Registration for Multi-Modal and Multi-Phase Images of Proximal Femur Based on Voxel Mutual Information
1 Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China
2 Institute of Orthopedics in PLA General Hospital, Beijing 100853, China
Abstract：Image analysis is a common method for evaluation of proximal femur osteoporosis and femoral head necrosis. This method evaluates patient’s condition based on images of proximal femur with different modes and phases. However, because the images are generated in different systems and the positions of the patient relative to systems are different, the positions of anatomical points in different images are not one-to-one consistent. We need to align points of images with different modes and phases before we can research on interesting area. To solve the problem, we proposed a solution to get spatial rigid transformation through image pre-processing, voxels segmentation of femur based on dualthreshold combined with Bayes decision rule, and femurs registration based on normalized mutual information. The error of CT-MR registration and CT-CT registration were below 4 mm and 2 mm respectively. Using the matrix transfer relationship, rigid transformation between any two images based on multiphase CT-CT registration matrix was obtained. On this basis, image fusion of any two images, point-to-point analysis and quantitative evaluation of osseous and blood supply condition were processed. The interesting area in images with different modes and phases through the solution was compared.
［1］Blumenfeld J, Studholme C, CarballidoGamio J, et al. Threedimensional image registration of MR proximal femur images for the analysis of trabecular bone parameters［J］. Medical Physics, 2008, 35(10): 4630-4639.
［2］Li Wenjun, Sode M, Saeed I, et al.Automated registration of hip and spine for longitudinal QCT studies: integration with 3D densitometric and structural analysis［J］. Bone, 2006,38:273-279.
［3］Li Wenjun, Kezele I, Collins DL, et al. Voxelbased modeling and quantification of the proximal femur using intersubject registration of quantitative CT images［J］. Bone, 2007, 41(5): 888-895.
［4］Baena FR, Hawke T, Jakopec M. A bounded iterative closest point method for minimally invasive registration of the femur［J］. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 2013,〖STHZ〗227〖STBZ〗(10):1135-1144.
［5］张建国，杨庆铭. 股骨头坏死病灶清除计算机辅助系统配准模块和导航模块的建立［J］. 中国组织工程研究, 2011, 15(26): 4768-4772.
［6］Serra J. Introduction to mathematical morphology［J］. Computer vision, Graphics, and Image Processing, 1986, 35(3): 283-305.
［7］Cheng Yuanzhi, Zhou Shengjun, Wang Yadong, et al. Automatic segmentation technique for acetabulum and femoral head in CT images［J］. Pattern Recognition, 2013,46(11): 2969-2984.
［8］Maes F, Collignon A, Vandermeulen D, et al. Multimodality image registration by maximization of mutual information［J］. IEEE Transactions on Medical Imaging, 1997, 16(2): 187-198.
［9］Studholme C, Hill DLG, Hawkes DJ. An overlap invariant entropy measure of 3D medical image alignment［J］. Pattern Recognition, 1999, 32(1): 71-86.
［10］Powell MJD. On search directions for minimization algorithms［J］. Mathematical Programming, 1973, 4(1): 193-201.
［11］Canny J.A computational approach to edge detection［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986 (6): 679-698.
［12］Xu Chenyang, Prince JL. Snakes, shapes, and gradient vector flow［J］. IEEE Transactions on Image Processing, 1998,7(3): 359-369.
