Segmentation Of Mid-Brain Structures Based On Active Contours

Based on a discrete dynamic contour model, a method for segmentation of brain structures like thalamus and red nucleus from magnetic resonance images (MRI) is developed. A new method for solving common problems in extracting the discontinuous boundary of a structure from a low contrast image is presented. External and internal forces deform the dynamic contour model. Internal forces are obtained from local geometry of the contour, which consist of vertices and edges, connecting adjacent vertices. The image data and desired image features such as image energy are utilized to obtain external forces. The problem of low contrast image data and unclear edges in the image energy is overcome by the proposed algorithm that uses several methods like thresholding, unsupervised clustering methods such as fuzzy C-means (FCM), edge-finding filters like Prewitt, and morphological operations. We also present a method for generating an initial contour for the model from the image data automatically. Evaluation and validation of the methods are conducted by comparing radiologist and automatic segmentation results. The average of the similarity between segmentation results is 0.8 for the left and right thalami indicating excellent performance of the new method. Additional noise and intensity inhomogeneity changed the evaluation results slightly illustrating the robustness of the proposed method to the image noise and intensity inhomogeneity.