A data-driven approach to prior extraction for segmentation of left ventricle in cardiac MR images

Authors:
Xiao Jia;Chao Li;Ying Sun;Ashraf A. Kassim;Yijen L. Wu;T. Kevin Hitchens;Chien Ho
Affiliations:
Department of Electrical & Computer Engineering, National University of Singapore, Singapore, Singapore;Department of Electrical & Computer Engineering, National University of Singapore, Singapore, Singapore;Department of Electrical & Computer Engineering, National University of Singapore, Singapore, Singapore;Department of Electrical & Computer Engineering, National University of Singapore, Singapore, Singapore;Pittsburgh NMR Center for Biomedical Research, Carnegie Mellon University, Pittsburgh, PA;Pittsburgh NMR Center for Biomedical Research, Carnegie Mellon University, Pittsburgh, PA;Pittsburgh NMR Center for Biomedical Research, Carnegie Mellon University, Pittsburgh, PA
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

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Abstract

In this paper, we propose a data-driven approach that extracts prior information for segmentation of the left ventricle in cardiac MR images of transplanted rat hearts. In our approach, probabilistic priors are generated from prominent features, i.e., corner points and scale-invariant edges, for both endoand epi-cardium segmentation. We adopt a level set formulation that integrates probabilistic priors with intensity, texture, and edge information for segmentation. Our experimental results show that with minimal user input, representative priors are correctly extracted from the data itself, and the proposed method is effective and robust for segmentation of the left ventricle myocardium even in images with very low contrast. More importantly, it avoids inter- and intra- observer variations and makes accurate quantitative analysis of low-quality cardiac MR images possible.