Hybrid Particle Swarm Optimization and Its Application to Multimodal 3D Medical Image Registration
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 use...
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| Veröffentlicht in: | Computational Intelligence and Neuroscience 2012-01, Vol.2012 (2012), p.196-202 |
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| container_title | Computational Intelligence and Neuroscience |
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| creator | Lin, Chen-Lun Mimori, Aya Chen, Yen-Wei |
| description | 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. |
| doi_str_mv | 10.1155/2012/561406 |
| format | Article |
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| subjects | Algorithms Computational biology Diagnostic imaging Humans Image Processing, Computer-Assisted - methods Imaging, Three-Dimensional - methods Mathematical optimization Models, Genetic |
| title | Hybrid Particle Swarm Optimization and Its Application to Multimodal 3D Medical Image Registration |
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