Retinal vessel segmentation using enhanced fuzzy min-max neural network
- 39 Downloads
Automated segmentation of retinal vessels plays a pivotal role in early diagnosis of ophthalmic disorders. In this paper, a blood vessel segmentation algorithm using an enhanced fuzzy min-max neural network supervised classifier is proposed. The input to the network is an optimal 11-D feature vector which consists of spatial as well as frequency domain features extracted from each pixel of a fundus image. The essence of the method is its hyperbox classifier which performs online learning and gives binary output without any need of post-processing. The method is tested on publicly available databases DRIVE and STARE. The results are compared with the existing methods in the literature. The proposed method exhibits efficient performance and can be implemented in computer aided screening and diagnosis of retinal diseases. The method attains an average accuracy, sensitivity and specificity of 95.73%, 74.75% and 97.81% on DRIVE database and 95.51%, 74.65% and 97.11% on STARE database, respectively.
KeywordsDiabetic retinopathy Vessel segmentation Hyperbox Fuzzy min-max neural network
Compliance with Ethical Standards
Conflict of interests
There is no conflict of interest declared by any of the authors.
The article does not contain any studies with human participants performed by any of the authors.
- 7.Gonzalez RC, Woods ASLERE (2010) Digital Image Processing Using MATLAB, 2nd edn. McGraw Hill Education, New YorkGoogle Scholar
- 9.International Diabetes Federation (2015). IDF diabetes atlas, 7th ed. ISBN: 978-2-930229-81-2Google Scholar
- 10.Kovesi P (1999) Image features from phase congruency. Videre: A J Comput Vis Res MIT Press 1:1–27Google Scholar
- 15.Niemeijer M, Staal J, Van-ginneken B, Loog M, Abramoff M (2004) Comparative study of retinal vessel segmentation methods on a new publicly available database. In: Fitzpatrick JM, Sonka M (eds) SPIE Medical Imaging. SPIE, vol 24, pp 648–656Google Scholar
- 17.Roychowdhury S, Koozekanani DD, Parhi KK (2015) Blood vessel segmentation of fundus images by major vessel extraction and subimage classification. IEEE J Biomed Health Inf 19:1118–1128Google Scholar