compendium

M. T. McCann, D. G. Mixon, M. C. Fickus, C. A. Castro, J. A. Ozolek, and J. Kovačević. Images as occlusions of random textures: A framework for segmentation. IEEE Trans. Image Process., 23(5):2033-2046, May 2014.

[ pdf | @ IEEE Xplore | bibtex]

We propose a new mathematical and algorithmic framework for unsupervised image segmentation, which is a critical step in a wide variety of image processing applications. We have found that most existing segmentation methods are not successful on histopathology images, which prompted us to investigate segmentation of a broader class of images, namely those without clear edges between the regions to be segmented. We model these images as occlusions of random images, which we call textures, and show that local histograms are a useful tool for segmenting them. Based on our theoretical results, we describe a flexible segmentation framework that draws on existing work on non-negative matrix factorization and image deconvolution. Results on synthetic texture mosaics and real histology images show the promise of the method.

The archive contains the code for ORTSEG as well as the datasets used in the paper.

[download]

This work is licensed under a Creative Commons GNU General Public License. To view a copy of this license, visit http://creativecommons.org/licenses/GPL/2.0. If you use this code or any part thereof in your research or publication, please also include a reference to this paper. Thank you!

Windows 7 Service Pack 1 (64-bit), MATLAB 2013a

Linux (64-bit), MATLAB 2011a

additional results

For many more results (numbers and images), please see the paper referenced above. Here we provide some additional results not present in the paper.

Table A. Results in terms of variation of information mean and standard deviation. A value of 0 indicates perfect agreement with the ground truth.

contact

For more information or to report bugs contact jelenak at cmu dot edu.