFICIENT LEVEL SET-BASED METHOD FOR ENHANCED CARDIAC IMAGE SEGMENTATION

Abstract

In the domain of medical image analysis, accurately segmenting cardiac images remains challenging due to issues like intensity inhomogeneity, poor contrast, noise patterns, and blur. To address these challenges, this paper introduces an innovative strategy by utilizing region-based level set evolution in conjunction with the fast algorithm known as Alternating Direction Method of Multipliers (ADMM). By integrating ADMM into the proposed level set model for segmentation, our approach aims to overcome previous limitations, enhancing segmentation accuracy, and concurrently optimizing time efficiency. Through comprehensive experimentation and validation on diverse cardiac image datasets, this paper contributes to advancing segmentation techniques in medical imaging, ultimately enhancing diagnostic capabilities in clinical settings.