Knowledge discovery by probabilistic clustering of distributed databases
Data & Knowledge Engineering
Bayesian inference for multiband image segmentation via model-based cluster trees
Image and Vision Computing
MR contrast synthesis for lesion segmentation
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
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This paper proposes a model-based method for intensity-based segmentation of images acquired from multiple modalities. Pixel intensity within a modality image is represented by a univariate Gaussian distribution mixture in which the components correspond to different segments. The proposed Multi-Modality Expectation-Maximization (MMEM) algorithm then estimates the probability of each segment along with parameters of the Gaussian distributions for each modality by maximum likelihood using the Expectation-Maximization (EM) algorithm. Multimodal images are simultaneously involved in the iterative parameter estimation step. Pixel classes are determined by maximising a posteriori probability contributed from all multimodal images. Experimental results show that the method exploits and fuses complementary information of multimodal images. Segmentation can thus be more precise than when using single-modality images.