Unsupervised Texture Segmentation with Nonparametric Neighborhood Statistics

1. Unsupervised Texture Segmentation with Nonparametric Neighborhood Statistics Suyash P. Awate, Tolga Tasdizen, and Ross T. Whitaker Scientific Computing and Imaging (SCI) Institute, School of Computing, University of Utah 1. Texture 2. Segmenting Images having Textures • Regularity, at multiple scales. • Statistically characterized as a Julesz ensemble: set of all images belonging to a certain texture • Shift-invariant Markov statistics • Finding boundaries between different textures in images • Applications: distinguishing cell types in medical images, scene analysis in computer vision, etc. 3. Adaptive Texture Modeling: Learning Markov Statistics from Input Data 4. Nonparametric Markov-Statistics Learning • Parzen-window density estimate: Markov neighborhoods from image: converges asymptotically • Nonparametric-MRF parameter estimation: use pseudo maximum-likelihood: converges asymptotically • Intensities in each neighborhood  point in the feature space • Estimate Markov probabilities  density estimation in this feature space True density 5. Information-Theoretic Coherency Measure 6. Level-Set Optimization via Fast Threshold-Dynamics • Catch-22 • Knowing Markov statistics of all textures, we can produce correct segmentation. • But to learn statistics of each texture, we need the correct segmentation. • Iterative algorithm • Start with a randomly generated segmentation (labels). • Estimate Markov statistics based on the current segmentation (labels). • Update each pixel label, based on current estimation of Markov statistics, to increase coherency (Be careful: explicitly account for the change in Markov statistics after the segmentation update) • Gaussian-smooth the label image. • Maximize functional dependence, or mutual information, between the segmented texture classes and their statistics. • Minimize weighted sum of entropies of classes. • Entropy: goodness measure for the segmentation. • Incorrect segmentations  higher entropies for classes  more randomness in the class References • Suyash P. Awate, Tolga Tasdizen, Ross T. Whitaker. Unsupervised Texture Segmentation with Nonparametric Neighborhood Statistics. To Appear, European Conference on Computer Vision (ECCV) 2006. • Suyash P. Awate, Tolga Tasdizen, Norman Foster, Ross T. Whitaker. Adaptive, Nonparametric Markov Modeling for Unsupervised, MRI Brain-Tissue Classification. Invited paper from MICCAI 2005: Submitted to Medical Image Analysis (MedIA) Journal, 2006. Acknowledgements Grants: NSF EIA0313268, NSF CAREER CCR0092065, NIH EB005832-01, NIH EY002576, NIH EY015128. Thanks to Prof. Robert Marc and Prof. Bryan Jones from the John A.Moran Eye Center (University of Utah) for providing the electron-microscopy retinal images.