Enhanced Detection of Cerebral Atherosclerosis Using Hybrid Algorithm of Image Segmentation

Abstract

In medical science for envisaging human body’s phenomenal structure a major part has been driven by image processing techniques. Major objective of this work is to detect of cerebral atherosclerosis for image segmentation application. Detection of some abnormal structures in human body has become a difficult task to complete with some simple images. For expounding and distinguishing neural architecture of human brain in an effective manner, MRI (Magnetic Resonance Imaging) is one of the most suitable and significant technique. Here we work on detection of Cerebral Atherosclerosis from MRI images of patients. Cerebral Atherosclerosis is a cerebral vascular disease causes narrowing of the arteries due to buildup of fatty plaque inside the blood vessels of the brain. It leads to Ischemic stroke if not diagnosed early. Stroke affects majorly old age people and percentage of affected women is more compared to men. Results: Preprocessing is done by using alpha trimmed mean filter which is used to remove noise and also it enhances the image. Segmentation of cerebral atherosclerosis is done by using K-means clustering, Contextual clustering, and proposed Hybrid algorithm. Various parameters like Correlation, Pixel density, energy is determined and from the analysis of parameters it is determined that proposed Hybrid algorithm is efficient.