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Journal of Imaging Informatics in Medicine

Erschienen in:

07.08.2017

Statistical Geometrical Features for Microaneurysm Detection

verfasst von: Arati Manjaramkar, Manesh Kokare

Erschienen in: Journal of Imaging Informatics in Medicine | Ausgabe 2/2018

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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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Titel: Statistical Geometrical Features for Microaneurysm Detection
verfasst von: Arati Manjaramkar
Manesh Kokare
Publikationsdatum: 07.08.2017
Verlag: Springer International Publishing
Erschienen in: Journal of Imaging Informatics in Medicine / Ausgabe 2/2018
Print ISSN: 2948-2925
Elektronische ISSN: 2948-2933
DOI: https://doi.org/10.1007/s10278-017-0008-0

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Statistical Geometrical Features for Microaneurysm Detection

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