Segmentation of medical image based on mean shift and deterministic annealing EM algorithm
Myungeun Lee, Soo-Hyung Kim, Wanhyun Cho, Xin Bo Zhao · 2008
In this paper, we use the mean shift procedure to determine the number of components in a mixture model and to detect their modes of each mixture component. Next, we have adopted the Gaussian mixture model to represent the probability distribution of feature vectors. A deterministic annealing expectation maximization algorithm is used to estimate the parameters of the GMM. The experimental results show that the mean shift part of the proposed algorithm is efficient to determine the number of components and modes of each component in mixture models. And it shows that the DAEM part provides a global optimal solution for the parameter estimation in a mixture model.