• DocumentCode
    478269
  • Title

    Multimodal Medical Image Elastic Registration Using Mean Shift

  • Author

    Yang, Xuan ; Pei, Jihong

  • Author_Institution
    Coll. of Inf. Eng., Shenzhen Univ., Shenzhen
  • Volume
    4
  • fYear
    2008
  • fDate
    18-20 Oct. 2008
  • Firstpage
    177
  • Lastpage
    181
  • Abstract
    Estimating landmarks corresponding plays a key role in landmark-based multimodal image registration. In this paper, a novel landmarks corresponding estimation in multimodal image registration using mean shift algorithm is proposed. Edge feature potential is defined to transform images from intensity feature space to edge structure feature space. Image corner points are detected as candidate landmarks. Mean shift iterations are adopted to search the most probable corresponding point positions in the two images based on the edge structure feature. Moreover, mutual information between two local regions is computed to eliminate mis-matching landmarks. Finally, the source images are transformed by compact support thin-plate spline interpolation. Experiments show that the precision in location of corresponding landmarks is satisfied. The proposed technique is feasible and rapid shown in the experiments of various multi-modal medical images registration.
  • Keywords
    biomedical MRI; image registration; interpolation; iterative methods; medical image processing; splines (mathematics); compact support thin-plate spline interpolation; edge feature potential; iterations; mean shift algorithm; multimodal medical image elastic registration; Biomedical engineering; Biomedical imaging; Educational institutions; Image edge detection; Image registration; Iterative algorithms; Kernel; Mutual information; Probability distribution; Shape; Elastic Transformation; Mean Shift; Multimodal Image Registration;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation, 2008. ICNC '08. Fourth International Conference on
  • Conference_Location
    Jinan
  • Print_ISBN
    978-0-7695-3304-9
  • Type

    conf

  • DOI
    10.1109/ICNC.2008.159
  • Filename
    4667272