ArticleBioMed research international2017
A Registration Method Based on Contour Point Cloud for 3D Whole-Body PET and CT Images.
Article in BioMed research international, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- BigReg: an efficient registration pipeline for high-resolution X-ray and light-sheet fluorescence microscopy.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Head and neck multi-organ auto-segmentation on CT images aided by synthetic MRI.Medical physics · 2020Article
- A Novel Fusion Framework Based on Adaptive PCNN in NSCT Domain for Whole-Body PET and CT Images.Computational and mathematical methods in medicine · 2017Article
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5 authors.
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Abstract
The PET and CT fusion image, combining the anatomical and functional information, has important clinical meaning. An effective registration of PET and CT images is the basis of image fusion. This paper presents a multithread registration method based on contour point cloud for 3D whole-body PET and CT images. Firstly, a geometric feature-based segmentation (GFS) method and a dynamic threshold denoising (DTD) method are creatively proposed to preprocess CT and PET images, respectively. Next, a new automated trunk slices extraction method is presented for extracting feature point clouds. Finally, the multithread Iterative Closet Point is adopted to drive an affine transform. We compare our method with a multiresolution registration method based on Mattes Mutual Information on 13 pairs (246~286 slices per pair) of 3D whole-body PET and CT data. Experimental results demonstrate the registration effectiveness of our method with lower negative normalization correlation (NC = -0.933) on feature images and less Euclidean distance error (ED = 2.826) on landmark points, outperforming the source data (NC = -0.496, ED = 25.847) and the compared method (NC = -0.614, ED = 16.085). Moreover, our method is about ten times faster than the compared one.
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