Abstract
Macula Edema is observed in many patients having diabetes for more than ten years. It is more so in patients who have fluctuating sugar level or uncontrolled diabetes. In the case of Macula Edema, in spite of being the commonest cause, the patient realizes the issue, only when there is deterioration of vision. Experts use surrogates such as exudates near to fovea in fundus photographs for detection of Macula Edema through clinical examination. The severity is based on the proximity of the exudates to the fovea. In the present scenario with the rising rate of diabetes, an automated technique can act as an aid for the quick detection of the disease and also adds value to healthcare. This paper proposes a morphological method for extraction of exudates. A novel approach is proposed for locating the macula irrespective of the position of optic disc. The overall accuracy obtained for classification is 94.74%. The balanced accuracy obtained for classification of Normal, Non Clinically Significant Macula Edema and Clinically Significant Macula Edema is 97.92%, 92.42% and 96.77%, respectively.
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References
Lee, R., Wang, T.Y., Sabanayagam, C.: Epidemiology of diabetic retinopathy, diabetic macula edema and related vison loss. In: Eye vision, pp. 1–25 (2015)
Wild, S., Roglic, G., Green, A., Sicree, R., King, H.: Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care 27, 1047–1053 (2004). [PMID: 15111519]
Sopharak, A., Uyyanonvara, B., Barman, S., Williamson, T.H.: Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods. Comput. Med. Imaging Graph. 32, 720–727 (2008). https://doi.org/10.1016/j.compmedimag.2008.08.009
Reza, A.W., Eswaran, C., Hati, S.: Automatic tracing of optic disc and exudates from color fundus images fixed and variable thresholds. J. Med. Syst. 33, 73–80 (2009). https://doi.org/10.1007/s10916-008-9166-4
Sai Deepak, K., Siyaswamy, J.: Automatic assessment of macular edema from color retinal images. IEEE Trans. Med. Imaging 31(3), 766–776 (2012). https://doi.org/10.1109/TMI.2011.2178856
Zaidi, Z.Y., Akram, M.U., Tariq, A.: Retinal Image Analysis for Diagnosis of Macular Edema using Digital Fundus Images. In: IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (2013)
Jaya, T., Dheeba, J., Singh, N.A.: Detection of hard exudates in colour fundus images using fuzzy support vector machine-based expert system. J. Digital Imaging 28, 761–768 (2015). https://doi.org/10.1007/s10278-015-9793-5
Dutta, M.K., Ganguly, S., Srivastava, K., Ganguly, S., Parthasarathi, M., Burget, R., Masek, J.: An efficient grading algorithm for non-proliferative diabetic retinopathy using region based detection. In: IEEE 38th International Conference on Telecommunications and Signal Processing (TSP), pp. 743–747 (2015)
Giancardo, L., Meriaudeau, F., Karnowski, T.P., Yaqinli, G., Tobin Jr., S.W., Chaum, E.: Exudate based diabetic macula edema detection in fundus images using publicly available datasets. Med. Image Anal. 16(1), 216–226 (2012). https://doi.org/10.1016/j.media.2011.07.004
Ramya, M., Vijayprasath, S.: An effective analysis of macular edema severity for diabetic retinopathy. IJIRSET 3(3), 739–746 (2014). http://www.ijirset.com
Franklin, S.W., Rajan, S.E.: Diagnosis of Diabetic Retinopathy by employing image processing technique to detect exudates in retinal images. Inst. Eng. Technol. Image Process. 8(10), 601–609 (2014). https://doi.org/10.1049/iet-ipr.2013.0565
Tjandrasa, H., Putra, R.E., Wijaya, A.Y., Arieshanti, I.: Classification of non-proliferative diabetic retinopathy based on hard exudates using soft margin SVM. In: IEEE International Conference on Control System, Computing and Engineering, Penang, Malaysia, pp. 376–380 (2013)
Tan, J.H., Acharya, U.R., Bhandary, S.V., Chua, K.C., Sivaprasad, S.: Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network. J. Comput. Sci. 20, 70–79 (2017). http://dx.doi.org/10.1016/j.jocs.2017.02.006
Quellec, G., Charrière, K., Boudi, Y., Cochener, B., Lamard, M.: Deep image mining for diabetic retinopathy screening. Med. Image Anal. 39, 178–193 (2017). https://doi.org/10.1016/j.media.2017.04.012
Prentašic, P., Loncˇaric, S.: Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion. Comput. Methods Programs Biomed. 137, 281–292 (2016). http://dx.doi.org/10.1016/j.cmpb.2016.09.018
Mo, J., Zhang, L., Feng, Y.: Exudate-based diabetic macular edema recognition in retinal images using cascaded deep residual networks. Neurocomputing 290, 161–171 (2018). https://doi.org/10.1016/j.neucom.2018.02.035
Srinivasan, P.P., Kim, L.A., Mettu, P.S., Cousins, S.W., Comer, G.M., Izzat, J.A., Farsiu, S.: Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. In: Biomed. Optic. Express. (2014). https://doi.org/10.1364/boe.5.003568
Samagaio, G., Estévez, A., Moura, J., Novo, J., Fernández, M.I., Ortega, M.: Automatic macular edema identification and characterization using OCT images. Comput. Methods Programs Biomed. 63, 47–63 (2018). https://doi.org/10.1016/j.cmpb.2018.05.033
Kauppi, T., Kalesnykiene, V., Kamarainen, J.K., Lensu, L., Sorri, I., Uusitalo, H., Kälviäinen, H., Pietilä, J.: DIARETDB0 evaluation database and methodology for diabetic retinopathy algorithms. Technical report, Finland (2006). http://www.it.lut.fi/project/imageret/diaretdb0/doc/diaretdb0_techreport_v_1_1.pdf
Patwari, M.B., Manza, Dr. R.R., Rajput, Y.M., Saswade, M., Deshpande, N.K.: Automatic detection of retinal venous beading and tortuosity by using image processing techniques. IJCA (2014). ISBN: 973-93-80880-06-7
Senger, N., Dutta, M.K., Burget, R., Povoda, L.: Detection of diabetic macula edema in retina images using a region based method. In: IEEE TSP, pp. 412–415 (2015)
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Prabhu, N., Bhoir, D., Shanbhag, N., Rao, U. (2020). Exudate-Based Classification for Detection of Severity of Diabetic Macula Edema. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1230. Springer, Cham. https://doi.org/10.1007/978-3-030-52243-8_12
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