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Content Provider | IEEE Xplore Digital Library |
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Author | Tsai, A. Yezzi Jr., A. Wells III, W. Tempany, C. Tucker, D. Fan, A. Grimson, W.E. Willsky, A. |
Copyright Year | 2001 |
Description | Author affiliation: Dept. of Electr. Eng. & Comput. Sci., MIT, Cambridge, MA, USA (Tsai, A.) |
Abstract | We propose a model-based curve evolution technique for segmentation of images containing known object types. In particular, motivated by the work of Leventon et al. (2000), we derive a parametric model for an implicit representation of the segmenting curve by applying principal component analysis to a collection of signed distance representations of the training data, The parameters of this representation are then calculated to minimize an objective function for segmentation. We found the resulting algorithm to be computationally efficient, able to handle multidimensional data, robust to noise and initial contour placements, while at the same time, avoiding the need for point correspondences during the training phase of the algorithm. We demonstrate this technique by applying it to two medical applications. |
Sponsorship | IEEE Comput. Soc. Tech. Committee on Pattern Analysis & Machine Intelligence |
File Size | 885838 |
File Format | |
ISBN | 0769512720 |
ISSN | 10636919 |
DOI | 10.1109/CVPR.2001.990511 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2001-12-08 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Image segmentation Shape Noise robustness Principal component analysis Artificial intelligence Laboratories Hospitals Biomedical imaging Training data Multidimensional systems |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition Software |
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