Keywords
Image segmentation
PCNN
GWO
Parameter adaptation
Multi-feature evaluation criteria
Submitted : 2025-05-07
Accepted : 2025-05-22
Published : 2025-06-06
Abstract
Since the problems of branch loss and fracture in retinal blood vessel segmentation algorithms, an image segmentation method is proposed based on improved pulse coupled neural network (PCNN) and gray wolf optimization algorithm (GWO). Simplifying the neuron input domain and neuron connection domain of the PCNN network, increasing the gradient information factor in the internal activity items, reducing the model parameters, enhancing the pulse issuing ability, and the optimal parameters of the network are automatically obtained based on multiple feature evaluation criteria and the GWO algorithm. The test in the public data set drive shows that the sensitivity, accuracy, precision, and specificity of the algorithm are 0.799549, 0.962789, 0.889163, and 0.986552, respectively. The accuracy and specificity are better than the classical segmentation algorithm. It solved the influence of low illumination, optic disc highlight, and foveal shadow on vascular segmentation, and showed excellent performance of vessel connectivity and terminal sensitivity.
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