Evidence map›Paper›PMID 34367316›Full record

ArticleComputational and mathematical methods in medicine2021

A Medical Image Registration Method Based on Progressive Images.

Qian Zheng, Qiang Wang, Xiaojuan Ba, Shan Liu, Jiaofen Nan, Shizheng Zhang

Abstract read
In one paragraph

Article in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Qian ZhengZhengzhou University of Light Industry, Zhengzhou, Henan 450002, China.
Qiang WangZhengzhou University of Light Industry, Zhengzhou, Henan 450002, China.
Xiaojuan BaZhengzhou University of Light Industry, Zhengzhou, Henan 450002, China.
Shan LiuZhengzhou University of Light Industry, Zhengzhou, Henan 450002, China.
Jiaofen NanZhengzhou University of Light Industry, Zhengzhou, Henan 450002, China.ORCID https://orcid.org/0000-0001-6730-6977
Shizheng ZhangZhengzhou University of Light Industry, Zhengzhou, Henan 450002, China.ORCID https://orcid.org/0000-0003-1535-2618

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMedical image registration is an essential task for medical image analysis in various applications. In this work, we develop a coarse-to-fine medical image registration method based on progressive images and SURF algorithm (PI-SURF) for higher registration accuracy.

methodsAs a first step, the reference image and the floating image are fused to generate multiple progressive images. Thereafter, the floating image and progressive image are registered to get the coarse registration result based on the SURF algorithm. For further improvement, the coarse registration result and the reference image are registered to perform fine image registration. The appropriate progressive image has been investigated by experiments. The mutual information (MI), normal mutual information (NMI), normalized correlation coefficient (NCC), and mean square difference (MSD) similarity metrics are used to demonstrate the potential of the PI-SURF method.

resultsFor the unimodal and multimodal registration, the PI-SURF method achieves the best results compared with the mutual information method, Demons method, Demons+B-spline method, and SURF method. The MI, NMI, and NCC of PI-SURF are improved by 15.5%, 1.31%, and 7.3%, respectively, while MSD decreased by 13.2% for the multimodal registration compared with the optimal result of the state-of-the-art methods.

conclusionsThe extensive experiments show that the proposed PI-SURF method achieves higher quality of registration.

Indexed as

AlgorithmsBrainComputational BiologyHumansImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedMultimodal ImagingSoftware Design

Identifiers

PMID34367316
PMCPMC8337131

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