Abstract
Microaneurysm (MA) is the earliest lesion of diabetic retinopathy (DR). Accurate detection of MA is helpful for the early diagnosis of DR. In this paper, an efficient approach is proposed to detect MA, based on feature-transfer network and local background suppression. In order to reduce noise, a feature-distance-based algorithm is proposed to suppress local background. The similarity matrix of feature distances is calculated to measure the difference between background noise and retinal objects. Moreover, a feature-transfer network is proposed to detect MAs with imbalanced data. For each training process, the optimized weights and bias are transferred to the next training, until the optimal network is generated. Experimental results demonstrate that the proposed approach can accurately detect subtle MAs surrounded by complex background. Furthermore, the sensitivity values on the public datasets are up to 98.3%, 100%, 99.3%, 100%, 96.5%, respectively. The proposed approach outperforms the state-of-the-arts, in terms of the competition performance measure score.
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References
American Diabetes Association: Diagnosis and classification of diabetes mellitus. Diabetes Care 37(Supplement 1), S81–S90 (2014)
Gilbert, M.P.: Screening and treatment by the primary care provider of common diabetes complications. Med. Clin. N. Am. 99(1), 201–219 (2015)
Harris, S.B., Tompkins, J.W., Tehiwi, B.: Call to action: a new path for improving diabetes care for indigenous peoples, a global review. Diabetes Res. Clin. Pract. 123, 120–133 (2017)
Stitt, A.W., Curtis, T.M., Chen, M., Medina, R.J., Mckay, G.J., Jenkins, A., Gardiner, T.A., Lyons, T.J., Hammes, H.P., Simó, R.: The progress in understanding and treatment of diabetic retinopathy. Prog. Retin. Eye Res. 51, 156–186 (2016)
United Kingdom Prospective Diabetes Study Group: Tight blood pressure control and risk of macrovascular and microvascular complications in type 2 diabetes: UKPDS 38. UK Prospective Diabetes Study Group. Br. Med. J. 317(7160), 703–713 (1998)
Martins, C.I.O., Medeiros, F.N.S., Bezerra, F.N., Bezerra, F.N.: Evaluation of retinal vessel segmentation methods for microaneurysms detection. In: IEEE International Conference on Image Processing, pp. 3365–3368 (2009)
Abramoff, M., Reinhardt, J., Russell, S., Folk, J., Mahajan, V., Niemeijer, M., Quellec, G.: Automated early detection of diabetic retinopathy. Inf. Sci. 117(6), 1147–1154 (2010)
Dai, L., Fang, R., Li, H., Hou, X., Sheng, B., Wu, Q., Jia, W.: Clinical report guided retinal microaneurysm detection with multi-sieving deep learning. IEEE Trans. Med. Imaging 37(5), 1149–1161 (2018)
Lyu, X., Li, H., Yi, Z., Xin, J., Zhang, S.: Deep tessellated retinal image detection using convolutional neural networks. In: International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 676–680 (2017)
Quellec, G., Lamard, M., Josselin, P.M., Cazuguel, G., Cochener, B., Roux, C.: Optimal wavelet transform for the detection of microaneurysms in retina photographs. IEEE Trans. Med. Imaging 27(9), 1230–1241 (2008)
Ding, S., Ma, W.: An accurate approach for microaneurysm detection in digital fundus images. In: International Conference on Pattern Recognition, pp. 1846–1851 (2014)
Lazar, I., Hajdu, A.: Retinal microaneurysm detection through local rotating cross-section profile analysis. IEEE Trans. Med. Imaging 32(2), 400–407 (2013)
Kamble, R., Kokare, M.: Detection of microaneurysm using local rank transform in color fundus images. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 4442–4446 (2017)
Niemeijer, M., van Ginneken, B., Staal, J., Suttorp-Schulten, M.S.A., Abramoff, M.D.: Automatic detection of red lesions in digital color fundus photographs. IEEE Trans. Med. Imaging 24(5), 584–592 (2005)
Fleming, A.D., Philip, S., Goatman, K.A., Olson, J.A., Sharp, P.F.: Automated microaneurysm detection using local contrast normalization and local vessel detection. IEEE Trans. Med. Imaging 25(9), 1223–1232 (2006)
Sopharak, A., Uyyanonvara, B., Barman, S.: Simple hybrid method for fine microaneurysm detection from non-dilated diabetic retinopathy retinal images. Comput. Med. Imaging Graph. 37(5–6), 394–402 (2013)
Zhang, B., Zhang, L., You, J., Karray, F.: Microaneurysm (MA) detection via sparse representation classifier with MA and non-MA dictionary learning. In: International Conference on Pattern Recognition, pp. 277–280 (2010)
Akram, M.U., Khalid, S., Khan, S.A.: Identification and classification of microaneurysms for early detection of diabetic retinopathy. Pattern Recognit. 46(1), 107–116 (2013)
Dai, B., Wu, X., Bu, W.: Retinal microaneurysms detection using gradient vector analysis and class imbalance classification. PLoS ONE 11(8), e0161556-1–e0161556-23 (2016)
Wu, B., Zhu, W., Shi, F., Zhu, S., Chen, X.: Automatic detection of microaneurysms in retinal fundus images. Comput. Med. Imaging Graph. 55, 106–112 (2017)
Wang, S., Tang, H., Al Turk, L., Hu, Y., Sanei, S., Saleh, G., Peto, T.: Localizing microaneurysms in fundus images through singular spectrum analysis. IEEE Trans. Biomed. Eng. 64(5), 990–1002 (2017)
Antal, B., Hajdu, A.: Improving microaneurysm detection using an optimally selected subset of candidate extractors and preprocessing methods. Pattern Recognit. 45(1), 264–270 (2012)
Kuanar, S., Athitsos, V., Pradhan, N., Mishra, A., Rao, K.R.: Cognitive analysis of working memory load from EEG, by a deep recurrent neural network. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2576–2580 (2018)
Kuanar, S., Athitsos, V., Mahapatra, D., Rao, K.R., Akhtar, Z., Dasgupta, D.: Low dose abdominal ct image reconstruction: an unsupervised learning based approach. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 1351–1355 (2019)
Haloi, M.: Improved microaneurysm detection using deep neural networks. arXiv preprint arXiv:1505.04424 (2015)
Chudzik, P., Majumdar, S., Caliva, F., Al-Diri, B., Hunter, A.: Microaneurysm detection using deep learning and interleaved freezing. In: Proceedings of SPIE in Medical Imaging: Image Processing, vol. 105741I, pp. 1–9 (2018)
Eftekhari, N., Pourreza, H.-R., Masoudi, M., Ghiasi-Shirazi, K., Saeedi, E.: Microaneurysm detection in fundus images using a two-step convolutional neural networks. Biomed. Eng. Online 18(1), 67–82 (2019)
Chudzik, P., Majumdar, S., Calivá, F., Al-Diri, B., Hunter, A.: Microaneurysm detection using fully convolutional neural networks. Comput. Methods Programs Biomed. 158, 185–192 (2018)
Tan, J.H., Fujita, H., Sivaprasad, S., Bhandary, S.V., Rao, A.K., Chua, K.C., Acharya, U.R.: Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network. Inf. Sci. 420, 66–76 (2017)
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)
Tamilarasi, M., Duraiswamy, K.: Automatic detection of microaneurysms using microstructure and wavelet methods. Sadhana 40(4), 1185–1203 (2015)
Xiao, Z., Zhang, X., Zhang, F., Geng, L., Wu, J., Su, L., Chen, L.: Diabetic retinopathy retinal image enhancement based on gamma correction. J. Med. Imaging Health Inform. 7, 149–154 (2017)
Lupascu, C.A., Tegolo, D., Trucco, E.: FABC: retinal vessel segmentation using AdaBoost. IEEE Trans. Inf. Technol. Biomed. 14(5), 1267–1274 (2010)
Morales, J.L., Nocedal, J.: Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization. ACM Trans. Math. Softw. (TOMS) 23(4), 550–560 (1997)
Niemeijer, M., Van Ginneken, B., Cree, M.J., Mizutani, A., Quellec, G., Sanchez, C.I., Zhang, B., Hornero, R., Lamard, M., Muramatsu, C.: Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs. IEEE Trans. Med. Imaging 29(1), 185–195 (2010)
Kauppi, T., Kalesnykiene, V., Kamarainen, J.-K., Lensu, L., Sorri, I., Raninen, A., Voutilainen, R., Pietila, J., Kalviainen, H., Uusitalo, H.: The diaretdb1 diabetic retinopathy database and evaluation protocol. In: Proceedings of the British Machine Vision Conference, vol. 2007, pp. 1–10 (2007)
Decenciere, E., Cazuguel, G., Zhang, X., Thibault, G., Klein, J.C., Meyer, F., Marcotegui, B., Quellec, G., Lamard, M., Danno, R., Elie, D., Massin, P., Viktor, Z., Erginay, A., Lay, B., Chabouis, A.: TeleOphta: machine learning and image processing methods for teleophthalmology. IRBM 34(2), 196–203 (2013)
Dashtbozorg, B., Zhang, J., Huang, F., Ter Haar Romeny, B.M.: Retinal microaneurysms detection using local convergence index features. IEEE Trans. Image Process. 27(7), 3300–3315 (2018)
Seoud, L., Hurtut, T., Chelbi, J., Cheriet, F., Langlois, J.: Red lesion detection using dynamic shape features for diabetic retinopathy screening. IEEE Trans. Med. Imaging 35(4), 1116–1126 (2016)
Adal, K.M., Sidibé, D., Ali, S., Chaum, E., Karnowski, T.P., Mériaudeau, F.: Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning. Comput. Methods Prog. Biomed. 114(1), 1–10 (2014)
Pereira, C., Veiga, D., Mahdjoub, J., Guessoum, Z., Gonçalves, L., Ferreira, M., Monteiro, J.: Using a multi-agent system approach for microaneurysm detection in fundus images. Artif. Intell. Med. 60(3), 179–188 (2014)
Antal, B., Hajdu, A.: An ensemble-based system for microaneurysm detection and diabetic retinopathy grading. IEEE Trans. Biomed. Eng. 59(6), 1720–1726 (2012)
Zhang, B., Li, Q., Zhang, L.: Sparse representation classifier for microaneurysm detection and retinal blood vessel extraction. Inf. Sci. 200(1), 78–90 (2012)
Giancardo, L., Meriaudeau, F., Karnowski, T.P., Li, Y., Tobin Jr, K.W., Chaum, E.: Microaneurysm detection with radon transform-based classification on retina images. In: International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5939–5942 (2011)
Ram, K., Joshi, G.D., Sivaswamy, J.: A successive clutter-rejection-based approach for early detection of diabetic retinopathy. IEEE Trans. Biomed. Eng. 58(3), 664–673 (2011)
Sánchez, C.I., Hornero, R., Mayo, A., García, M.: Mixture modelbased clustering and logistic regression for automatic detection of microaneurysms in retinal images. In: Proceedings of SPIE in Medical Imaging, vol. 7260, pp. 72601M-1–72601M-8 (2009)
Acknowledgements
This work was supported by the National Key R&D Program of China under Grant No. 2018YFB1003201. It was also supported in part by the National Natural Science Foundation of China under Grant Nos. 61902078 and 61702114. It was supported in part by Key-Area R&D Program of Guangdong under Grant No. 2018B010107003, by Natural Science Foundation of Guangdong, China, under Grant Nos. 2018B030311007 and 2020A1515011361.
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Zhang, X., Wu, J., Meng, M. et al. Feature-transfer network and local background suppression for microaneurysm detection. Machine Vision and Applications 32, 1 (2021). https://doi.org/10.1007/s00138-020-01119-9
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DOI: https://doi.org/10.1007/s00138-020-01119-9