Hong, Xin, McClean, Sally, Scotney, Bryan and Morrow, Philip (2007) Model-Based Segmentation of Multimodal Images. In: Computer Analysis of Images and Patterns. Springer, pp. 604-611. ISBN 978-3-540-74271-5 [Book section]
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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.
|Item Type:||Book section|
|Faculties and Schools:||Faculty of Computing & Engineering|
Faculty of Computing & Engineering > School of Computing and Information Engineering
|Research Institutes and Groups:||Computer Science Research Institute|
Computer Science Research Institute > Information and Communication Engineering
|Deposited By:||Professor Philip Morrow|
|Deposited On:||04 May 2010 09:16|
|Last Modified:||15 Jun 2011 10:08|
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