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A Generative Model for Image Segmentation Based on Label Fusion
2010
IEEE Transactions on Medical Imaging
The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Abstract-We propose a nonparametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms rely on pairwise registrations between the test image and individual training images. The training labels are then transferred to the test image and fused to compute the final
doi:10.1109/tmi.2010.2050897
pmid:20562040
pmcid:PMC3268159
fatcat:nt4bfbcc3ff5rjwhirdfviwqrq