3D segmentation of medical image using the geometric active contour model

Accurate segmentation is a key issue in medical image analysis. Segmentation aids the tasks of object representation and structure quantification. Currently, many methods are introduced in 3D dice medical image segmentation. Conventionary, each slice image was segmented in 2D space and reconstructed in 3D space for 3D analysis, so it required more time and effort. Thus, this paper presents a modified geometric active contour model in 3D domain for reducing user's efforts, shortening the processing time and detecting an exact edge of object. This method uses a new speed function based on the level set approach developed by Sethian and the fast marching method for its fast implementation. The main idea of the level set methodology is to embed the propagating interface as the particular level set of a higher dimensional function(hypersurface) flowing along gradient force and curvature force. This technique retains the attractive feature which is it's topological and geometric flexibility of the contour in recovering objects with complex shapes and unknown topologies. And the other feature is that this method is easily applied to the 3D domain easily. The fast marching method is a fast algorithm using the fact that the interface in the level set method is flowing in one direction. We apply the proposed model to various 2D, 3D images such as synthetic image, CT and MR Angiogram. From the results, the presented model confirms that it works very naturally and efficiently with the desired features of 2D and 3D medical images.