Research Article Open Access

Segmentation of Exudates via Color-based K-means Clustering and Statistical-based Thresholding

Ali Mohamed Nabil Allam1, Aliaa Abdel-Halim Youssif2 and Atef Zaki Ghalwash2
  • 1 Arab Academy for Science & Technology (AASTMT), Egypt
  • 2 Helwan University, Egypt


This paper provides a novel approach for the problem of detecting the yellowish lesions in the eye fundus images, such as hard and soft exudates, in a fully-automated manner. To solve this problem of segmenting exudates automatically, the fundus image was first converted into the L*a*b* color space to decouple the chromaticity information of the image. Next, the fundus image was partitioned into five disjoint clusters based on this information via the unsupervised k-means algorithm. Among the clustered images, the one having the brightest average intensity was chosen to be the best cluster containing all the bright yellowish pixels. Using this cluster, a threshold value was estimated via statistic-based metrics and subsequently applied to remove any non-bright clustered pixels and preserve only the relatively bright ones within the image. Finally, the optic disc was eliminated from the thresholded image, leaving out only the bright abnormalities. This approach was evaluated over a total of 1419 images retrieved from three heterogeneous datasets: DIARETDB0, DIARETDB1 and MESSIDOR. The proposed segmentation algorithm was fully-automated, non-customized, simple and straightforward, regardless of the heterogeneity of the datasets. The proposed system correctly detected the bright abnormalities achieving an average sensitivity and specificity of 85.08% and 56.77%, respectively.

Journal of Computer Science
Volume 13 No. 10, 2017, 524-536


Submitted On: 27 March 2017 Published On: 14 October 2017

How to Cite: Allam, A. M. N., Youssif, A. A. & Ghalwash, A. Z. (2017). Segmentation of Exudates via Color-based K-means Clustering and Statistical-based Thresholding. Journal of Computer Science, 13(10), 524-536.

  • 4 Citations



  • Abnormalities Segmentation
  • Cotton Wool Spots
  • Hard Exudates
  • K-Means Clustering
  • Statistical-Based Thresholding