Automatic exudates detection from diabetic retinopathy retinal image using fuzzy C-means and morphological methods

Authors:
Akara Sopharak;Bunyarit Uyyanonvara
Affiliations:
Sirindhorn International Institute of Technology, Thammasat University, Bangkadi, Muang, Pathumthani, Thailand;Sirindhorn International Institute of Technology, Thammasat University, Bangkadi, Muang, Pathumthani, Thailand
Venue:
ACST'07 Proceedings of the third conference on IASTED International Conference: Advances in Computer Science and Technology
Year:
2007

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Abstract

Exudates are the primary signs of diabetic retinopathy which are mainly cause of blindness and could be prevented with an early screening process. Pupil dilation is required in the normal screening process but this affects patients' vision. This paper investigated and proposed automatic methods of exudates detection on low-contrast images taken from non-dilated pupils. The process has two main segmentation steps which are coarse segmentation using Fuzzy C-Means clustering and fine segmentation using morphological reconstruction. Four features, namely intensity, standard deviation on intensity, hue and adapted edge, were selected for coarse segmentation. The detection results are validated by comparing with expert ophthalmologists' hand-drawn ground-truth. The sensitivity and specificity for our exudates detection are 86% and 99% respectively.