Model-based segmentation of medical imagery by matching distributions
Freedman, D.
Radke, R.J.
Tao Zhang
Yongwon Jeong
Lovelock, D.M.
Chen, G.T.Y.
Dept. of Comput. Sci., Rensselaer Polytech. Inst., Troy, NY, USA;
This paper appears in: Medical Imaging, IEEE Transactions on
Publication Date: March 2005
Volume: 24,
Issue: 3
On page(s): 281-292
Location: Davis, CA, USA,
ISSN: 0278-0062
INSPEC Accession Number: 8326021
Digital Object Identifier: 10.1109/TMI.2004.841228
Current Version Published: 2005-02-28
Abstract
The segmentation of deformable objects from three-dimensional (3-D) images is an important and challenging problem, especially in the context of medical imagery. We present a new segmentation algorithm based on matching probability distributions of photometric variables that incorporates 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. This allows for a fast, principled algorithm. We present promising results on difficult imagery for 3-D computed tomography images of the male pelvis for the purpose of image-guided radiotherapy of the prostate.
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