Chapter

Computer Analysis of Images and Patterns

Volume 4673 of the series Lecture Notes in Computer Science pp 604-611

Model-Based Segmentation of Multimodal Images

  • Xin HongAffiliated withSchool of Computing and Information Engineering, University of Ulster, Cromore Road, Coleraine, BT52 1SA, Northern Ireland
  • , Sally McCleanAffiliated withSchool of Computing and Information Engineering, University of Ulster, Cromore Road, Coleraine, BT52 1SA, Northern Ireland
  • , Bryan ScotneyAffiliated withSchool of Computing and Information Engineering, University of Ulster, Cromore Road, Coleraine, BT52 1SA, Northern Ireland
  • , Philip MorrowAffiliated withSchool of Computing and Information Engineering, University of Ulster, Cromore Road, Coleraine, BT52 1SA, Northern Ireland

Abstract

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.

Keywords

data fusion multimodal images model-based segmentation Gaussian mixture maximum likelihood EM algorithm