Comparative analysis of improved FCM algorithms for the segmentation of retinal blood vessels
Author:
Publisher
Springer Science and Business Media LLC
Subject
Geometry and Topology,Theoretical Computer Science,Software
Link
https://link.springer.com/content/pdf/10.1007/s00500-022-07531-9.pdf
Reference37 articles.
1. Abdulsahib AA, Mahmoud MA, Mohammed MA, Rasheed HH, Mostafa SA, Maashi MS (2021) Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images. Netw Model Anal Health Inf Bioinf 10(1):1–32
2. Ahmed MN, Yamany SM, Mohamed N, Farag AA, Moriarty T (2002) A modified fuzzy c-means algorithm for bias field estimation and segmentation of mri data. IEEE Trans Med Imaging 21(3):193–199
3. Almotiri J, Elleithy K, Elleithy A (2018) Retinal vessels segmentation techniques and algorithms: a survey. Appl Sci 8(2):155. https://doi.org/10.3390/app8020155
4. Azzopardi G, Strisciuglio N, Vento M, Petkov N (2015) Trainable cosfire filters for vessel delineation with application to retinal images. Med Image Anal 19(1):46–57. https://doi.org/10.1016/j.media.2014.08.002
5. Bezdek JC, Ehrlich R, Full W (1984) Fcm: The fuzzy c-means clustering algorithm. Comput Geosc 10(2–3):191–203
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