Abstract
Reliable detection of fundus lesion is important for automated screening of diabetic retinopathy. This paper presents a novel method to detect the fundus lesion in retinal fundus image based on a visual attention model. The proposed method intends to model the visual attention mechanism of ophthalmologists during observing fundus images. That is, the abnormal structures, such as the dark and bright lesions in the image, usually attract the most attention of experts, however, the normal structures, such as optic disc and vessels, have been usually selectively ignored. To measure the visual attention for abnormal and normal areas, the incremental coding length is computed in local and global manner respectively. The final saliency map of fundus lesion is a fusion of attention maps computed for the abnormal and normal areas. Experimental results conducted on the publicly DiaRetDB1 dataset show that the proposed method achieved a sensitivity of 0.71 at a specificity of 0.82 and an AUC of 0.76 for fundus lesion detection, and achieved an accuracy of 100 % for normal area (optic disc) detection. The proposed method can assist the ophthalmologists in the inspection of fundus lesion.
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References
Zhang, B., Wu, X., You, J., Li, Q., Karray, F.: Detection of microaneurysms using multi-scale correlation coefficients. Pattern Recognit. 43(6), 2237–2248 (2010)
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(6), 555–566 (2007)
Tang, L., Niemeijer, M., Reinhardt, J.M., Garvin, M.K., Abràmoff, M.D.: Splat feature classification with application to retinal hemorrhage detection in fundus images. IEEE Trans. Med. Imaging 32(2), 364–375 (2013)
Sánchez, C.I., García, M., Mayo, A., López, M.I., Hornero, R.: Retinal image analysis based on mixture models to detect hard exudates. Med. Image Anal. 13(4), 650–658 (2009)
Hsu, W., Pallawala, P., Lee, M.L., Eong, K.G.A.: The role of domain knowledge in the detection of retinal hard exudates. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001, CVPR 2001, vol. 2, pp. II-246–II-251. IEEE (2001)
Xiaohui, Z., Chutatape, O.: Detection and classification of bright lesions in color fundus images. In: 2004 International Conference on Image Processing, 2004, ICIP 2004, vol. 1, pp. 139–142. IEEE (2004)
Pratt, J., Abrams, R.A.: Inhibition of return to successively cued spatial locations. J. Exp. Psychol. Hum. Percept. Perform. 21(6), 1343 (1995)
Wright, J., Ma, Y., Tao, Y., Lin, Z., Shum, H.Y.: Classification via minimum incremental coding length. SIAM J. Imaging Sci. 2(2), 367–395 (2009)
Li, Y., Zhou, Y., Xu, L., Yang, X., Yang, J.: Incremental sparse saliency detection. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 3093–3096. IEEE (2009)
Hou, X., Zhang, L.: Dynamic visual attention: searching for coding length increments. In: Advances in Neural Information Processing Systems, pp. 681–688 (2009)
Wong, D., Liu, J., Lim, J., Jia, X., Yin, F., Li, H., Wong, T.: Level-set based automatic cup-to-disc ratio determination using retinal fundus images in argali. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008, EMBS 2008, pp. 2266–2269 (2008)
Kauppi, T., et al.: The DiaRetDB1 diabetic retinopathy database and evaluation protocol. In: Proceedings of BMVC, pp. 1–10 (2007)
Farbman, Z., Fattal, R., Lischinski, D., Szeliski, R.: Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Trans. Graph. 27(3), 67:1–67:10 (2008)
Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009, CVPR 2009, pp. 1794–1801. IEEE (2009)
Wang, Z., Fan, B., Wu, F.: Local intensity order pattern for feature description. In: Proceedings of ICCV, pp. 603–610. IEEE (2011)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of CVPR, vol. 1, pp. 886–893. IEEE (2005)
Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)
Rocha, A., Carvalho, T., Jelinek, H.F., Goldenstein, S., Wainer, J.: Points of interest and visual dictionaries for automatic retinal lesion detection. IEEE Trans. Biomed. Eng. 59(8), 2244–2253 (2012)
Hatanaka, Y., Nakagawa, T., Hayashi, Y., Mizukusa, Y., Fujita, A., Kakogawa, M., Kawase, K., Hara, T., Fujita, H.: CAD scheme for detection of hemorrhages and exudates in ocular fundus images. In: Medical Imaging, pp. 65142M–65142M. International Society for Optics and Photonics (2007)
Lowell, J., Hunter, A., Steel, D., Basu, A., Ryder, R., Fletcher, E., Kennedy, L.: Optic nerve head segmentation. IEEE Trans. Med. Imaging 23(2), 256–264 (2004)
Soares, I., Castelo-Branco, M., Pinheiro, A.: Optic disk localization in retinal images based on cumulative sum fields (2015)
Mahfouz, A.E., Fahmy, A.S.: Fast localization of the optic disc using projection of image features. IEEE Trans. Image Process. 19(12), 3285–3289 (2010)
Ramakanth, S.A., Babu, R.V.: Approximate nearest neighbour field based optic disk detection. Comput. Med. Imaging Graph. 38(1), 49–56 (2014)
Acknowledgments
The authors would like to thank those who provided materials that were used in this study. This work was supported in part by the Natural Science Foundation of China under Grant 61472102, in part by the Fundamental Research Funds for the Central Universities under Grant HIT.NSRIF.2013091, and in part by the Humanity and Social Science Youth foundation of Ministry of Education of China under Grant 14YJC760001.
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Dai, B., Bu, W., Wang, K., Wu, X. (2016). Fundus Lesion Detection Based on Visual Attention Model. In: Che, W., et al. Social Computing. ICYCSEE 2016. Communications in Computer and Information Science, vol 623. Springer, Singapore. https://doi.org/10.1007/978-981-10-2053-7_34
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DOI: https://doi.org/10.1007/978-981-10-2053-7_34
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