Exploiting user labels with generalized distance transforms random field level sets
ISBI（2010）
摘要
We present an approach for exploiting user labels with random field level sets in image segmentation. A sparse set of user labels is propagated to the rest of the image by computing a generalized distance transform which takes into account image intensity information. The region-based level set formulation is modified to use random field level sets whose range is restricted to the probability values. These two ideas are combined in a single level set functional. Improved results are shown on a liver segmentation task.
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关键词
random field level sets,level set,user label sparse set,image intensity information,probability value,user labels,image segmentation,segmentation,single level,generalized distance transform,generalized distance,user label,single level set functional,liver segmentation task,user interaction,improved result,liver,exploiting user label,semi-automatic,medical imaging,account image intensity information,medical image processing,random field level set,probability,region-based level set formulation,random field,labeling,biomedical imaging,cost function,distance transform,automation
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