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A novel image registration algorithm that uses two kinds of information is presented: One kind is the shape information of an object and the other kind is the intensity information of a voxel and its neighborhoods consisting of the object. We, first, segment the medical volume data using the Markov random field model and the ICM algorithm and extract the surface region of the object from a segmented...
An automatic segmentation method for medical images that uses a geometric deformable model is presented, and the segmented results are visualized with the help of a modified marching cubes algorithm. The geometric deformable model is based on evolution theory and the level set method. In particular, the level set method utilizes a new derived speed function to improve the segmentation performance...
We present a level set framework for medical image segmentation using a new defined speed function. 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 smoothing term, which makes a segmented boundary become less sensitive to noise. The use of...
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