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Retinal fundus vasculature multilevel segmentation using whale optimization algorithm


The aim was to present a novel automated approach for extracting the vasculature of retinal fundus images. The proposed vasculature extraction method on retinal fundus images consists of two phases: preprocessing phase and segmentation phase. In the first phase, brightness enhancement is applied for the retinal fundus images. For the vessel segmentation phase, a hybrid model of multilevel thresholding along with whale optimization algorithm (WOA) is performed. WOA is used to improve the segmentation accuracy through finding the \(n{-}1\) optimal n-level threshold on the fundus image. To evaluate the accuracy, sensitivity, specificity, accuracy, receiver operating characteristic (ROC) curve analysis measurements are used. The proposed approach achieved an overall accuracy of 97.8%, sensitivity of 88.9%, and specificity of 98.7% for the identification of retinal blood vessels by using a dataset that was collected from Bostan diagnostic center in Fayoum city. The area under the ROC curve reached a value of 0.967. Automated identification of retinal blood vessels based on whale algorithm seems highly successful through a comprehensive optimization process of operational parameters.

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Correspondence to Gehad Hassan.

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Scientific Research Group in Egypt

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Hassan, G., Hassanien, A.E. Retinal fundus vasculature multilevel segmentation using whale optimization algorithm. SIViP 12, 263–270 (2018).

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  • Whale optimization algorithm
  • Vessel segmentation
  • Swarm optimization
  • Multilevel segmentation
  • Threshold