Liver segmentation from low contrast open MR scans using k-means clustering and graph-cuts
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
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Liver segmentation from scans of the abdominal area is an important step in several diagnostic processes. CT scans of the abdominal area contain several organs in close proximity exhibiting similar image characteristics. In this paper, we present preliminary results on an algorithm that uses Markov Random Fields to obtain an initial contour of the liver. Gradient vector fields (GVF) and active contours are used to refine the initial estimate and segment the liver. Tests are reported on 13 clinical cases using a similarity metric that combines area and space.