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Towards Automated Eye Diagnosis: An Improved Retinal Vessel Segmentation Framework Using Ensemble Block Matching 3D

Khuram Naveed1, Faizan Daud2, Hussain Ahmad Madni1

  • 1Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad 45550, Pakistan.

Diagnostics (Basel, Switzerland)
|January 15, 2021
PubMed
Summary

This study introduces an unsupervised method using an ensemble Block Matching 3D (BM3D) speckle filter to accurately segment retinal blood vessels in noisy fundus images for eye disease detection.

Keywords:
Block Matching 3D (BM3D)Diabetic Retinopathy (DR)Vascular Endothelial Growth Factor (VEGF)retinaspeckle noise

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Image Processing

Background:

  • Accurate segmentation of retinal blood vessels is crucial for automated detection of vision-threatening eye diseases.
  • Noisy and poorly illuminated fundus images present significant challenges for detecting fine vessels.
  • Common noise types include additive and multiplicative (speckle) noise inherent to fundus imaging systems.

Purpose of the Study:

  • To develop an efficient unsupervised strategy for retinal vessel segmentation in noisy fundus images.
  • To improve the accuracy of eye disease classification by enhancing vessel detection.
  • To address and mitigate artifacts introduced by noise reduction techniques.

Main Methods:

  • Proposed an ensemble Block Matching 3D (BM3D) speckle filter for noise removal in fundus images.
  • Developed a strategy to suppress checkerboard artifacts generated by the BM3D-speckle filter.
  • Implemented an improved unsupervised segmentation approach utilizing the enhanced image data.

Main Results:

  • Achieved high sensitivity rates: 82.88% (DRIVE), 81.41% (STARE), and 82.03% (CHASE_DB1).
  • Boosted accuracy to 95.41% (DRIVE), 95.70% (STARE), and 95.61% (CHASE_DB1).
  • Demonstrated superior performance on images with pathologies compared to state-of-the-art methods.

Conclusions:

  • The proposed ensemble BM3D filtering and unsupervised segmentation effectively enhances fine vessel detection in noisy fundus images.
  • The method offers improved accuracy and robustness for automated eye disease classification.
  • This approach shows significant potential for clinical applications in ophthalmology.