Validation of a parallel genetic algorithm for image reconstruction from projections |
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| Affiliation: | 1. School of Earth Sciences, Yunnan University, Kunming 650500, PR China;2. State Key Laboratory of Lithospheric Evolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, PR China;3. State Key Laboratory for Continental Dynamics, Department of Geology, Northwest University, Xian 710069, PR China;4. Centre for Earth Sciences, Indian Institute of Science, Bengaluru 560012, India;5. Innovation Academy for Earth Sciences, Chinese Academy of Sciences, Beijing 100029, PR China;6. Key Laboratory of Computation Geodynamics, University of Chinese Academy of Sciences, Beijing 100049, PR China;7. Faculty of Science, University Brunei Darussalam, Gadong BE1410, Brunei Darussalam;8. College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, PR China;1. College of Petroleum Engineering, Liaoning Shihua University, Fushun 113001, China;2. William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, OH 43210, USA;1. Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, No. 129, Sec. 3, Sanmin Rd., Taichung, Taiwan, ROC;2. Department of Electrical Engineering, National Chung Hsing University, No. 250 Kuo Kuang Rd., Taichung, Taiwan, ROC;1. Taiwan International Graduate Program in Molecular Medicine, National Yang Ming Chiao Tung University and Academia Sinica, Taipei, Taiwan;2. Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei 112, Taiwan;3. Department of Clinical Neurobiology at the Medical Faculty of Heidelberg University and German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany |
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| Abstract: | The problem of accurate image reconstruction from projections has repeatedly arisen over the last decades in a large number of scientific, medical and technical fields. Reconstruction algorithms use data from electron microscopes to reconstruct molecular structures or X-ray projection data to compute medical images. Usually, the applied projection data are noisy and therefore iterative algorithms are used to solve numerically a number of equations. Theory and empirical results demonstrate that genetic algorithms (GA) can accurately solve a broad class of problems, especially if noisy input data are used. GA are based on the evolution of random tries by individuals, and therefore the time to find an appropriate solution is rather long. In this work, we use a parallel approach using JavaSpaces to speed up a genetic reconstruction algorithm. |
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