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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2009.5
- 수록면
- 73 - 76 (4page)
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초록· 키워드
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We present a novel segmentation approach to jointly delineate the boundaries of epiand endocardium of the left ventricle on the Magnetic Resonance Imaging (MRI) images in a variational framework using level sets. While most left ventricle segmentation approaches incorporate a shape prior obtained by a training process to robustly delineate the target object with a subtle boundary, we exploit a novel shape constraint as an implicit shape prior knowledge, which allows distance variation between epi- and endocardium in the Gaussian distribution. Our approach does not demand a training procedure in generating the shape prior. Instead, we use a statistical shape constraint to introduce a shape prior by adjusting the distance distribution measured by employing signed distance functions. Cardiac MRI data are used in our experiment and quantitative evaluations are performed on 10 subjects and the results demonstrate the accuracy and robustness of the proposed approach.
목차
abstract
Introduction
Proposed Shape Constraint
Variational Formulation and Bayesian Inference for Coupled Segmentation
Energy Formulation
Energy Minimization
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UCI(KEPA) : I410-ECN-0101-2012-410-003697046