Abstract
Branch retinal vein occlusion (BRVO) is one of the most common retinal vascular diseases of the elderly that would dramatically impair one’s vision if it is not diagnosed and treated timely. Automatic recognition of BRVO could significantly reduce an ophthalmologist’s workload, make the diagnosis more efficient, and save the patients’ time and costs. In this paper, we propose for the first time, to the best of our knowledge, automatic recognition of BRVO using fundus images. In particular, we propose Hierarchical Local Binary Pattern (HLBP) to represent the visual content of an fundus image for classification. HLBP is comprised of Local Binary Pattern (LBP) in a hierarchical fashion with max-pooling. In order to evaluate the performance of HLBP, we establish a BRVO dataset for experiments. HLBP is compared with several state-of-the-art feature presentation methods on the BRVO dataset. Experimental results demonstrate the superior performance of our proposed method for BRVO recognition.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
The dataset can be downloaded on: http://pan.baidu.com/s/1ntohK5V.
References
Ehlers, J.P., Decroos, F.C., Fekrat, S.: Intravitreal bevacizumab for macular edema secondary to branch retinal vein occlusion. Retina J. Retinal and Vitreous Dis. 31, 1856–1862 (2011)
Walter, T., Massin, P., Erginay, A., Ordonez, R., Jeulin, C., Klein, J.C.: Automatic detection of microaneurysms in color fundus images. Med. Image Anal. 11, 555–566 (2007)
Fleming, A.D., Philip, S., Goatman, K.A., Olson, J.A., Sharp, P.F.: Automated assessment of diabetic retinal image quality based on clarity and field definition. Invest. Ophthalmol. Vis. Sci. 47, 1120–1125 (2006)
Tavakoli, M., Shahri, R.P., Pourreza, H., Mehdizadeh, A., Banaee, T., Toosi, M.H.B.: A complementary method for automated detection of microaneurysms in fluorescein angiography fundus images to assess diabetic retinopathy. Pattern Recogn. 46, 2740–2753 (2013)
Akram, M.U., Khalid, S., Khan, S.A.: Identification and classification of microaneurysms for early detection of diabetic retinopathy. Pattern Recogn. 46, 107–116 (2013)
Zhang, B., Karray, F., Li, Q., Zhang, L.: Sparse representation classifier for microaneurysm detection and retinal blood vessel extraction. Inf. Sci. 200, 78–90 (2012)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. TPAMI 24, 971–87 (2002)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, pp. 886–893 (2005)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60, 91–110 (2004)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: CVPR, pp. 2169–2178 (2006)
Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. IJCV 42, 145–175 (2001)
Wu, J., Rehg, J.M.: CENTRIST: a visual descriptor for scene categorization. TPAMI 33, 1489–1501 (2011)
Wang, X., Han, T.X., Yan, S.: An HOG-LBP human detector with partial occlusion handling. In: ICCV, pp. 32–39 (2009)
Li, M., Staunton, R.C.: Optimum Gabor filter design and local binary patterns for texture segmentation. Pattern Recogn. Lett. 29, 664–672 (2008)
Heusch, G., Rodriguez, Y., Marcel, S.: Local binary patterns as an image preprocessing for face authentication. In: FG, pp. 9–14 (2006)
Moore, S., Bowden, R.: Local binary patterns for multi-view facial expression recognition. Comput. Vis. Image Underst. 115, 541–558 (2011)
Zhang, B.C., Gao, Y.S., Zhao, S.Q., Liu, J.Z.: Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. TIP 19, 533–544 (2010)
Sorensen, L., Shaker, S., de Bruijne, M.: Quantitative analysis of pulmonary emphysema using local binary patterns. IEEE Trans. Med. Imaging 29, 559–569 (2010)
He, Y., Sang, N., Gao, C.: Pyramid-based multi-structure local binary pattern for texture classification. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part III. LNCS, vol. 6494, pp. 133–144. Springer, Heidelberg (2011)
Zhu, C., Bichot, C.E., Chen, L.: Multi-scale color local binary patterns for visual object classes recognition. In: ICPR, pp. 3065–3068 (2010)
Chen, J., Kellokumpu, V., Zhao, G., Pietikainen, M.: RLBP: robust local binary pattern. In: BMVC (2013)
Guo, Y., Zhao, G., Zhou, Z., Pietikainen, M.: Video texture synthesis with multi-frame LBP-TOP and diffeomorphic growth model. TIP 22, 3879–3891 (2013)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006)
Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2, 1–127 (2009)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS (2012)
Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Learning hierarchical features for scene labeling. TPAMI 35, 1915–1929 (2013)
Sun, Y., Wang, X., Tang, X.: Deep convolutional network cascade for facial point detection. In: CVPR, pp. 3476–3483 (2013)
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: CVPR, pp. 1725–1732 (2014)
Wolf, L., Hassner, T., Taigman, Y.: Descriptor based methods in the wild. In: ECCV Workshop on Faces in Real-Life Images (2008)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011)
Acknowledgement
This work was supported by a research grant from The Hong Kong Polytechnic University (Project Code: G-YL77). The authors thank Yancheng Third People’s Hospital for providing the BRVO and normal color fundus images.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Chen, Z., Zhang, H., Chi, Z., Fu, H. (2015). Hierarchical Local Binary Pattern for Branch Retinal Vein Occlusion Recognition. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9008. Springer, Cham. https://doi.org/10.1007/978-3-319-16628-5_49
Download citation
DOI: https://doi.org/10.1007/978-3-319-16628-5_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16627-8
Online ISBN: 978-3-319-16628-5
eBook Packages: Computer ScienceComputer Science (R0)