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.

Gallea, R., Ardizzone, E., Pirrone, R., Gambino, O. (2013). Three-dimensional Fuzzy Kernel Regression framework for registration of medical volume data. PATTERN RECOGNITION, In press [10.1016/j.patcog.2013.03.025].

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

GALLEA, Roberto;ARDIZZONE, Edoardo;PIRRONE, Roberto;GAMBINO, Orazio
2013

Abstract

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.
http://www.sciencedirect.com/science/article/pii/S0031320313001556
Gallea, R., Ardizzone, E., Pirrone, R., Gambino, O. (2013). Three-dimensional Fuzzy Kernel Regression framework for registration of medical volume data. PATTERN RECOGNITION, In press [10.1016/j.patcog.2013.03.025].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/76606
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