New medical image segmentation algorithm based on Gaussian-mixture model
Hua Ming Yang, Jie Tian, Jia Yang · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2000
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.