Model-Based Segmentation of Multimodal Images
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.
Keywordsdata fusion multimodal images model-based segmentation Gaussian mixture maximum likelihood EM algorithm
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- 1.Al Momani, B., Morrow, P., McClean, S.: Knowledge based semi-supervised satellite image classification. In: Proc. ISSPA 2007 (2007)Google Scholar
- 3.Boudraa, A.-O., Bentabet, L., Salzenstein, F., Guillon, L.: Dempster-Shafer’s basic probability assignment based on fuzzy membership functions. Electronic Letters on Computer Vision and Image Analysis 4(1), 1–9 (2004)Google Scholar