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Abstract #5086

Automatic Segmentation of the Prostate in MR Images Using a Prior Knowledge of Shape, Geometry and Gradient Information

Yujin Jang1, Helen Hong1, Hak Jong Lee2, Sung Il Hwang2

1Division of Multimedia Engineering, Seoul Women's University, Seoul, Korea, Republic of; 2Department of Radiology, Seoul National University Hospital of Bundang, Seongnam-si, Korea, Republic of

To segment the prostate in MR images with a poor tissue contrast and shape variation, we propose a reliable and reproducible segmentation method using a prior knowledge of shape, geometry and gradient information. The prostate surface is generated by 3D active shape model using adaptive density profile and multiresolution technique. To prevent holes from occurring by the convergence of the surface shape on the local optima, the hole is eliminated by 3D shape correction using geometry information. In the apex of the prostate which has a large anatomical variation, the boundary is refined by 2D contour correction using gradient information.