Contrast normalization steps for increased sensitivity of a retinal image segmentation method

Toufique Ahmed Soomro*, Mohammad A.U. Khan, Junbin Gao, Tariq M. Khan, Manoranjan Paul

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)


Retinal vessel segmentation plays a major role in the detection of many eye diseases, and it is required to implement an automated algorithm for analyzing the progress of eye diseases. A variety of automated segmentation methods have been presented but almost all studies to date showed weakness in their low sensitivity toward narrow low-contrast vessels. A new segmentation method is proposed to address the issue of low sensitivity, by including modules such as principal component analysis-based color-to-gray conversion, scale normalization factors for improved narrow vessel detection, anisotropic diffusion filtering with an adequate stopping rule, and edge pixel-based hysteresis threshold. The impact of these additional steps is assessed on publicly available databases like DRIVE and STARE. For the case of DRIVE database, the sensitivity is raised from 73 to 75%, while maintaining the accuracy of 96.5%, and found to provide evidence of improved sensitivity.

Original languageEnglish
Pages (from-to)1509–1517
Number of pages9
JournalSignal, Image and Video Processing
Issue number8
Early online date24 May 2017
Publication statusPublished - Nov 2017


  • Retinal images
  • Scale normalization
  • PCA
  • Hysteresis threshold


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