Atlas based AAM and SVM model for fully automatic MRI prostate segmentation

Automatic prostate segmentation in MR images is a challenging task due to inter-patient prostate shape and texture variability, and the lack of a clear prostate boundary. We propose a supervised learning framework that combines the atlas based AAM and SVM model to achieve a relatively high segmentat...

Full description

Saved in:
Bibliographic Details
Published in:2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2014-01, Vol.2014, p.2881-2585
Main Authors: Ruida Cheng, Turkbey, Baris, Gandler, William, Agarwal, Harsh K., Shah, Vijay P., Bokinsky, Alexandra, McCreedy, Evan, Wang, Shijun, Sankineni, Sandeep, Bernardo, Marcelino, Pohida, Thomas, Choyke, Peter, McAuliffe, Matthew J.
Format: Article
Language:English
Subjects:
Active appearance model
Image segmentation
Magnetic resonance imaging
Shape
Solid modeling
Support vector machines
Training
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Automatic prostate segmentation in MR images is a challenging task due to inter-patient prostate shape and texture variability, and the lack of a clear prostate boundary. We propose a supervised learning framework that combines the atlas based AAM and SVM model to achieve a relatively high segmentation result of the prostate boundary. The performance of the segmentation is evaluated with cross validation on 40 MR image datasets, yielding an average segmentation accuracy near 90%.
ISSN:1094-687X
1558-4615
2694-0604
DOI:10.1109/EMBC.2014.6944225