Retinal blood vessel segmentation in fundus images based on morphological operators within entropy information

Amal Chouchene; Walid Barhoumi; Amer Ahmad

doi:10.1117/12.2587048

4 January 2021 Retinal blood vessel segmentation in fundus images based on morphological operators within entropy information

Amal Chouchene, Walid Barhoumi, Amer Ahmad

Author Affiliations +

Proceedings Volume 11605, Thirteenth International Conference on Machine Vision; 116050Z (2021) https://doi.org/10.1117/12.2587048
Event: Thirteenth International Conference on Machine Vision, 2020, Rome, Italy

Abstract

Retinal blood vessel segmentation in fundus images has become essential for various applications of computer-aided anomaly analysis. In this work, we propose an automated segmentation method based on mathematical morphology combined with entropy information, what allows an accurate classification of each pixel depending on its neighbors’ comportment. The main contribution resides in the joint integration, for the first time within the context of retinal blood vessel segmentation to the best of our knowledge, of the entropy information within the hysteresis thresholding. The first step of the method consists of classifying the image pixels using morphological operators. Then, we extract the entropy information followed by a hysteresis thresholding in order to isolate the retinal blood vessels from the background, while ensuring the smoothness and the spatial coherence of the kept pixels. Finally, morphological operators are applied to refine the segmentation results. Qualitative and quantitative tests were performed on the standard DRIVE and STARE datasets and the obtained results proved the effectiveness of the proposed method.

Citation Download Citation

Amal Chouchene, Walid Barhoumi, and Amer Ahmad "Retinal blood vessel segmentation in fundus images based on morphological operators within entropy information", Proc. SPIE 11605, Thirteenth International Conference on Machine Vision, 116050Z (4 January 2021); https://doi.org/10.1117/12.2587048

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