Hybrid particle swarm optimization and its application to multimodal 3D medical image registration

Authors:
Chen-Lun Lin;Aya Mimori;Yen-Wei Chen
Affiliations:
College of Information and Science, Ritsumeikan University, Shiga, Japan;College of Information and Science, Ritsumeikan University, Shiga, Japan;College of Information and Science, Ritsumeikan University, Shiga, Japan
Venue:
Computational Intelligence and Neuroscience - Special issue on Computational Intelligence in Biomedical Science and Engineering
Year:
2012

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Abstract

In the area of medical image analysis, 3D multimodality image registration is an important issue. In the processing of registration, an optimization approach has been applied to estimate the transformation of the reference image and target image. Some local optimization techniques are frequently used, such as the gradient descent method. However, these methods need a good initial value in order to avoid the local resolution. In this paper, we present a new improved global optimization approach named hybrid particle swarm optimization (HPSO) for medical image registration, which includes two concepts of genetic algorithms-- subpopulation and crossover.