Automatic Segmentation and Decision Making of Carotid Artery Ultrasound Images

Abstract

Disease diagnostics based on medical imaging is getting popularity day after day. Presence of the arthrosclerosis is one of the causes of narrowing of carotid arteries which may block partially or fully blood flow into the brain. Serious brain strokes may occur due to such types of blockages in blood flow. Early detection of the plaque and taking precautionary steps in this regard may prevent from such type of serious strokes. In this paper, we present automatic image segmentation and decision making technique for carotid artery ultrasound images based on active contour approach. We have successfully applied the automatic segmentation of carotid artery ultrasound images using snake based model. Intima-media thickness (IMT) measurement is used to form a feature vector for classification. Five different features are extracted from IMT measured values. K-nearest neighbors (KNN) classifier is applied for classification of the images. Qualitative comparison of the proposed approach has been made with the manual initialization of snakes for carotid artery image segmentation. Decision is made based on the feature vector obtained from IMT values. Using the proposed approach we have obtained 98.30% classification accuracy. Our proposed approach successfully segment and classify the carotid artery images in an automated way to help radiologists. Obtained results show the effectiveness of the proposed approach.