Chapter title |
A Latent Source Model for Patch-Based Image Segmentation.
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Chapter number | 17 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015
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Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, January 2015
|
DOI | 10.1007/978-3-319-24574-4_17 |
Pubmed ID | |
Book ISBNs |
978-3-31-924573-7, 978-3-31-924574-4
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Authors |
George H. Chen, Devavrat Shah, Polina Golland |
Editors |
Nassir Navab, Joachim Hornegger, William M. Wells, Alejandro F. Frangi |
Abstract |
Despite the popularity and empirical success of patch-based nearest-neighbor and weighted majority voting approaches to medical image segmentation, there has been no theoretical development on when, why, and how well these nonparametric methods work. We bridge this gap by providing a theoretical performance guarantee for nearest-neighbor and weighted majority voting segmentation under a new probabilistic model for patch-based image segmentation. Our analysis relies on a new local property for how similar nearby patches are, and fuses existing lines of work on modeling natural imagery patches and theory for nonparametric classification. We use the model to derive a new patch-based segmentation algorithm that iterates between inferring local label patches and merging these local segmentations to produce a globally consistent image segmentation. Many existing patch-based algorithms arise as special cases of the new algorithm. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 5 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 2 | 40% |
Student > Bachelor | 2 | 40% |
Other | 1 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 4 | 80% |
Engineering | 1 | 20% |