Heart Atherosclerosis disease is one of the world's most critical health issues today. We can detect arterial stenosis and plaque, which are the major causes of this condition, by segmenting and analysing coronary arteries in medical imaging. Manually segmenting coronary arteries is time-consuming and subjective, and most segmentation methods need a suitable starting point, which is challenging to achieve using 3D coronary computed tomography angiography (CTA) data. Medical image segmentation is critical in one of the most difficult sectors of engineering. An imaging modality gives comprehensive anatomical information. It also aids in the detection of the disease and its gradual therapy. In this paper, we used RWPSO (Random Walk Particle Swarm Optimization) segmentation method by combining Random Walk and Particle Swarm Optimization algorithm. By using RWPSO segmentation on medical images we get better Accuracy for Heart Atherosclerosis detection than existing methods

Image segmentation; Random Walk; PSO; RWPSO