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In this paper we present a multiphase level set model for histology image segmentation. Global K-means energy is weighted by a Gaussian kernel to cluster image pixels in local neighborhoods. We group these local clusters into different source classes using a multiphase level set model to produce the final segmentation results. Our energy functional is formulated as the integral of local K-means energies across the entire image. Unlike current local region-based active contour methods that update the pixel neighborhood distributions (e.g. local intensity means) in each iteration, we estimate these statistics before contour evolution for more efficient computation. In addition, such pre-derived local intensity distributions enable a model without initial contour selection, i.e., the level set functions can be initialized with a random constant instead of a distance map. In this way our model ameliorates the initialization sensitivity problem of most active contour methods. Experiments on the National Cancer Institute ALTS histology images show the improved performance of our approach over standard multithresholding and K-means clustering, as well as state-of-the-art active contours, mean shift clustering, and Markov random field-based pixel labeling methods.