Segmentation for Medical Image Using a Statistical Initial Process and a Level Set Method

Abstract

In this paper, we present a segmentation method for medical image based on the statistical clustering technique and the level set method. The segmentation method consists of a pre-processing stage for initialization and the final segmentation stage. First, in the initial segmentation stage, we adopt the Gaussian mixture model (GMM) and the Deterministic Annealing Expectation Maximization (DAEM) algorithm to compute the posterior probabilities for each pixels belonging to some region. And then we usually segment an image to assign each pixel to the object with maximum posterior probability. Next, we use the level set method to achieve the final segmentation. By using the level set method with a new defined speed function, the segmentation accuracy can be improved while making the boundaries of each object much smoother. This function combines the alignment term, which makes a level set as close as possible to a boundary of object, the minimal variance term, which best separates the interior and exterior in the contour and the mean curvature term, which makes a segmented boundary become less sensitive to noise. And we also use the Fast Matching Method for re-initialization that can reduce the computing time largely. The experimental results show that our proposed method can segment exactly the synthetic and CT images.