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2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 1
Model-Based Multi-Object Segmentation via Distribution Matching
Washington, D.C., USA
June 27-July 02
ISBN: 0-7695-2158-4
Daniel Freedman, Rensselaer Polytechnic Institute, Troy, NY
Richard J. Radke, Rensselaer Polytechnic Institute, Troy, NY
Tao Zhang, Rensselaer Polytechnic Institute, Troy, NY
Yongwon Jeong, Rensselaer Polytechnic Institute, Troy, NY
George T. Y. Chen, Massachusetts General Hospital, Boston, MA
A new algorithm for the segmentation of objects from 3D images using deformable models is presented. This algorithm relies on learned shape and appearance models for the objects of interest. The main innovation over similar approaches is that there is no need to compute a pixelwise correspondence between the model and the image; instead, probability distributions are compared. This allows for a faster, more principled algorithm. Furthermore, the algorithm is not sensitive to the form of the shape model, making it quite flexible. Results of the algorithm are shown for the segmentation of the prostate and bladder from medical images.
Index Terms:
deformable segmentation, prostate segmentation, shape and appearance model, medical image segmentation
Citation:
Daniel Freedman, Richard J. Radke, Tao Zhang, Yongwon Jeong, George T. Y. Chen, "Model-Based Multi-Object Segmentation via Distribution Matching," cvprw, vol. 1, pp.11, 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 1, 2004
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