Local Mean Multiphase Segmentation with HMMF Models
This paper presents two similar multiphase segmentation methods for recovery of segments in complex weakly structured images, with local and global bias fields, because they can occur in some X-ray CT imaging modalities. Derived from the Mumford-Shah functional, the proposed methods assume a fixed number of classes. They use local image average as discriminative features. Region labels are modelled by Hidden Markov Measure Field Models. The resulting problems are solved by straightforward alternate minimisation methods, particularly simple in the case of quadratic regularisation of the labels. We demonstrate the proposed methods’ capabilities on synthetic data using classical segmentation criteria as well as criteria specific to geoscience. We also present a few examples using real data.
KeywordsTikhonov Regularisation Bias Field Kernel Parameter Descent Step Classical Segmentation
J. Hansen and F. Lauze acknowledge funding from the Innovation Fund Denmark and Mærsk Oil and Gas A/S, for the P\(^3\) Project. We thank Henning Osholm Sørensen for giving us access to the experimental tomography data.
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