An approach to multimodal biomedical image registration utilizing particle swarm optimization

Biomedical image registration, or geometric alignment of two-dimensional and/or three-dimensional (3D) image data, is becoming increasingly important in diagnosis, treatment planning, functional studies, computer-guided therapies, and in biomedical research. Registration based on intensity values us...

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Veröffentlicht in:IEEE transactions on evolutionary computation Jg. 8; H. 3; S. 289 - 301
Hauptverfasser: Wachowiak, M.P., Smolikova, R., Yufeng Zheng, Zurada, J.M., Elmaghraby, A.S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.06.2004
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Applied sciences
Artificial intelligence
Biomedical computing
Biomedical imaging
Computed tomography
Computer science
Computer science; control theory; systems
Evolutionary
Exact sciences and technology
Image registration
Local optimization
Magnetic resonance imaging
Medical treatment
Optimization
Optimization techniques
Particle swarm optimization
Pattern recognition. Digital image processing. Computational geometry
Positron emission tomography
Similarity
Strategy
Studies
Three dimensional
Ultrasonic imaging
Image registration
Evolutionary algorithm
Global optimum
Neural network
local optimization
particle swarm optimization
Ground truth
global optimization
Evolutionary strategies
Image matching
Medical imagery
Tridimensional image
Swarm intelligence
Diagnosis
Mutual information
Biomedical engineering
ISSN:1089-778X, 1941-0026
Online-Zugang:Volltext
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Zusammenfassung:Biomedical image registration, or geometric alignment of two-dimensional and/or three-dimensional (3D) image data, is becoming increasingly important in diagnosis, treatment planning, functional studies, computer-guided therapies, and in biomedical research. Registration based on intensity values usually requires optimization of some similarity metric between the images. Local optimization techniques frequently fail because functions of these metrics with respect to transformation parameters are generally nonconvex and irregular and, therefore, global methods are often required. In this paper, a new evolutionary approach, particle swarm optimization, is adapted for single-slice 3D-to-3D biomedical image registration. A new hybrid particle swarm technique is proposed that incorporates initial user guidance. Multimodal registrations with initial orientations far from the ground truth were performed on three volumes from different modalities. Results of optimizing the normalized mutual information similarity metric were compared with various evolutionary strategies. The hybrid particle swarm technique produced more accurate registrations than the evolutionary strategies in many cases, with comparable convergence. These results demonstrate that particle swarm approaches, along with evolutionary techniques and local methods, are useful in image registration, and emphasize the need for hybrid approaches for difficult registration problems.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2004.826068