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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...
Most statistical models for applications rely on the Gaussian assumption. Yet, in many realistic situations, the underlying variation or uncertainty is essentially non-Gaussian. In detection problems, for instance, the Gaussian assumption leads to false alarms in cases where the tail is a fatter one, such as in the case of the Laplace density function. In classification problems, the Gaussian model...
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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