A Latent Source Model for Patch-Based Image Segmentation
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A latent source.pdf
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254.11 KB
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Author(s)
Shah, Devavrat
Chen, George
Golland, Polina
Date Issued
November 2015
Journal
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015
Publisher
Springer International Publishing
Citation
Chen, George H., et al. “A Latent Source Model for Patch-Based Image Segmentation.” Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, 5-9 October, 2015, Munich, Germany, edited by Nassir Navab et al., vol. 9351, Springer International Publishing, 2015, pp. 140–48.
Version
Author's final manuscript
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. Keywords: Mixture Model, Image Segmentation, Gaussian Mixture Model, Image Patch, Label Image
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/978-3-319-24574-4_17
