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Statistical Geometrical Features for Microaneurysm Detection

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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.

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Authors and Affiliations

  1. Department of Information Technology, SGGS Institute of Engineering & Technology, Nanded, Maharashtra, 431606, India

    Arati Manjaramkar

  2. Department of Electronics & Telecommunication, SGGS Institute of Engineering & Technology, Nanded, Maharashtra, 431606, India

    Manesh Kokare

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  1. Arati Manjaramkar
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  2. Manesh Kokare
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Correspondence to Arati Manjaramkar.

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Manjaramkar, A., Kokare, M. Statistical Geometrical Features for Microaneurysm Detection. J Digit Imaging 31, 224–234 (2018). https://doi.org/10.1007/s10278-017-0008-0

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  • DOI: https://doi.org/10.1007/s10278-017-0008-0

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