Estimating the Number of Components of Mixture Models for Medical Image
医学图像的混合模型成份数估计

Estimating the number of mixture models is the key part of clustering analysis and density estimation for medical image. In order to overcome the over-fitting problem of the method of information criteria, we proposed a new estimation method which is based on a feature function of Gaussian mixture models(GMMs). First, the feature function of medial image was defined on the GMMs. Second, constructed a new criterion with the feature function to estimate the number of components of the mixture models. At last, we proposed an algorithm to compute the new criterion. Our new criterion uses a parameter to adjust the value of log-feature function and to keep the balance effect of the penalized function. Experiments on the simulate data and real CT image show our criterion can determine a more reasonable number of components K than others information model selection criteria and avoid the over-fitting problem of the medical image.