Segmentation of Intima – Media Thickness in Intravascular Ultrasound Images for Detection of Atherosclerosis

Abstract

Cardiovascular Diseases (CVDs) are the leading cause of deaths, and adverse CVDs are related to Coronary Artery Disease (CAD). CAD is caused due to the accumulation of fatty lesions called plaques on the vessels that nourish the heart with blood. The Intravascular Ultrasound (IVUS) imaging modality has captured considerable attention in the diagnosis of CVDs in recent years. Generally coronary artery consists of three distinct regions: Media, Intima and Luminal region. Intima-Media Thickness (IMT) is perceived as a significant indicator in the risk evaluation process, tracking the amount of atherosclerosis development. In this paper, IVUS image is preprocessed using Total Variance Regularization for speckle noise removal and the contrast is improved by Contrast Limited Adaptive Histogram Equalization (CLAHE) technique. Region of Interest (RoI) is extracted using segmentation techniques such as Multi-Level Set Based, Otsu’s segmentation, Active Contour and Watershed segmentation and their performances are compared. The performance metrics used are Jaccard Index (JAC), Dice Coefficient (DC), Cohen Kappa Coefficient (KAP), Variation of Information (VOI), Global Consistency Error (GCE), and Rand Index (RI). From the analysis, it is observed that the Multi-Level Set based technique has a high JAC, DICE, KAP and RI. These values indicate the similarity between the segmented and ground truth image. Also the value of GI, indicates the less error measurement between segmented image and ground truth image. The significance of using Multi- level set based technique is that it uses the B-spline function-based curvature updation. This function is less dependent on the degree, smoothness and domain partition of the image, resulting in increased segmenting accuracy.

Optimal Deep Learning Based Atherosclerotic Plaque Classification on Intravascular Ultrasound Images

“…16 somewhat challenging due to great scattering from erythrocytes within the lumen. Thus, automated estimate of the luminal borders and media-adventitia is indispensable to evaluate the degree of stenosis and luminal region during IVUS-assisted diagnoses [6]. Whereas many automatic solutions have been introduced for assisting IVUS segmentation, not any technique has demonstrated its potential to precisely identify the external elastic membrane (EEM) and lumen border and process IVUS images in real-time [7].…”