2013/02/13 by Anirban Mukhopadhyay, Zhen Qian, Mukhopadhyay, Anirban +10 · 1 citation
Computer Science · Engineering · Mathematics · #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Geography #Geometry #Industrial Vision Systems and Defect Detection #Mathematics #Medical Image Segmentation Techniques #Surface (topology) #cs.CV
paper · pdf · doi:10.48550/arxiv.1302.3155
published in arXiv (Cornell University) (Cornell University) · Submitted to Medical Image Analysis
arxiv created 2013/02/13 · openalex publication_date 2013/02/13 · arxiv updated 2013/02/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The limitations of conventional imaging techniques have hitherto precluded a thorough and formal investigation of the complex morphology of the left ventricular (LV) endocardial surface and its relation to the severity of Coronary Artery Disease (CAD). Recent developments in high-resolution Multirow-Detector Computed Tomography (MDCT) scanner technology have enabled the imaging of LV endocardial surface morphology in a single heart beat. Analysis of high-resolution Computed Tomography (CT) images from a 320-MDCT scanner allows the study of the relationship between percent Diameter Stenosis (DS) of the major coronary arteries and localization of the cardiac segments affected by coronary arterial stenosis. In this paper a novel approach for the analysis using a combination of rigid transformation-invariant shape descriptors and a more generalized isometry-invariant Bag-of-Features (BoF) descriptor, is proposed and implemented. The proposed approach is shown to be successful in identifying, localizing and quantifying the incidence and extent of CAD and thus, is seen to have a potentially significant clinical impact. Specifically, the association between the incidence and extent of CAD, determined via the percent DS measurements of the major coronary arteries, and the alterations in the endocardial surface morphology is formally quantified. A multivariate regression test performed on a strict leave-one-out basis are shown to exhibit a distinct pattern in terms of the correlation coefficient within the cardiac segments where the incidence of coronary arterial stenosis is localized.