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tomography angiography (CTA) images. Output data (3-D model) form the proposed method can be used for measurement of aortic shape and dimensions. Knowledge of aortic shape and size is very important in planning of minimally invasive procedure that is for selection of appropriate stent graft device for treatment of AAA. The technique is based on a 3-D deformable model and utilizes the level-set algorithm for implementation of the method. The method performs 3-D segmentation of CTA images and extracts a 3-D model of aortic wall. Once the 3-D model of aortic wall is available it is easy to perform all required measurements for appropriate stent graft selection. The method proposed in this paper uses the level-set algorithm for deformable models, instead of the classical snake algorithm. The main advantage of the level set algorithm is that it enables easy segmentation of complex structures, surpassing most of the drawbacks of the classical approach. We have extended the deformable model to incorporate the a priori knowledge about the shape of the AAA. This helps direct the evolution of the deformable model to correctly segment the aorta. The algorithm has been implemented in IDL and C languages. Experiments have been performed using real patient CTA images and have shown good results.

In this work, two novel methods for semi-automatic image segmentation of medical images are presented. The developed techniques are applied to the problem of medical image segmentation. The two developed approaches are compared and the results are discussed. The rst method is based on interactive boundary detection where the user is assisted by the computer in selection of border of the desired anatomical region. The user performs segmentation by selecting a sparse series of points along the desired region border. The optimal path (border segment) is calculated based on the minimization of the energy function. The second method is based on interactive region growing process. The algorithm computes image features along the user-de ned path and the computed features are used in the region-growing process. The region selection process is repeated until the user is satis ed with the look of the segmented region. Those two methods are compared to two semi automatic edge and region based image segmentation techniques.

Image segmentation is an important and challenging problem in image analysis. Segmentation of moving objects in image sequences is even more difficult and computationally expensive. In this work we propose a technique for spatio -temporal segmentation of medical sequences based on K-mean clustering in the feature vector space. The motivation for spatio-temporalsegmentation approach comes from the fact that motion is a useful clue for object segme ntation. Two- dimensional feature vector has been used for clustering in the feature space. In this paper we apply the proposed technique to segmentation of cardiac images. The first feature used in this particular application is image brightness, which reveals the structure of interest in the image. The second feature is the Euclidean norm of the optical flow vector. The third feature is the three-dimensional optical flow vector, which consists of computed motion in all three dimensions. The optical flow itself is computed using Horn-Schunck algorithm. The fourth feature is the mean brightness of the input image in a local neighborhood. By applying the clustering algorithm it is possible to detect moving object in the image sequence. The experiment has been conducted using a sequence of ECG-gated magnetic resonance (MR) images of a beating heart taken as in time so in the space.

In this paper a method for three-dimensional (3-D) semi-automatic segmentation of volumes of medical images is described. The method is semi-automatic in the sense that, in the initial phase, the user assistance is required for manual segmentation of a certain number of slices (cross-sections) of the volume. In the second phase, the algorithm for automatic segmentation is started. The segmentation algorithm is based on the active contour approach. A semi 3-D active contour algorithm is used in the sense that additional inter-slice forces are introduced in order to constrain the obtained solution. The energy function which is minimized is modified to exploit information provided by the manual segmentation of some of the slices performed by the user. The experiments have been performed using computed tomography (CT) scans of the abdominal region of the human body. In particular, CT images of abdominal aortic aneurysms have been segmented to determine the location of aorta. The experiments have shown the feasibility of the approach.