TY  - CHAP
Y1  - 2011///
EP  - 283
TI  - Shape analysis of the left ventricular endocardial surface and its application in detecting coronary artery disease
UR  - http://dx.doi.org/10.1007/978-3-642-21028-0_34
A1  - Mukhopadhyay, Anirban
A1  - Qian, Zhen
A1  - Bhandarkar, Suchendra M.
A1  - Liu, Tianming
A1  - Voros, Szilard
PB  - Springer
T2  - Functional Imaging and Modeling of the Heart
AV  - none
SN  - 978-3-642-21027-3
T3  - Lecture Notes in Computer Science
SP  - 275
KW  - Ventricular endocardial surface; cardiovascular CT; shape analysis
ID  - eprints2233
N2  - Coronary artery disease is the leading cause of morbidity and mortality worldwide. The complex morphological structure of the ventricular endocardial surface has not yet been studied properly due to the limitations of conventional imaging techniques. With the recent developments in Multi-Detector Computed Tomography (MDCT) scanner technology, we propose to study, in this paper, the complex endocardial surface morphology of the left ventricle via analysis of Computed Tomography (CT) image data obtained from a 320 Multi-Detector CT scanner. The CT image data is analyzed using a 3D shape analysis approach and the clinical significance of the analysis in detecting coronary artery disease is investigated. Global and local 3D shape descriptors are adapted for the purpose of shape analysis of the left ventricular endocardial surface. In order to study the association between the incidence of coronary artery disease and the alteration of the endocardial surface structure, we present the results of our shape analysis approach on 5 normal data sets, and 6 abnormal data sets with obstructive coronary artery disease. Based on the morphological characteristics of the endocardial surface as quantified by the shape descriptors, we implement a Linear Discrimination Analysis (LDA)-based classification algorithm to test the effectiveness of our shape analysis approach. Experiments performed on a strict leave-one-out basis are shown to achieve a classification accuracy of 81.8%.
ER  -