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Segmentation and Classification of Carotid Artery Ultrasound Images using Active Contours

Manikandan .V, Mohammed Farook .I, Dhanalakshmi .S

This work presents a novel approach on completely automated technique for recognition and segmentation of carotid-arteries (CA) in view of computing various dynamic properties such as intimamedia thickness (IMT) measurement and distal (far) wall segmentation. The goal of this work is to provide a clinical tool for cardiovascular diseases. Atherosclerosis is a generalized disease that causes lesions in large- and medium-sized elastic and muscular arteries and this is the major cause for cardio-vascular disease. The fully automated segmentation algorithm is based on active contours and active contours without edges and incorporates anatomical information to achieve accurate segmentation. The segmented regions are used to automatically achieve image normalization, which is followed by speckle removal. The resulting smoothed lumen-intima boundary combined with anatomical information provide an excellent initialization for parametric active contours that provide the final IMT segmentation. After segmentation the carotid artery ultrasound image is classified as normal or abnormal depends on the parametric values extracted.