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

Orazio Gambino, Roberto Gallea, Edoardo Ardizzone, Roberto Pirrone, Orazio Gambino, Roberto Gallea, Roberto Pirrone, Edoardo Ardizzone

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


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.
Original languageEnglish
Number of pages17
JournalPattern Recognition
VolumeIn press
Publication statusPublished - 2013

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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