Affiliations: Beijing Engineering Research Center of Mixed Reality
and Advanced Display, School of Optics and Electronics, Beijing Institute of
Technology, Beijing, China | School of Life Science, Beijing Institute of
Technology, Beijing, China
Note: [] Corresponding author: Qin Li, School of Life Science, Beijing
Institute of Technology, Beijing 100081, China. E-mail: [email protected]
Abstract: This study proposes a novel hierarchical pyramid strategy for 3D
registration of multimodality medical images. The surfaces of the source and
target volume data are first extracted, and the surface point clouds are then
aligned roughly using convex hull matching. The convex hull matching
registration procedure could align images with large-scale transformations. The
original images are divided into blocks and the corresponding blocks in the two
images are registered by affine and non-rigid registration procedures. The
sub-blocks are iteratively smoothed by the Gaussian kernel with different sizes
during the registration procedure. The registration result of the large kernel
is taken as the input of the small kernel registration. The fine registration
of the two volume data sets is achieved by iteratively increasing the number of
blocks, in which increase in similarity measure is taken as a criterion for
acceptation of each iteration level. Results demonstrate the effectiveness and
robustness of the proposed method in registering the multiple modalities of
medical images.
