Retinal microaneurysms detection using local convergence index features

Research output: Contribution to journalArticleAcademicpeer-review

32 Citations (Scopus)

Abstract

Retinal microaneurysms (MAs) are the earliest clinical sign of diabetic retinopathy disease. Detection of MAs is crucial for the early diagnosis of diabetic retinopathy and prevention of blindness. In this paper, a novel and reliable method for automatic detection of MAs in retinal images is proposed. In the first stage of the proposed method, several preliminary microaneurysm candidates are extracted using a gradient weighting technique and an iterative thresholding approach. In the next stage, in addition to intensity and shape descriptors, a new set of features based on local convergence index filters is extracted for each candidate. Finally, the collective set of features is fed to a hybrid sampling/boosting classifier to discriminate the MAs from non-MAs candidates. The method is evaluated on images with different resolutions and modalities (color and scanning laser ophthalmoscope) using six publicly available data sets including the retinopathy online challenges (ROC) data set. The proposed method achieves an average sensitivity score of 0.471 on the ROC data set outperforming state-of-the-art approaches in an extensive comparison. The experimental results on the other five data sets demonstrate the effectiveness and robustness of the proposed MAs detection method regardless of different image resolutions and modalities.

Original languageEnglish
Pages (from-to)3300-3315
Number of pages16
JournalIEEE Transactions on Image Processing
Volume27
Issue number7
DOIs
Publication statusPublished - Jul 2018

Keywords

  • Computer-aided diagnosis
  • diabetic retinopathy
  • local convergence filter
  • microaneurysm detection
  • retina
  • Algorithms
  • Diabetic Retinopathy/diagnostic imaging
  • Humans
  • Microaneurysm/diagnostic imaging
  • Image Interpretation, Computer-Assisted/methods

Fingerprint Dive into the research topics of 'Retinal microaneurysms detection using local convergence index features'. Together they form a unique fingerprint.

Cite this