Please use this identifier to cite or link to this item: https://doi.org/10.1109/ISBI.2009.5193181
Title: A data-driven approach to prior extraction for segmentation of left ventricle in cardiac mr images
Authors: Jia, X. 
Li, C.
Sun, Y. 
Kassim, A.A. 
Wu, Y.L.
Hitchens, T.K.
Ho, C.
Keywords: Cardiac MRI
Left ventricle
Prior leaning
Segmentation
Issue Date: 2009
Source: Jia, X., Li, C., Sun, Y., Kassim, A.A., Wu, Y.L., Hitchens, T.K., Ho, C. (2009). A data-driven approach to prior extraction for segmentation of left ventricle in cardiac mr images. Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 : 831-834. ScholarBank@NUS Repository. https://doi.org/10.1109/ISBI.2009.5193181
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. © 2009 IEEE.
Source Title: Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009
URI: http://scholarbank.nus.edu.sg/handle/10635/68769
ISBN: 9781424439324
DOI: 10.1109/ISBI.2009.5193181
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

1
checked on Dec 7, 2017

Page view(s)

28
checked on Dec 11, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.