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A robust technique based on VLM and Frangi filter for retinal vessel extraction and denoising

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  • Khan Bahadar Khan
  • Amir A Khaliq
  • Abdul Jalil
  • Muhammad Shahid

Abstract

The exploration of retinal vessel structure is colossally important on account of numerous diseases including stroke, Diabetic Retinopathy (DR) and coronary heart diseases, which can damage the retinal vessel structure. The retinal vascular network is very hard to be extracted due to its spreading and diminishing geometry and contrast variation in an image. The proposed technique consists of unique parallel processes for denoising and extraction of blood vessels in retinal images. In the preprocessing section, an adaptive histogram equalization enhances dissimilarity between the vessels and the background and morphological top-hat filters are employed to eliminate macula and optic disc, etc. To remove local noise, the difference of images is computed from the top-hat filtered image and the high-boost filtered image. Frangi filter is applied at multi scale for the enhancement of vessels possessing diverse widths. Segmentation is performed by using improved Otsu thresholding on the high-boost filtered image and Frangi’s enhanced image, separately. In the postprocessing steps, a Vessel Location Map (VLM) is extracted by using raster to vector transformation. Postprocessing steps are employed in a novel way to reject misclassified vessel pixels. The final segmented image is obtained by using pixel-by-pixel AND operation between VLM and Frangi output image. The method has been rigorously analyzed on the STARE, DRIVE and HRF datasets.

Suggested Citation

  • Khan Bahadar Khan & Amir A Khaliq & Abdul Jalil & Muhammad Shahid, 2018. "A robust technique based on VLM and Frangi filter for retinal vessel extraction and denoising," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-22, February.
  • Handle: RePEc:plo:pone00:0192203
    DOI: 10.1371/journal.pone.0192203
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    References listed on IDEAS

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    1. Lei Wang & Huimao Zhang & Kan He & Yan Chang & Xiaodong Yang, 2015. "Active Contours Driven by Multi-Feature Gaussian Distribution Fitting Energy with Application to Vessel Segmentation," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-18, November.
    2. Peter Bankhead & C Norman Scholfield & J Graham McGeown & Tim M Curtis, 2012. "Fast Retinal Vessel Detection and Measurement Using Wavelets and Edge Location Refinement," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-12, March.
    3. Peishan Dai & Hanyuan Luo & Hanwei Sheng & Yali Zhao & Ling Li & Jing Wu & Yuqian Zhao & Kenji Suzuki, 2015. "A New Approach to Segment Both Main and Peripheral Retinal Vessels Based on Gray-Voting and Gaussian Mixture Model," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-22, June.
    4. 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.
    5. Khan BahadarKhan & Amir A Khaliq & Muhammad Shahid, 2016. "A Morphological Hessian Based Approach for Retinal Blood Vessels Segmentation and Denoising Using Region Based Otsu Thresholding," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-19, July.
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