A fast algorithm for active contours and curvature estimation
CVGIP: Image Understanding
Boundary Finding with Parametrically Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic NURBS with geometric constraints for interactive sculpting
ACM Transactions on Graphics (TOG) - Special issue on interactive sculpting
Active shape models—their training and application
Computer Vision and Image Understanding
Medical Image Analysis: Progress over Two Decades and the Challenges Ahead
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deformable Organisms for Automatic Medical Image Analysis
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
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This paper presents a deformable model based approach for automated segmentation of kidneys from tree dimensional (3D) abdominal CT images. Since the quality of an input image is very poor and noisy due to the large slice thickness, we use a deformable model represented by NURBS surface, which uses not only the gray level appearance of the target but also statistical information of the shape. A shape feature vector is defined to evaluate geometric character of the surface and its statistical information is incorporated into the deformable model through an energy formulation for deformation. Principal curvature on the model surface, which is invariant to rotation and translation, is adopted as a component of the vector. Furthermore, automated positioning procedure of an initial model is presented in this paper. We applied the proposed method to the 33 abdominal CT images whose slice thickness is 10mm and evaluated the effectiveness of the proposing method.