Key result
Multi-level set segmentation improves IVUS region-of-interest extraction accuracy over standard methods.
Why the study?
Intima-Media Thickness (IMT) is a significant indicator for evaluating atherosclerosis development, but optimal segmentation techniques for IVUS images require comparison and validation.
Answers are grounded in this paper and cite their sources.
A Multi-Level Set based segmentation technique improves the accuracy of Intima-Media Thickness extraction from IVUS images compared to traditional methods.
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.
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Archana et al. (2022) studied Atherosclerosis. Multi-Level Set Based segmentation technique vs. Otsu's segmentation, Active Contour and Watershed segmentation was evaluated on Segmentation performance metrics (Jaccard Index, Dice Coefficient, Cohen Kappa Coefficient, Variation of Information, Global Consistency Error, and Rand Index). Multi-Level Set based segmentation technique demonstrated high Jaccard Index, Dice Coefficient, Cohen Kappa Coefficient, and Rand Index for extracting the Region of Interest in IVUS images.
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