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We present a unified approach to Expectation-Maximization (EM) and Level Set image segmentation that combines the advantages of the two algorithms via a geometric prior that encourages local classification similarity. Compared to level sets, our method increases the information returned by providing probabilistic soft decisions, is easily extensible to multiple regions, and does not require solving...
We present a methodology for characterizing small size heterogeneous textures that are hard to analyze in general due to the paucity of pixels and textural heterogeneity. The methodology overcomes the limitation for a large class of heterogeneous textures that exhibit onion layer type textural variation, where we may assume that within a layer the behavior is homogeneous, but may vary from layer to...
We apply texture image analysis to automated classification of stem cell nuclei, based on the observation that chromatin in human embryonic stem cells becomes more granular during differentiation. Using known probability models for texture multiresolution decompositions, we derive likelihood ratio test statistics. We also derive the probability density functions of these non-Gaussian statistics and...
We present a non-invasive, non-destructive automatable image-based methodology for classifying human embryonic stem cell (hESC) colonies. In contrast to differentiated colonies, pluripotent colonies contain homogeneous tight textures, thus allowing a statistical analysis of the coefficients obtained from a wavelet based texture decomposition to discriminate between the colonies. Similarly, borders...
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