Journal of Northeastern University Natural Science ›› 2019, Vol. 40 ›› Issue (2): 158-163.DOI: 10.12068/j.issn.1005-3026.2019.02.002

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Retinal Image Segmentation Based on Multifractal Detrended Fluctuation Analysis

ZHANG Shi, SHE Li-huang, WANG Ya-fan, SU Ting   

  1. School of Computer Science & Engineering, Northeastern University, Shenyang 110169, China.
  • Received:2017-11-14 Revised:2017-11-14 Online:2019-02-15 Published:2019-02-12
  • Contact: SHE Li-huang
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Abstract: A method based on multifractal detrended fluctuation analysis(DFA), which is adaptable for unsteady images, is introduced to segment retina images. In this method, histogram equalization is first used to precondition images for the enhancement of blood vessels, and then, the generalized Hurst index of images is calculated by multifractal DFA. The blood vessel index is used to segment the blood vessels. Finally, morphological postprocessing is used to get the final blood vessel images. Experiments based on DIARETDE0 and DIARETDE1 databases show that the proposed method has better integrity and connectivity in the treatment of retinal lesions, and it can extract the main body of blood vessels better, which has a good clinical value.

Key words: retina, segmentation of blood vessel, multifractal, detrended fluctuation analysis, lesion image

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