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Accurate Image Analysis of the Retina Using Hessian Matrix and Binarisation of Thresholded Entropy with Application of Texture Mapping

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  • Xiaoxia Yin
  • Brian W-H Ng
  • Jing He
  • Yanchun Zhang
  • Derek Abbott

Abstract

In this paper, we demonstrate a comprehensive method for segmenting the retinal vasculature in camera images of the fundus. This is of interest in the area of diagnostics for eye diseases that affect the blood vessels in the eye. In a departure from other state-of-the-art methods, vessels are first pre-grouped together with graph partitioning, using a spectral clustering technique based on morphological features. Local curvature is estimated over the whole image using eigenvalues of Hessian matrix in order to enhance the vessels, which appear as ridges in images of the retina. The result is combined with a binarized image, obtained using a threshold that maximizes entropy, to extract the retinal vessels from the background. Speckle type noise is reduced by applying a connectivity constraint on the extracted curvature based enhanced image. This constraint is varied over the image according to each region's predominant blood vessel size. The resultant image exhibits the central light reflex of retinal arteries and veins, which prevents the segmentation of whole vessels. To address this, the earlier entropy-based binarization technique is repeated on the original image, but crucially, with a different threshold to incorporate the central reflex vessels. The final segmentation is achieved by combining the segmented vessels with and without central light reflex. We carry out our approach on DRIVE and REVIEW, two publicly available collections of retinal images for research purposes. The obtained results are compared with state-of-the-art methods in the literature using metrics such as sensitivity (true positive rate), selectivity (false positive rate) and accuracy rates for the DRIVE images and measured vessel widths for the REVIEW images. Our approach out-performs the methods in the literature.

Suggested Citation

  • Xiaoxia Yin & Brian W-H Ng & Jing He & Yanchun Zhang & Derek Abbott, 2014. "Accurate Image Analysis of the Retina Using Hessian Matrix and Binarisation of Thresholded Entropy with Application of Texture Mapping," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-17, April.
  • Handle: RePEc:plo:pone00:0095943
    DOI: 10.1371/journal.pone.0095943
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    Cited by:

    1. Amelia Carolina Sparavigna, 2014. "GIMP and Wavelets for Medical Image Processing: Enhancing Images of the Fundus of the Eye," International Journal of Sciences, Office ijSciences, vol. 3(08), pages 35-47, August.
    2. Guo Zhang & Jinzhao Lin & Enling Cao & Yu Pang & Weiwei Sun, 2022. "A Medical Endoscope Image Enhancement Method Based on Improved Weighted Guided Filtering," Mathematics, MDPI, vol. 10(9), pages 1-17, April.
    3. Wendeson S Oliveira & Joyce Vitor Teixeira & Tsang Ing Ren & George D C Cavalcanti & Jan Sijbers, 2016. "Unsupervised Retinal Vessel Segmentation Using Combined Filters," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-21, February.

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