Research on Improved SURF Breast Registration Algorithm in Multi-Mode MRI

Jiying, 李积英 Li; Yonghong, 杨永红 Yang; Qiang, 温强 Wen; Yan, 王燕 Wang; Yilin, 杨宜林 Yang

doi:10.3788/lop57.121010

Cited by 2 publications

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“…The accuracy of the matching pairs of feature points is the basis of correct registration. Therefore, the Random Sample Consensus algorithm (RANSAC) [21,22] is here used to eliminate the incorrect matching pairs based on the remaining feature points. Figure 5 shows the screening results based on Figure 4, which still includes relatively numerous feature points.…”

Section: Screening Of Matching Pairs Of Feature Pointsmentioning

confidence: 99%

“…First, the brain tissue segmentation was carried out to obtain different tissues including the white matter, gray matter, and cerebrospinal fluid, followed by performing the morphological edge detection of different brain tissues. The key feature points were then extracted from each brain tissue and the original brain image and consisted of a set of feature points, followed by removing mismatched pairs of feature points by a random sample consensus (RANSAC) algorithm [21,22]. Finally, the registration parameters can be obtained depending on the remaining matched pairs of feature points, and be used for the final brain image registration.…”

Section: Introductionmentioning

confidence: 99%

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Methodological Research on Image Registration Based on Human Brain Tissue In Vivo

Nan

Zhang

2023

Electronics

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As one of the critical steps in brain imaging analysis and processing, brain image registration plays a significant role. In this paper, we proposed a technique of human brain image registration based on tissue morphology in vivo to address the problems of previous image registration. First, different feature points were extracted and combined, including those at the boundary of different brain tissues and those of the maximum or minimum from the original image. Second, feature points were screened through eliminating their wrong matching pairs between moving image and reference image. Finally, the remaining matching pairs of feature points were used to generate the model parameters of spatial transformation, with which the brain image registration can be finished by combining interpolation techniques. Results showed that compared with the Surf, Demons, and Sift algorithms, the proposed method can perform better not only for four quantitative indicators (mean square differences, normalized cross correlation, normalized mutual information and mutual information) but also in spatial location, size, appearance contour, and registration details. The findings may suggest that the proposed method will be of great value for brain image reconstruction, fusion, and statistical comparison analysis.

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Section: Screening Of Matching Pairs Of Feature Pointsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%