An expectation-maximization approach to joint curve evolution for medical image segmentation

This paper proposes a new Expectation-Maximization curve evolution algorithm for medical image segmentation. Traditional level set algorithms perform poorly when image information is incomplete, missing or some objects are corrupted. In such cases, statistical model-based segmentation methods are widely used since they allow object shape variations subject to shape prior constraints to overcome the incomplete or noisy information. Although matching robustly in dealing with noisy and low contrast images, the shape parameters are estimated intractably through the Maximum A Posterior (MAP) framework by using incomplete image features. In this paper, we present a statistical shape-based joint curve evolution algorithm for image segmentation based on the assumption that using hidden features of the image as missing data can simplify the estimation problem and help improve the matching performance. In our method, these hidden features are designed to be the local voxel labeling data determined based on the intensity distribution of the image and priori anatomical knowledge. Using an Expectation-Maximization formulation, both the hidden features and the object shapes can be extracted. In addition, this EM-based algorithm is applied to the joint parameter and non-parameter shape model for more accurate segmentation. Comparative results on segmenting putamen and caudate shapes in MR brain images confirm both robustness and accuracy of the proposed curve evolution algorithm.