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Journal of Digital Imaging

, Volume 31, Issue 2, pp 224–234 | Cite as

Statistical Geometrical Features for Microaneurysm Detection

  • Arati Manjaramkar
  • Manesh Kokare
Article

Abstract

Automated microaneurysm (MA) detection is still an open challenge due to its small size and similarity with blood vessels. In this paper, we present a novel method which is simple, efficient, and real-time for segmenting and detecting MA in color fundus images (CFI). To do this, a novel set of features based on statistics of geometrical properties of connected regions, that can easily discriminate lesion and non-lesion pixels are used. For large-scale evaluation proposed method is validated on DIARETDB1, ROC, STARE, and MESSIDOR dataset. It proves robust with respect to different image characteristics and camera settings. The best performance was achieved on per-image evaluation on DIARETDB1 dataset with sensitivity of 88.09 at 92.65% specificity which is quite encouraging for clinical use.

Keywords

Diabetic retinopathy Mass screening Red lesion Microaneurysms Digital fundus images Object rule-based classification 

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Copyright information

© Society for Imaging Informatics in Medicine 2017

Authors and Affiliations

  1. 1.Department of Information TechnologySGGS Institute of Engineering & TechnologyNandedIndia
  2. 2.Department of Electronics & TelecommunicationSGGS Institute of Engineering & TechnologyNandedIndia

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