A novel retinal vessel detection approach based on multiple deep convolution neural networks. (December 2018)

• Record Type: Journal Article
• Authors: Guo, Yanhui; Budak, Ümit; Şengür, Abdulkadir
• Is Part Of: Computer methods and programs in biomedicine. Volume 167(2018)
• Page Start: 43
• Page End: 48
• Publication Date: 2018-12
• Subjects: Retinal vessels segmentation -- Multiple deep convolution neural network -- Image segmentation; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.10.021
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: This study formulates the retinal vessel detection task as a classification problem and solves it using a multiple classifier framework based on deep convolutional neural networks. The MDCNN is trained using an incremental learning strategy to improve the networks' performance. The final classification results are obtained from the voting procedure on the results of MDCNN. The MDCNN achieves better performance and significantly outperforms the state-of-the-art for automatic retinal vessel segmentation on the DRIVE and STARE datasets. Abstract: Background and objective: Computer aided detection (CAD) offers an efficient way to assist doctors to interpret fundus images. In a CAD system, retinal vessel (RV) detection is a crucial step to identify the retinal disease regions. However, RV detection is still a challenging problem due to variations in morphology of the vessels on noisy and low contrast fundus images. Methods: In this paper, we formulate the detection task as a classification problem and solve it using a multiple classifier framework based on deep convolutional neural networks. The multiple deep convolutional neural network (MDCNN) is constructed and trained on fundus images with limited image quantity. The MDCNN is trained using an incremental learning strategy to improve the networks' performance. The final classification results are obtained from the voting procedure on the results of MDCNN. Results: The MDCNN achieves better performance and significantly outperforms the state-of-the-art for automatic retinal vessel segmentation on the DRIVE dataset with 95.97% and 96.13% accuracy and 0.9726 and 0.9737 AUC (area below the operator receiver character curve) score on training and testing sets, respectively. Another public dataset, STARE, is also used to evaluate the proposed network. The experimental results demonstrate that the proposed MDCNN network achieves 95.39% accuracy and 0.9539 AUC score in STARE dataset. We further compare our result with several state-of-the-art methods based on AUC values. The comparison is shown that our proposal yields the third best AUC value. Conclusions: Our method yields the better performance in the compared the state of the art methods. In addition, our proposal has no preprocessing stage, and the input color fundus images are fed into the CNN directly.