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.