A Novel Model-Based Approach for Medical Image Segmentation Using Spatially Constrained Inverted Dirichlet Mixture Models

In this paper, we present a novel statistical approach to medical image segmentation. This approach is based on finite mixture models with spatial smoothness constrains. The main advantages of the proposed approach can be summarized as follows. Firstly, the proposed model is based on inverted Dirichlet mixture models, which have demonstrated better performance in modeling positive data (e.g., images) than Gaussian mixture models. Secondly, we integrate spatial relationships between pixels with the inverted Dirichlet mixture model, which makes it more robust against noise and image contrast levels. Finally, we develop a variational Bayes method to learn the proposed model, such that the model parameters and model complexity (i.e., the number of mixture components) can be estimated simultaneously in a unified framework. The performance of the proposed approach in medical image segmentation is compared with some state-of-the-art segmentation approaches through various numerical experiments on both simulated and real medical images.