This paper presents a deformable model based approach for segmenting three dimensional (3D) anatomic volumes (such as human organs) in medical images. The deformable model is represented by non-uniform rational B-splines surface and a priori knowledge of an organs shape, such as average and variation of the shape, is introduced as energy function for the model fitting process. To describe the shape, we used principal curvature and its statistical information which are invariant for rotation and translation. The proposed method is applied to the segmentation problems of the kidney in 3D CT images and the promising results are shown.
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