DocumentCode :
1771739
Title :
Segmentation with a shape dictionary
Author :
Wenyang Liu ; Dan Ruan
Author_Institution :
Dept. of Bioeng., Univ. of California, Los Angeles, Los Angeles, CA, USA
fYear :
2014
fDate :
April 29 2014-May 2 2014
Firstpage :
357
Lastpage :
360
Abstract :
Image segmentation plays an important role in many medical applications. Automatic segmentation algorithms are challenged by low SNR and significant artifacts resulting from motion and signal voids. In this study, we propose a novel level set based segmentation method with a shape dictionary. Unlike previous studies that use a single template or probabilistic models, we propose to construct a shape dictionary and model the shape prior as sparse combinations of shape templates in the dictionary. The proposed method generated promising segmentation results on low SNR MR images, even with signal voids.
Keywords :
biomedical MRI; image segmentation; medical image processing; automatic segmentation algorithms; image segmentation; low SNR MR images; magnetic resonance imaging; probabilistic models; shape dictionary; shape templates; signal voids; Biomedical imaging; Dictionaries; Image segmentation; Level set; Motion segmentation; Robustness; Shape; Level Set; Segmentation; Shape dictionary; Shape prior; Sparse;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on
Conference_Location :
Beijing
Type :
conf
DOI :
10.1109/ISBI.2014.6867882
Filename :
6867882
Link To Document :
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