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Article type: Research Article
Authors: Du, Xin-Feng | Wang, Jie-Sheng; * | Sun, Wei-Zhen | Zhang, Zhi-Hao | Zhang, Yun-Hao
Affiliations: School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, China
Correspondence: [*] Corresponding author. Jie-Sheng Wang, School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, China. E-mail: [email protected].
Abstract: Accurate blood vessel segmentation on retinal blood vessel images is helpful for the early detection of ophthalmic diseases such as diabetes, hypertension, cardiovascular and cerebrovascular diseases, and inhibits the deterioration of the disease. In current research within the field of retinal blood vessel segmentation, significant challenges exist in accurately segmenting small blood vessels and maintaining blood vessel continuity. The segmentation algorithm proposed in this article offers substantial improvements to address these issues. To enhance the segmentation performance of retinal blood vessels and facilitate more accurate diagnosis of fundus diseases by ophthalmologists, this paper introduces a novel bidirectional convolutional long short-term memory (LSTM) residual U-Net segmentation algorithm, incorporating improvements to the Focal loss function. Firstly, in the encoding part of U-Net, the multi-scale convolution kernels and Bi-ConvLSTM were adopted to improve the residual structure, obtain richer blood vessel features and enhance the detection ability of micro vessels and the continuity of blood vessel characteristics. At the same time, the class balanced cross entropy loss function was improved and the proportional modulation factor is introduced to enhance the learning ability of the network for difficult samples. By adding the Bi-ConvLSTM to the residual structure and introducing the proportional modulation coefficient to the loss function, the network structure realizes better feature information detection and greatly enhances the detection ability of small blood vessels. The experimental analysis on the DRIVE and CHASE_DB1 data sets showed that the sensitivity, specificity, accuracy and AUC reached 0.7961, 0.9796, 0.9563, 0.9792; 0.8344, 0.9665, 0.9547, 0.9758, respectively. The experimental results fully show that the Bi-ConvLSTM residual U-Net segmentation algorithm based on the improved Focal loss function enhances the detection ability of small blood vessel features, improves the continuity of blood vessel features and the network segmentation performance, and is superior to U-Net algorithm and some current mainstream retinal blood vessel segmentation algorithms.
Keywords: Retinal blood vessel segmentation, bi-directional convolution long and short time memory network, residual block, Multi-scaleconvolution, U-Net, proportional modulation coefficient
DOI: 10.3233/JIFS-236702
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10167-10186, 2024
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