Takehiro Kajihara1, Takuya Funatomi1, Hiroyuki Kubo1, Takahito Aoto2,
Haruyuki Makishima3, Shigehito Yamada3, Yasuhiro Mukaigawa1
1 Nara Institute of Science and Technology (NAIST), Japan, 2 National Institute of Informatics, Japan, 3 Kyoto University, Japan

Appeared in ACPR '17.


In this paper, we describe feature-based non-rigid registration of histological serial section images. Our method represents non-rigid deformation by blending the rigid transformations estimated in the local region around a control point. This approach can efficiently represent non-rigid deformation with a smaller number of control points than conventional methods that interpolate displacement, such as free-form deformation (FFD). A feature-based approach is adopted to extract the control points and robustly estimate the local rigid transformation at each control point. By blending the rigid transformations, the displacement at each pixel is computed as a transformation field. The experimental results demonstrate that the proposed method is effective for achieving non-rigid registration efficiently and robustly for histological serial section images.


  • T. Kajihara, T. Funatomi, H. Kubo, T. Aoto, H. Makishima, S. Yamada, Y. Mukaigawa
    Feature-Based Non-Rigid Registration of Serial Section Images by Blending Rigid Transformations,
    In The 4th Asian Conference on Pattern Recognition (ACPR 2017)
  • IEEE Xplore (TBA)
  • Preprint (PDF)