DocumentCode :
3509894
Title :
Estimating patient-specific shape prior for medical image segmentation
Author :
Zhang, Wuxia ; Yan, Pingkun ; Li, Xuelong
Author_Institution :
State Key Lab. of Transient, Opt. & Photonics, Chinese Acad. of Sci., Xi´´an, China
fYear :
2011
fDate :
March 30 2011-April 2 2011
Firstpage :
1451
Lastpage :
1454
Abstract :
Image segmentation is one of the key problems in medical image analysis. This paper presents a new statistical shape model for automatic image segmentation. In contrast to the previous model based segmentation methods, where shape priors are estimated from a general population-based shape model, our proposed method aims to estimate patient-specific shape priors to achieve more accurate segmentation by using manifold learning techniques. The proposed shape prior estimation method is incorporated into a deformable model based framework for image segmentation. The effectiveness of the proposed method has been demonstrated by the experiments on segmenting the prostate from MR images.
Keywords :
biomedical MRI; image segmentation; medical image processing; MR images; automatic image segmentation; deformable model based framework; manifold learning techniques; medical image segmentation; patient-specific shape; prostate; statistical shape model; Image segmentation; Manifolds; Manuals; Pixel; Shape; Silicon; Training; image segmentation; manifold learning; shape modeling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on
Conference_Location :
Chicago, IL
ISSN :
1945-7928
Print_ISBN :
978-1-4244-4127-3
Electronic_ISBN :
1945-7928
Type :
conf
DOI :
10.1109/ISBI.2011.5872673
Filename :
5872673
Link To Document :
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