title: Morphological analysis of the left ventricular endocardial surface using a bag-of-features descriptor.
abstract: 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). However, recent developments in high-resolution multirow-detector computed tomography (MDCT) scanner technology have enabled the imaging of the complex LV endocardial surface morphology in a single heartbeat. Analysis of high-resolution computed tomography images from a 320-MDCT scanner allows for the noninvasive study of the relationship between the percent diameter stenosis (DS) values 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 of the nonrigid LV endocardial surface from MDCT images, using a combination of rigid body transformation-invariant shape descriptors and a more generalized isometry-invariant Bag-of-Features 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. The results of the proposed approach on 16 normal datasets and 16 abnormal datasets exhibiting CAD with varying levels of severity are presented. A multivariable regression test is employed to test the effectiveness of the proposed morphological analysis approach. Experiments performed on a strictly leave-one-out basis are shown to exhibit a distinct and interesting pattern in terms of the correlation coefficient values within the cardiac segments, where the incidence of coronary arterial stenosis is localized.
authors.forename: Anirban
authors.initials: A
authors.lastname: Mukhopadhyay
authors.forename: Zhen
authors.initials: Z
authors.lastname: Qian
authors.forename: Suchendra M
authors.initials: SM
authors.lastname: Bhandarkar
authors.forename: Tianming
authors.initials: T
authors.lastname: Liu
authors.forename: Szilard
authors.initials: S
authors.lastname: Voros
authors.forename: Sarah
authors.initials: S
authors.lastname: Rinehart
mesh_terms: D000465:Algorithms; D000818:Animals; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging; D016208:Databases, Factual; D004699:Endocardium / Q000473:pathology*; D006352:Heart Ventricles / Q000473:pathology*; D006801:Humans; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013552:Swine; D014057:Tomography, X-Ray Computed / Q000379:methods