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Proceedings Paper

New medical image segmentation algorithm based on Gaussian-mixture model
Author(s): Hua Yang; Jie Tian; Jia Yang
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Paper Abstract

In this paper, we propose a probability model based method where the image pixels' features are modeled as Gaussian- Mixture distribution. Then the segmentation problem can be reduced to the estimation of the parameters of the Gaussian- Mixture model. Traditional method of estimating the parameters is EM (expectation maximization). But it has the drawbacks of heavy computational load and sensitivity to initialization. IN this paper, we get the initial parameters for EM by two steps: 1) Anisotropic diffusion is applied to original image. The histogram of the image after anisotropic diffusion is expected to have distinct peaks and valleys to detect, while in original image the modes may be overlapped to detect accurately. 2) A histogram analysis method is presented to deal with parameter initialization. Then the EM algorithm is applied to estimate the parameters iteratively. Due to the good initialization, the heavy computational load and instability of EM are overcome.

Paper Details

Date Published: 11 October 2000
PDF: 5 pages
Proc. SPIE 4224, Biomedical Photonics and Optoelectronic Imaging, (11 October 2000); doi: 10.1117/12.403921
Show Author Affiliations
Hua Yang, Institute of Automation (China)
Jie Tian, Institute of Automation (China)
Jia Yang, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 4224:
Biomedical Photonics and Optoelectronic Imaging
Hong Liu; Qingming Luo, Editor(s)

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