A modified medical image registration

Mao, Pan; Jiang, Jianjun; Rong, Qiusheng; Zhang, Fen; Hui-can, Zhou; Nie, Fangyan

doi:10.1007/s11042-012-1180-3

Cited by 9 publications

(3 citation statements)

References 30 publications

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“…Medical image registration, as the prerequisite for medical image fusion, signifies that the spatial geometric transformation is applied to match two or more images produced by various imaging devices, and enables the pixels (voxels) representing the same structure to achieve the space correspondence [1][2]. In the past several decades, medical image registration methods have achieved rapid development, and researchers all over the world have proposed many practical and effective technologies.…”

Section: Introductionmentioning

confidence: 99%

“…The iterative closest point algorithm (ICP), as a feature-based registration method pioneered by Besl and Mckay [8] in 1992, is one well known approach and extensively used for the registration on point sets. It can align the point sets without any prior and explicit segmentation or other preprocessing of object features, which makes it to be prevalent and very well adapted for point set registration [2].…”

Section: Introductionmentioning

confidence: 99%

“…So far, the initial parameters for registration of IMICP have been obtained. Second, the binarization images are acquired after the B-spline gradient operator (BSGO) [2] is employed to probe the edges of the reference and floating images, which will be selected as the feature points in the process of IMICP, namely, the reference and floating point sets for registration are acquired. Finally, ICP is applied to explore the optimal transformation parameters.…”

Section: Introductionmentioning

confidence: 99%

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Medical Image Registration Based on Inertia Matrix and Iterative Closest Point

Mao¹,

Jiang²,

Rong³

et al. 2015

IJSIP

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The closest iterative point algorithm (ICP) is widely used in medical image registration. But there exist some problems in the following aspects. First, due to its heavily computational load, it has a time-consuming process and a low registration efficiency. Second, due to the fact that it heavily depends on whether the initial rotation and translation matrices of the floating point set can be exactly extracted, it often traps in the local optimum and even fails to register images. In addition, due to the complexity of medical images, it is difficult to automatically extract the salutary feature points. In this paper, by computing the coordinate inertia matrices of the reference and floating images, the rotation angles are obtained and referred to as the initial rotation parameters of ICP for image registration. The edges of the reference and floating images are detected by the edge convolution kernel so-called B-spline gradient operator (BSGO) and then the binarization images involving the feature points are acquired. The experimental results reveal that, this proposed method has a fairly simple implementation, a low computational load, a fast registration and good alignment accuracy. Also, It can efficiently avoid trapping in the local optimum and cater to both mono-modality and multi-modality image registrations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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