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Improving Retinal Image Quality Using Registration with an SIFT Algorithm in Quasi-Confocal Line Scanning Ophthalmoscope

  • Yi HeEmail author
  • Yuanyuan Wang
  • Ling Wei
  • Xiqi Li
  • Jinsheng Yang
  • Yudong Zhang
Chapter
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 977)

Abstract

When high-magnification images are taken with a quasi-confocal line scanning ophthalmoscope (LSO), the quality of images always suffers from Gaussian noise, and the signal to noise ratio (SNR) is very low for a safer laser illumination. In addition, motions of the retina severely affect the stabilization of the real-time video resulting in significant distortions or warped images. We describe a scale-invariant feature transform (SIFT) algorithm to automatically abstract corner points with subpixel resolution and match these points in sequential images using an affine transformation. Once n images are aligned and averaged, the noise level drops by a factor of \( \sqrt{n} \) and the image quality is improved. The improvement of image quality is independent of the acquisition method as long as the image is not warped, particularly severely during confocal scanning. Consequently, even better results can be expected by implementing this image processing technique on higher resolution images.

Keywords

Confocal microscopy Feature extraction Image matching SIFT 

Notes

Acknowledgements

This work is supported by the National Science Foundation of China (Grant NO. 61605210), the National Instrumentation Program (NIP, Grant No. 2012YQ120080), the Jiangsu Province Science Fund for Distinguished Young Scholars (Grant NO. BK20060010), the Frontier Science research project of the Chinese Academy of Sciences (Grant NO. QYZDB-SSW-JSC03), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant NO. XDB02060000), the National Key Research and Development Program of China (2016YFC0102500), and the Zhejiang Province Technology Program (Grant No. 2013C33170).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yi He
    • 1
    Email author
  • Yuanyuan Wang
    • 1
    • 2
    • 3
  • Ling Wei
    • 1
  • Xiqi Li
    • 1
  • Jinsheng Yang
    • 1
  • Yudong Zhang
    • 1
  1. 1.The Key Laboratory on Adaptive Optics, Chinese Academy of SciencesChengduChina
  2. 2.Graduate School of Chinese Academy of SciencesBeijingChina
  3. 3.Wenzhou Medical UniversityWenzhouChina

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