Chapter title |
Coupling Statistical Segmentation and PCA Shape Modeling
|
---|---|
Chapter number | 19 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004
|
Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, January 2004
|
DOI | 10.1007/978-3-540-30135-6_19 |
Pubmed ID | |
Book ISBNs |
978-3-54-022976-6, 978-3-54-030135-6
|
Authors |
Kilian M. Pohl, Simon K. Warfield, Ron Kikinis, W. Eric L. Grimson, William M. Wells |
Abstract |
This paper presents a novel segmentation approach featuring shape constraints of multiple structures. A framework is developed combining statistical shape modeling with a maximum a posteriori segmentation problem. The shape is characterized by signed distance maps and its modes of variations are generated through principle component analysis. To solve the maximum a posteriori segmentation problem a robust Expectation Maximization implementation is used. The Expectation Maximization segmenter generates a label map, calculates image intensity inhomogeneities, and considers shape constraints for each structure of interest. Our approach enables high quality segmentations of structures with weak image boundaries which is demonstrated by automatically segmenting 32 brain MRIs into right and left thalami. |
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