title: Identification of Mitral Annulus Hinge Point Based on Local Context Feature and Additive SVM Classifier.
abstract: The position of the hinge point of mitral annulus (MA) is important for segmentation, modeling and multimodalities registration of cardiac structures. The main difficulties in identifying the hinge point of MA are the inherent noisy, low resolution of echocardiography, and so on. This work aims to automatically detect the hinge point of MA by combining local context feature with additive support vector machines (SVM) classifier. The innovations are as follows: (1) designing a local context feature for MA in cardiac ultrasound image; (2) applying the additive kernel SVM classifier to identify the candidates of the hinge point of MA; (3) designing a weighted density field of candidates which represents the blocks of candidates; and (4) estimating an adaptive threshold on the weighted density field to get the position of the hinge point of MA and exclude the error from SVM classifier. The proposed algorithm is tested on echocardiographic four-chamber image sequence of 10 pediatric patients. Compared with the manual selected hinge points of MA which are selected by professional doctors, the mean error is in 0.96 ± 1.04 mm. Additive SVM classifier can fast and accurately identify the MA hinge point.
authors.affiliation: School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China.
authors.forename: Jianming
authors.identifier: 0000-0002-4278-0805
authors.initials: J
authors.lastname: Zhang
authors.affiliation: School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China.
authors.forename: Yangchun
authors.initials: Y
authors.lastname: Liu
authors.affiliation: School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D002648:Child; D019295:Computational Biology; D019560:Echocardiography, Three-Dimensional / Q000706:statistics & numerical data*; D006330:Heart Defects, Congenital / Q000000981:diagnostic imaging; D006349:Heart Valve Diseases / Q000000981:diagnostic imaging; D006801:Humans; D008943:Mitral Valve / Q000000981:diagnostic imaging*; D060388:Support Vector Machine