This paper presents a feature-based solution for 3D registration of CT and MRI images of a human knee. It facilitates constructing high-quality models with clear outlining of bone tissues and detailed illustration of soft tissues. The model will be used for analysing the effect of posterior cruciate ligament and anterior cruciate ligament deficiency. The solution consists of preprocessing, feature extraction, transformation parameter estimation and resampling, and blending. In preprocessing, we propose partial preserving and iterative neighbour comparing filtering to help segment bone tissues from MRI images without having to construct a statistical model. Through analysing the characteristics of knee images, tibia and femur are selected as the features and the algorithm for effectively extracting them is described. To estimate transformation parameters, we propose a method based on the statistical information of projected feature images, including translating according to the project feature image centroids and calculating the rotation angle by searching and mapping boundary points. We transform the MRI image and then blend it with the CT image by taking the maximum intensity of every two corresponding voxels from the two images. At the end of the paper, the registration result is evaluated by computing the Pearson product-moment correlation coefficient of the binarised features and the accuracy is confirmed.
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This paper is sponsored by Fundamental Research Funds for Chinese Central Universities (No. R12JB00020) and National Natural Science Foundation of China (No. 50975013). Jingjie Zheng and Zhenyan Ji contributed equally to this work.
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Zheng, J., Ji, Z., Yu, K. et al. A feature-based solution for 3D registration of CT and MRI images of human knee. SIViP 9, 1815–1824 (2015). https://doi.org/10.1007/s11760-014-0660-5
- Feature-based image registration
- Magnetic resonance imaging
- Computed tomography
- Human knee