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Icon for: Benjamin Guan


University of California at Riverside


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Automatic Human Embryonic Stem Cell Detection by Spatial Information and Mixture of Gaussians

Human Embryonic Stem Cells (HESCs) are often used in the biological assay to study the effects of chemical agents in the human body. However, detection of HESC is often a challenge in phase contrast images. To improve the accuracy of cell colony detections, we combine spatial information and the outcome of mixture of Gaussians model. While mixture of Gaussians generates reasonable labels for various regions of HESC images, it lacks some spatial details and connectivity. Sets of spatially consistent candidate labeling are generated by median filtering the image at different scales followed by thresholding. An optimal combination of filter scale and threshold which maximizes correlation coefficient between spatial information and the mixture of Gaussians output is obtained. The paper validates the method for various HESC images.