Fractal Dimension of Fundoscopical Retinal Images for Diagnosing of Diabetic Retinopathy

  • B. Dhananjay
  • M. Srinivas
  • D. Suman
  • M. Malini
  • J. SivaramanEmail author
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 3)


The present work applied different image processing techniques like green component image, background estimation and image skeletonization on the subject’s fundus images. Statistical methods like fractal dimensions, neighbourhood concept was used to distinguish between normal and abnormal fundus images in subjects (n = 45). The results show that, in normal fundus images the vein structures were clearly visible, while in the fundoscopic positive images, the vein structures were totally absent. In fundoscopic negative images the visible vein structures are observed to be thick and coiled up. No significant changes were found in Fractal Dimension (FD) values among the subjects. Neighbourhood pixels (NP) values were found to be 45 ± 0.74 (mean ± S.D.) for normal subjects, 34 ± 1.01 for fundoscopic positive subjects, 20.47 ± 0.49 for fundoscopic negative subjects. The results of this work validated the skeletonized images and support the strength of diagnosis with the help of accurate figures.


Fractal dimensions Fundoscopy Fundus image Neighbourhood concept Skeletonised images 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • B. Dhananjay
    • 1
  • M. Srinivas
    • 2
  • D. Suman
    • 2
  • M. Malini
    • 2
  • J. Sivaraman
    • 1
    Email author
  1. 1.Medical Electronics & Instrumentation GroupNational Institute of Technology RourkelaRourkelaIndia
  2. 2.University College of EngineeringOsmania UniversityHyderabadIndia

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