Images as occlusions of textures: A framework for segmentation

Michael T. McCann, Dustin G. Mixon, Matthew C. Fickus, Carlos A. Castro, John A. Ozolek, Jelena Kovacevic

Research output: Contribution to journalArticlepeer-review


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 nonnegative matrix factorization and image deconvolution. Results on synthetic texture mosaics and real histology images show the promise of the method.

Original languageEnglish (US)
Article number6748922
Pages (from-to)2033-2046
Number of pages14
JournalIEEE Transactions on Image Processing
Issue number5
StatePublished - May 2014


  • deconvolution
  • image segmentation
  • local histograms
  • non-negative matrix factorization
  • occlusion models
  • texture

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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