Exudates Detection Using Morphology Mean Shift Algorithm in Retinal Images

• Main Authors: Kittipol Wisaeng, Worawat Sa-Ngiamvibool
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Diabetic retinopathy; retinal image; exudates; mean shift algorithm; mathematical morphology
• Online Access: https://ieeexplore.ieee.org/document/8600328/

Exudates are a serious complication causing blindness in diabetic retinopathy patients. The main objective of this paper is to develop a novel method to detect exudates lesions in color retinal images by using a morphology mean shift algorithm. The proposed method start with a normalization of the retinal image, contrast enhancement, noise removal, and the localization of the OD. Then, a coarse segmentation method by using mean shift provides a set of exudates and non-exudates candidates. Finally, a classification using the mathematical morphology algorithm (MMA) procedure is applied in order to keep only exudates pixels. The optimal value parameters of the MMA will facilitate an increase of the accuracy results from the solely MSA method by 13.10%. Based on a comparison between the results and ground truth images, the proposed method obtained an average sensitivity, specificity, and accuracy of detecting exudates as 98.40%, 98.13%, and 98.35%, respectively.