Three-dimensional Fuzzy Kernel Regression framework for registration of medical volume data

• We register medical volumes using our “Fuzzy Kernel Regression” framework, which is formally described.
• We describe three applications instantiating such framework of increasing complexity and performance.
• The framework is validated taking both quantitative and qualitative assessments of the applications.

In this work a general framework for non-rigid 3D medical image registration is presented. It relies on two pattern recognition techniques: kernel regression and fuzzy c-means clustering. The paper provides theoretic explanation, details the framework, and illustrates its application to implement three registration algorithms for CT/MR volumes as well as single 2D slices. The first two algorithms are landmark-based approaches, while the third one is an area-based technique. The last approach is based on iterative hierarchical volume subdivision, and maximization of mutual information. Moreover, a high performance Nvidia CUDA based implementation of the algorithm is presented.The framework and its applications were evaluated with a number of tests, which show that the proposed approaches achieve valuable results when compared with state-of-the-art techniques.Additional assessment was taken by expert radiologists, providing performance feedback from the final user perspective.