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Research of CT / MRI Tumor Image Registration Based on Least Square Support Vector Machines

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Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues (ICIC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5226))

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Abstract

In view of the characteristics of the local nonlinear distortion about medical CT and MRI tumor image registration, the method of Least Square Support Vector Machines (LS-SVM) was used to register the images. The verge point of tumor was marked using vector machine weights in this algorithm, and the eigenvalues of tumor images were obtained. The difference of corresponding feature points between CT and MRI tumor image was eliminated adopting the least square algorithm, which not only can effectively remove geometric deformation of the images, but also can be adaptive correct the errors caused by the positioning of feature points. Finally, we had experimented on the Matlab platform, the results show that the algorithm has higher registration accuracy and can meet medical tumor registration requirements. It also play guidance role in image fusion and tumor targeted therapy, which has important application in clinical medicine.

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Authors and Affiliations

  1. School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300130, China

    Zeqing Yang, Libing Liu, Weiling Liu & Weidong Yang

Authors
  1. Zeqing Yang
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  2. Libing Liu
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  3. Weiling Liu
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  4. Weidong Yang
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Editor information

Editors and Affiliations

  1. Department of Automation, University of Science and Technology of China, 230026, Hefei, Anhui, China

    De-Shuang Huang

  2. Applied Computational Intelligence Laboratory, P.O. Box

    Donald C. Wunsch II

  3. Arlington, USA

    Daniel S. Levine

  4. Graduate School of Electrical Engineering, University of Ulsan, Korea, San 29, Mugeo-Dong, 680 - 749, Nam-Ku, Ulsan, Korea

    Kang-Hyun Jo

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© 2008 Springer-Verlag Berlin Heidelberg

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Yang, Z., Liu, L., Liu, W., Yang, W. (2008). Research of CT / MRI Tumor Image Registration Based on Least Square Support Vector Machines. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2008. Lecture Notes in Computer Science, vol 5226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87442-3_133

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  • DOI: https://doi.org/10.1007/978-3-540-87442-3_133

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87440-9

  • Online ISBN: 978-3-540-87442-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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