NDLI: Segmentation of left ventricle from 3D cardiac MR image sequences using a subject-specific dynamical model

Content Provider	IEEE Xplore Digital Library
Author	Yun Zhu Papademetris, X. Sinusas, A. Duncan, J.S.
Copyright Year	2008
Description	Author affiliation: Dept. of Biomed. Eng. & Diagnostic Radiol., Yale Univ., New Haven, CT (Yun Zhu; Papademetris, X.; Sinusas, A.; Duncan, J.S.)
Abstract	Statistical model-based segmentation of the left ventricle from cardiac images has received considerable attention in recent years. While a variety of statistical models have been shown to improve segmentation results, most of them are either static models (SM) which neglect the temporal coherence of a cardiac sequence or generic dynamical models (GDM) which neglect the inter-subject variability of cardiac shapes and deformations. In this paper, we use a subject-specific dynamical model (SSDM) that handles inter-subject variability and temporal dynamics (intra-subject variability) simultaneously. It can progressively identify the specific motion patterns of a new cardiac sequence based on the segmentations observed in the past frames. We formulate the integration of the SSDM into the segmentation process in a recursive Bayesian framework in order to segment each frame based on the intensity information from the current frame and the prediction from the past frames. We perform ldquoleave-one-outrdquo test on 32 sequences to validate our approach. Quantitative analysis of experimental results shows that the segmentation with the SSDM outperforms those with the SM and GDM by having better global and local consistencies with the manual segmentation.
Starting Page	1
Ending Page	8
File Size	768646
Page Count	8
File Format	PDF
ISBN	9781424422425
ISSN	10636919
DOI	10.1109/CVPR.2008.4587433
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Image sequences Shape Principal component analysis Independent component analysis Deformable models Samarium Bayesian methods Active appearance model Heuristic algorithms
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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