ZHU Chengzhang, ZOU Beiji, XIANG Yao, CUI Jinkai, WU Hui. An Ensemble Retinal Vessel Segmentation Based on Supervised Learning in Fundus Images[J]. Chinese Journal of Electronics, 2016, 25(3): 503-511. doi: 10.1049/cje.2016.05.016
Citation: ZHU Chengzhang, ZOU Beiji, XIANG Yao, CUI Jinkai, WU Hui. An Ensemble Retinal Vessel Segmentation Based on Supervised Learning in Fundus Images[J]. Chinese Journal of Electronics, 2016, 25(3): 503-511. doi: 10.1049/cje.2016.05.016

An Ensemble Retinal Vessel Segmentation Based on Supervised Learning in Fundus Images

doi: 10.1049/cje.2016.05.016
Funds:  This work is supported by the National Natural Science Foundation of China (No.61573380, No.61262032, No.61579117), the Hunan Provincial Innovation Foundation for Postgraduate (No.CX2013B074) and the Specialized Research Fund for the Doctoral Program of Higher Education (No.20130162120089).
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  • Corresponding author: XIANG Yao was born in 1983. She received the Ph.D. degree from Central South University, in 2011. She is now an instructor in the School of Information Science and Engineering, Central South University. Her research interests include computer vision and image processing. (Email: yao.xiang@mail.csu.edu.cn)
  • Received Date: 2015-05-22
  • Rev Recd Date: 2015-09-08
  • Publish Date: 2016-05-10
  • An ensemble method based on supervised learning for segmenting the retinal vessels in color fundus images is proposed on the basis of previous work of Zhu et al. For each pixel, a 36 dimensional feature vector is extracted, including local features, morphological transformation with multi-scale and multi-orientation, and divergence of vector field which is firstly used to extract feature of retinal image pixels. Then the feature vector is used as input data set to train the weak classifiers by the Classification and regression tree (CART). Finally, an AdaBoost classifier is constructed by iteratively training for the retinal vessels segmentation. The experimental results on the public Digital retinal images for vessel extraction (DRIVE) database demonstrate that the proposed method is efficient and robust on the fundus images with lesions when compared with the other methods. Meanwhile, the proposed method also exhibits high robustness on a new Retinal images for screening (RIS) database. The average accuracy, sensitivity, and specificity of improved method are 0.9535, 0.8319 and 0.9607, respectively. It has potential applications for computer-aided diagnosis and disease screening.
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  • Yusup. Mehriban and X.Y. Chen, "Epidemiology survey of visual loss", International of Journal Ophthalmol, Vol.10, No.2, pp.304-307, 2012. (in Chinese)
    C. Kirbas and F. Quek, "A review of vessel extraction techniques and algorithms", ACM Computing Surveys, Vol.36, No.2, pp.81-121, 2004.
    International Diabetes Federation, "IDF diabetes atlas-7th edition", http://www.diabetesatlas.org, 2016-3-10.
    R.J. Winder, P.J. Morrow, I. NMcRitchie, et al., "Algorithms for digital image processing in diabetic retinopathy", Computer Medical Imaging and Graphics, Vol.33, No.8, pp.608-622, 2009.
    M.D. Abramoff, M.K. Garvin and M. Sonka, "Retinal imaging and image analysis", IEEE Reviews in Biomedical Engineering, Vol.3, pp.169-208, 2010.
    C.Z. Zhu, Y. Xiang, B.J. Zou, et al., "Retinal vessel segmentation in fundus images using CART and AdaBoost", Journal of Computer-Aided Design and Computer Graphics, Vol.26, No.3, pp.445-451, 2014. (in Chinese)
    S. Gey and E. Nedelec, "Model selection for CART regression trees", IEEE Transactions on Information Theory, Vol.51, No.2, pp.658-670, 2005.
    Y. Freund and R.E. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting", Journal of Computer and System Sciences, Vol.55, No.1, pp.119-139, 1997.
    M.M. Fraza, P. Remagninoa, A. Hoppea, et al., "Blood vessel segmentation methodologies in retinal images-A survey", Computer Methods and Programs in Biomedicine, Vol.108, No.1, pp.407-433, 2012.
    J. Staal, M.D. Abramoff, M. Niemeijeret, et al., "Ridge-based vessel segmentation in color images of the retina", IEEE Transactions on Medical Imaging, Vol.23, No.4, pp.501-509, 2004.
    J.V.B. Soares, J.J.G. Leandro, R.M. Cesar, et al., "Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification", IEEE Transactions on Medical Imaging, Vol.25, No.9, pp.1214-1222, 2006.
    E. Ricci and R. Perfetti, "Retinal blood vessel segmentation using line operators and support vector classification", IEEE Transactions on Medical Imaging, Vol.26, No.10, pp.1357-1365, 2007.
    C.A. Lupascu, D. Tegolo and E. Trucco, "FABC: Retinal vessel segmentation using AdaBoost", IEEE Transactions on Information Technology Biomedicine, Vol.14, No.5, pp.1267-1274, 2010.
    D. Marin, A. Aquino, M.E. Gegundez-Arias, et al., "A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features", IEEE Transactions on Medical Imaging, Vol.30, No.1, pp.146- 158, 2011.
    M.M. Fraz, P. Remagnino, A. Hoppe, et al., "An ensemble classification-based approach applied to retinal blood vessel segmentation", IEEE Transactions on Biomedicine Engineering, Vol.59, No.9, pp.2538-2548, 2012.
    S.L. Wang, Y.L. Yin, G.B. Cao, et al., "Hierarchical retinal blood vessel segmentation based on feature and ensemble learning", Neurocomputing, Vol.149, pp.708-717, 2015.
    T. Lindeberg, "Edge detection and ridge detection with automatic scale selection", International Journal of Computer Vision, Vol.30, No.2, pp.117-156, 1998.
    Y.F. Wang, G.R. Ji, P. Lin, et al., "Retinal vessel segmentation using multiwavelet kernels and multiscale hierarchical decomposition", Pattern Recognition, Vol.46, No.8, pp.2117-2133, 2013.
    X. Chang, L.C. Jiao, F. Liu, et al., "SAR image despeckling using scale mixtures of gaussians in the nonsubsampled contourlet domain", Chinese Journal of Electronics, Vol.24, No.1, pp.205-221, 2015.
    B.S.Y. Lam and H. Yan, "A novel vessel segmentation algorithm for pathological retina images based on the divergence of vector fields", IEEE Transactions on Medical Imaging, Vol.27, No.2, pp.237-246, 2008.
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