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Proceedings Paper

Curve evolution with a dual shape similarity and its application to segmentation of left ventricle
Author(s): Jonghye Woo; Byung-Woo Hong; Amit Ramesh; Guido Germano; C.-C. Jay Kuo; Piotr Slomka
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Paper Abstract

Automated image segmentation has been playing a critical role in medical image analysis. Recentl, Level Set methods have shown an efficacy and efficiency in various imaging modalities. In this paper, we present a novel segmentation approach to jointly delineate the boundaries of epi- and endocardium of the left ventricle on the Magnetic Resonance Imaging (MRI) images in a variational framework using level sets, which is in great demand as a clinical application in cardiology. One strategy to tackle segmentation under undesirable conditions such as subtle boundaries and occlusions is to exploit prior knowledge which is specific to the object to segment, in this case the knowledge about heart anatomy. While most left ventricle segmentation approaches incorporate a shape prior obtained by a training process from an ensemble of examples, we exploit a novel shape constraint using an implicit shape prior knowledge, which assumes shape similarity between epi- and endocardium allowing a variation under the Gaussian distribution. Our approach does not demand a training procedure which is usually subject to the training examples and is also laborious and time-consuming in generating the shape prior. Instead, we model a shape constraint by a statistical distance between the shape of epi- and endocardium employing signed distance functions. We applied this technique to cardiac MRI data with quantitative evaluations performed on 10 subjects. The experimental results show the robustness and effectiveness of our shape constraint within a Mumford-Shah segmentation model in the segmentation of left ventricle from cardiac MRI images in comparison with the manual segmentation results.

Paper Details

Date Published: 27 March 2009
PDF: 8 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72593T (27 March 2009); doi: 10.1117/12.814142
Show Author Affiliations
Jonghye Woo, Univ. of Southern California (United States)
Byung-Woo Hong, Chung-Ang Univ. (Korea, Republic of)
Amit Ramesh, Cedars-Sinai Medical Ctr. (United States)
Guido Germano, Cedars-Sinai Medical Ctr. (United States)
C.-C. Jay Kuo, Univ. of Southern California (United States)
Piotr Slomka, Cedars-Sinai Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

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