Statistical Geometrical Features for Microaneurysm Detection

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; Digital fundus images; Mass screening; Microaneurysms; Object rule-based classification; Red lesion.

Fig. 1

The proposed flow chart of an automated MA detection system

Fig. 2

Cross-section of intensity profile, a green channel view of MA. Pixels belonging to MA has dark intensity than background, b gray intensity profile

Fig. 3

MA detection stages of proposed method. a Input image, b green channel image, c pre-processed image, d edge-detected image, e candidate MAs, and f True MAs

Fig. 4

a True MA detected by proposed method. Highlighted to better visualize confidence interval, b Ground truth image (image019) from DIARETDB1

Fig. 5

Performance Evaluation on DIARETDB1 dataset