GGM classifier with multi-scale line detectors for retinal vessel segmentation

  • Mohammad A. U. Khan
  • Tariq M. KhanEmail author
  • Syed S. Naqvi
  • M. Aurangzeb Khan
Original Paper


Persistent changes in the diameter of retinal blood vessels may indicate some chronic eye diseases. Computer-assisted change observation attempts may become challenging due to the emergence of interfering pathologies around blood vessels in retinal fundus images. The end result is lower sensitivity to thin vessels for certain computerized detection methods. Quite recently, multi-scale line detection method proved to be worthy for improved sensitivity toward lower-caliber vessels detection. This happens largely due to its adaptive property that responds more to the longevity patterns than width of a given vessel. However, the method suffers from the lack of a better aggregation process for individual line detectors. This paper investigates a scenario that introduces a supervised generalized Gaussian mixture classifier as a robust solution for the aggregate process. The classifier is built with class-conditional probability density functions as a logistic function of linear mixtures. To boost the classifier’s performance, the weighted scale images are modeled as Gaussian mixtures. The classifier is trained with weighted images modeled on a Gaussian mixture. The net effect is increased sensitivity for small vessels. The classifier’s performance has been tested with three commonly available data sets: DRIVE, SATRE, and CHASE_DB1. The results of the proposed method (with an accuracy of 96%, 96.1% and 95% on DRIVE, STARE, and CHASE_DB1, respectively) demonstrate its competitiveness against the state-of-the-art methods and its reliability for vessel segmentation.


Image segmentation Vessel segmentation Retinal images Diabetic retinopathy 



The authors would like to thank Effat University in Jeddah, Saudi Arabia, for funding the research reported in this paper through the Research and Consultancy Institute.

Supplementary material

11760_2019_1515_MOESM1_ESM.docx (21 kb)
Supplementary material 1 (docx 20 KB)


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Biometric LabEffat University Research and Consultancy InstituteJeddahSaudi Arabia
  2. 2.Department of Electrical and Computer EngineeringCOMSATS University IslamabadIslamabadPakistan
  3. 3.School of Computing and Communication, Infolab21Lancaster UniversityLancasterUK

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