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.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Ailon, N., Chazelle, B.: Approximate nearest neighbors and the fast johnson-lindenstrauss transform. In: Symposium on Theory of Computing (2006)
Bai, W., et al.: A probabilistic patch-based label fusion model for multi-atlas segmentation with registration refinement: Application to cardiac MR images. Transactions in Medical Imaging (2013)
Barnes, C., et al.: Patchmatch: a randomized correspondence algorithm for structural image editing. Transactions on Graphics (2009)
Boyd, S., et al.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations & Trends in Machine Learning (2011)
Chen, G.H., Nikolov, S., Shah, D.: A latent source model for nonparametric time series classification. In: Neural Information Processing Systems (2013)
Coupé, P., et al.: Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage (2011)
Freeman, W.T., Liu, C.: Markov random fields for super-resolution and texture synthesis. In: Advances in Markov Random Fields for Vision and Image Proc. (2011)
Hanbury, A., Müller, H., Langs, G., Weber, M.A., Menze, B.H., Fernandez, T.S.: Bringing the algorithms to the data: Cloud–based benchmarking for medical image analysis. In: Catarci, T., Forner, P., Hiemstra, D., Peñas, A., Santucci, G. (eds.) CLEF 2012. LNCS, vol. 7488, pp. 24–29. Springer, Heidelberg (2012)
Muja, M., Lowe, D.G.: Fast approximate nearest neighbors with automatic algorithm configuration. In: ICCVTA (2009)
Rousseau, F., Habas, P.A., Studholme, C.: A supervised patch-based approach for human brain labeling. Transactions on Medical Imaging (2011)
Sabuncu, M.R., et al.: A generative model for image segmentation based on label fusion. Transactions on Medical Imaging (2010)
Wachinger, C., et al.: On the importance of location and features for the patch-based segmentation of parotid glands. MIDAS Journal - IGART (2014)
Zoran, D., Weiss, Y.: From learning models of natural image patches to whole image restoration. In: International Conference on Computer Vision (2011)
Zoran, D., Weiss, Y.: Natural images, gaussian mixtures and dead leaves. In: Neural Information Processing Systems (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Chen, G.H., Shah, D., Golland, P. (2015). A Latent Source Model for Patch-Based Image Segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9351. Springer, Cham. https://doi.org/10.1007/978-3-319-24574-4_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-24574-4_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24573-7
Online ISBN: 978-3-319-24574-4
eBook Packages: Computer ScienceComputer Science (R0)