Abstract
Blood vessel segmentation is a topic of high interest in fundus image analysis. This paper presents a clustering method to segment the blood vessels automatically from the fundus images. Our proposed method integrates with the wavelet transform, the morphological transformation and self-organizing map (SOM). Firstly, we extract a multi-dimensional feature vector of every pixel in the fundus image by wavelet transform and morphological operation. Then, the SOM network is integrated with K-mean method to cluster pixels. Finally, we validate the accuracy of our proposed method on DRIVE database, and compare our proposed method with other methods.
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Zhang, J., Wang, L., Cui, Y., Guo, L., Jiang, W. (2021). Self-organizing Map for Blood Vessel Segmentation of Fundus Images. In: Guan, M., Na, Z. (eds) Machine Learning and Intelligent Communications. MLICOM 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 342. Springer, Cham. https://doi.org/10.1007/978-3-030-66785-6_14
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