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Stitching method for panoramic nailfold images based on capillary contour enhancement
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  • Jianan Lin,
  • Hao Yin,
  • Yanxiong Wu,
  • Jiaxiong Luo,
  • Qianyao Ye,
  • Bin Zhou,
  • Mugui Xie,
  • Cong Ye,
  • Junzhao Liang,
  • Xiaosong Li,
  • Wei Bin,
  • Zhimin Yang
Jianan Lin
Foshan University School of Physics and Optoelectronic Engineering
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Hao Yin
Foshan University School of Physics and Optoelectronic Engineering
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Yanxiong Wu
Foshan University School of Physics and Optoelectronic Engineering

Corresponding Author:[email protected]

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Jiaxiong Luo
Foshan University School of Physics and Optoelectronic Engineering
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Qianyao Ye
Foshan University School of Physics and Optoelectronic Engineering
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Bin Zhou
Foshan University School of Physics and Optoelectronic Engineering
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Mugui Xie
Foshan University School of Physics and Optoelectronic Engineering
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Cong Ye
Foshan University School of Physics and Optoelectronic Engineering
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Junzhao Liang
Foshan University School of Physics and Optoelectronic Engineering
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Xiaosong Li
Foshan University School of Physics and Optoelectronic Engineering
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Wei Bin
The Second Affiliated Hospital of Guangzhou University of Chinese Medicine
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Zhimin Yang
The Second Affiliated Hospital of Guangzhou University of Chinese Medicine
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Abstract

Nailfold capillaroscopy is an important means of monitoring human health. Panoramic nailfold images improve the efficiency and accuracy of examinations. However, the acquisition of panoramic nailfold images is seldom studied and the problem manifests of few matching feature points when image stitching is used for such images. Therefore, this paper presents a method for panoramic nailfold image stitching based on vascular contour enhancement, which first solves the problem of few matching feature points by pre-processing the image with contrast-constrained adaptive histogram equalization (CLAHE), bilateral filtering (BF), and sharpening algorithms. The panoramic images of the nailfold blood vessels are then successfully stitched using the fast robust feature (SURF), fast library of approximate nearest neighbors (FLANN) and random sample agreement (RANSAC) algorithms. The experimental results show that the panoramic image stitched by this paper’s algorithm has a field of view width of 7.43 mm, which improves the efficiency and accuracy of diagnosis.
17 Mar 2024Submitted to Journal of Biophotonics
17 Mar 2024Submission Checks Completed
17 Mar 2024Assigned to Editor
09 Apr 2024Review(s) Completed, Editorial Evaluation Pending
09 Apr 2024Editorial Decision: Revise Major
11 Jun 2024Review(s) Completed, Editorial Evaluation Pending
12 Jun 2024Editorial Decision: Accept