Journal
NEUROCOMPUTING
Volume 149, Issue -, Pages 708-717Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2014.07.059
Keywords
Convolutional neural network; Ensemble learning; Feature learning; Random forest; Retinal blood vessel segmentation
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Funding
- NSFC Joint Fund with Guangdong [U1201258]
- Program for New Century Excellent Talents in University of Ministry of Education of China [NCET-11-0315]
- Shandong Natural Science Funds for Distinguished Young Scholar [JQ201316]
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Segmentation of retinal blood vessels is of substantial clinical importance for diagnoses of many diseases, such as diabetic retinopathy, hypertension and cardiovascular diseases. In this paper, the supervised method is presented to tackle the problem of retinal blood vessel segmentation, which combines two superior classifiers: Convolutional Neural Network (CNN) and Random Forest (RF). In this method, CNN performs as a trainable hierarchical feature extractor and ensemble RFs work as a trainable classifier. By integrating the merits of feature learning and traditional classifier, the proposed method is able to automatically learn features from the raw images and predict the patterns. Extensive experiments have been conducted on two public retinal images databases (DRIVE and STARE), and comparisons with other major studies on the same database demonstrate the promising performance and effectiveness of the proposed method. (C) 2014 Elsevier B.V. All rights reserved.
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