{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T20:38:37Z","timestamp":1789591117795,"version":"build-2803163510"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T00:00:00Z","timestamp":1644364800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T00:00:00Z","timestamp":1644364800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1007\/s10278-022-00587-x","type":"journal-article","created":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T18:05:12Z","timestamp":1644429912000},"page":"496-513","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Fast and Robust Exudate Detection in Retinal Fundus Images Using Extreme Learning Machine Autoencoders and Modified KAZE Features"],"prefix":"10.1007","volume":"35","author":[{"given":"N Jagan","family":"Mohan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9341-3810","authenticated-orcid":false,"given":"R","family":"Murugan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tripti","family":"Goel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Parthapratim","family":"Roy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,2,9]]},"reference":[{"key":"#cr-split#-587_CR1.1","unstructured":"Gao, W., Shen, J., Zuo, J.: A novel method for detection of hard exudates from fundus images based on rbf and improved fcm. In: BIBE 2019"},{"key":"#cr-split#-587_CR1.2","unstructured":"The Third International Conference on Biological Information and Biomedical Engineering, pp. 1-6. VDE (2019)"},{"key":"587_CR2","doi-asserted-by":"crossref","unstructured":"Lokuarachchi, D., Gunarathna, K., Muthumal, L., Gamage, T.: Automated detection of exudates in retinal images. In: 2019 IEEE 15th International Colloquium on Signal Processing & Its Applications (CSPA), pp. 43\u201347. IEEE (2019)","DOI":"10.1109\/CSPA.2019.8696052"},{"key":"587_CR3","doi-asserted-by":"crossref","unstructured":"Long, S., Huang, X., Chen, Z., Pardhan, S., Zheng, D.: Automatic detection of hard exudates in color retinal images using dynamic threshold and svm classification: algorithm development and evaluation. BioMed research international 2019 (2019)","DOI":"10.1155\/2019\/3926930"},{"key":"587_CR4","doi-asserted-by":"crossref","unstructured":"Mohan, N.J., Murugan, R., Goel, T., Roy, P.: An improved accuracy rate in microaneurysms detection in retinal fundus images using non-local mean filter. In: International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, pp. 183\u2013193. Springer (2020)","DOI":"10.1007\/978-981-15-6315-7_15"},{"key":"587_CR5","doi-asserted-by":"crossref","unstructured":"Zulfahmi, R., Al\u00a0Bukhory, M.I., Triyuni, U., Utari, H., Laraswati, Y.: Techniques for exudate detection for diabetic retinopathy. In: 2019 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS), pp. 55\u201360. IEEE (2019)","DOI":"10.1109\/ICIMCIS48181.2019.8985226"},{"key":"587_CR6","doi-asserted-by":"crossref","unstructured":"Murugan, R., Albert, A.J., Nayak, D.K.: An automatic localization of microaneurysms in retinal fundus images. In: 2019 International Conference on Smart Structures and Systems (ICSSS), pp. 1\u20135. IEEE (2019)","DOI":"10.1109\/ICSSS.2019.8882858"},{"key":"587_CR7","doi-asserted-by":"crossref","unstructured":"Pereira, J., Colomer, A., Naranjo, V.: Comparison of local analysis strategies for exudate detection in fundus images. In: International Conference on Intelligent Data Engineering and Automated Learning, pp. 174\u2013183. Springer (2018)","DOI":"10.1007\/978-3-030-03493-1_19"},{"issue":"2","key":"587_CR8","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1007\/BF00920219","volume":"231","author":"R Phillips","year":"1993","unstructured":"Phillips, R., Forrester, J., Sharp, P.: Automated detection and quantification of retinal exudates. Graefe\u2019s archive for clinical and experimental ophthalmology 231(2), 90\u201394 (1993)","journal-title":"Graefe\u2019s archive for clinical and experimental ophthalmology"},{"key":"587_CR9","doi-asserted-by":"crossref","unstructured":"Soman, K., Ravi, D.: Detection of exudates in human fundus image with a comparative study on methods for the optic disk detection. In: International Conference on Information Communication and Embedded Systems (ICICES2014), pp. 1\u20135. IEEE (2014)","DOI":"10.1109\/ICICES.2014.7034010"},{"key":"587_CR10","doi-asserted-by":"crossref","unstructured":"Patil, P., Shettar, P., Narayankar, P., Patil, M.: An efficient method of detecting exudates in diabetic retinopathy: Using texture edge features. In: 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1188\u20131191. IEEE (2016)","DOI":"10.1109\/ICACCI.2016.7732206"},{"key":"587_CR11","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez, C.I., Niemeijer, M., Schulten, M.S., Abr\u00e0moff, M., van Ginneken, B.: Improving hard exudate detection in retinal images through a combination of local and contextual information. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 5\u20138. IEEE (2010)","DOI":"10.1109\/ISBI.2010.5490429"},{"issue":"5","key":"587_CR12","doi-asserted-by":"publisher","first-page":"86","DOI":"10.4236\/jbise.2017.105B010","volume":"10","author":"M Islam","year":"2017","unstructured":"Islam, M., Dinh, A.V., Wahid, K.A.: Automated diabetic retinopathy detection using bag of words approach. Journal of Biomedical Science and Engineering 10(5), 86\u201396 (2017)","journal-title":"Journal of Biomedical Science and Engineering"},{"key":"587_CR13","doi-asserted-by":"crossref","unstructured":"Chidambaram, N., Vijayan, D.: Detection of exudates in diabetic retinopathy. In: 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 660\u2013664. IEEE (2018)","DOI":"10.1109\/ICACCI.2018.8554923"},{"key":"587_CR14","doi-asserted-by":"crossref","unstructured":"Satyananda, V., Narayanaswamy, K.: Exudate extraction from fundus images. In: 2019 11th International Conference on Knowledge and Smart Technology (KST), pp. 94\u201398. IEEE (2019)","DOI":"10.1109\/KST.2019.8687565"},{"key":"587_CR15","doi-asserted-by":"crossref","unstructured":"Anggraeni, Z., Wibawa, H.A.: Detection of the emergence of exudate on the image of retina using extreme learning machine method. In: 2019 3rd International Conference on Informatics and Computational Sciences (ICICoS), pp. 1\u20136. IEEE (2019)","DOI":"10.1109\/ICICoS48119.2019.8982492"},{"key":"587_CR16","doi-asserted-by":"crossref","unstructured":"Rekhi, R.S., Issac, A., Dutta, M.K., Travieso, C.M.: Automated classification of exudates from digital fundus images. In: 2017 International Conference and Workshop on Bioinspired Intelligence (IWOBI), pp. 1\u20136. IEEE (2017)","DOI":"10.1109\/IWOBI.2017.7985527"},{"key":"587_CR17","doi-asserted-by":"crossref","unstructured":"Sengar, N., Dutta, M.K., Burget, R., Povoda, L.: Detection of diabetic macular edema in retinal images using a region based method. In: 2015 38th International Conference on Telecommunications and Signal Processing (TSP), pp. 412\u2013415. IEEE (2015)","DOI":"10.1109\/TSP.2015.7296294"},{"key":"587_CR18","doi-asserted-by":"publisher","first-page":"176912","DOI":"10.1109\/ACCESS.2019.2957776","volume":"7","author":"X Guo","year":"2019","unstructured":"Guo, X., Lu, X., Liu, Q., Che, X.: Emfn: Enhanced multi-feature fusion network for hard exudate detection in fundus images. IEEE Access 7, 176912\u2013176920 (2019)","journal-title":"IEEE Access"},{"issue":"2","key":"587_CR19","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1109\/TBME.2003.820400","volume":"51","author":"H Li","year":"2004","unstructured":"Li, H., Chutatape, O.: Automated feature extraction in color retinal images by a model based approach. IEEE Transactions on biomedical engineering 51(2), 246\u2013254 (2004)","journal-title":"IEEE Transactions on biomedical engineering"},{"issue":"3","key":"587_CR20","doi-asserted-by":"publisher","first-page":"1052","DOI":"10.1016\/j.cmpb.2012.06.006","volume":"108","author":"D Youssef","year":"2012","unstructured":"Youssef, D., Solouma, N.H.: Accurate detection of blood vessels improves the detection of exudates in color fundus images. Computer methods and programs in biomedicine 108(3), 1052\u20131061 (2012)","journal-title":"Computer methods and programs in biomedicine"},{"key":"587_CR21","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.compbiomed.2014.09.001","volume":"54","author":"B Harangi","year":"2014","unstructured":"Harangi, B., Hajdu, A.: Automatic exudate detection by fusing multiple active contours and regionwise classification. Computers in biology and medicine 54, 156\u2013171 (2014)","journal-title":"Computers in biology and medicine"},{"issue":"02","key":"587_CR22","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1142\/S0218339015500102","volume":"23","author":"G Mahendran","year":"2015","unstructured":"Mahendran, G., Dhanasekaran, R.: Detection and localization of retinal exudates for diabetic retinopathy. Journal of Biological Systems 23(02), 195\u2013212 (2015)","journal-title":"Journal of Biological Systems"},{"key":"587_CR23","doi-asserted-by":"publisher","first-page":"11946","DOI":"10.1109\/ACCESS.2018.2890426","volume":"7","author":"K Wisaeng","year":"2019","unstructured":"Wisaeng, K., Sa-Ngiamvibool, W.: Exudates detection using morphology mean shift algorithm in retinal images. IEEE Access 7, 11946\u201311958 (2019)","journal-title":"IEEE Access"},{"key":"587_CR24","doi-asserted-by":"crossref","unstructured":"Rekhi, R.S., Issac, A., Dutta, M.K.: Automated detection and grading of diabetic macular edema from digital colour fundus images. In: 2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON), pp. 482\u2013486. IEEE (2017)","DOI":"10.1109\/UPCON.2017.8251096"},{"key":"587_CR25","doi-asserted-by":"crossref","unstructured":"Agurto\u00a0et al., C.: A multiscale optimization approach to detect exudates in the macula. IEEE Journal Of Biomedical and health informatics 18(4), 1328\u20131336 (2014)","DOI":"10.1109\/JBHI.2013.2296399"},{"key":"587_CR26","doi-asserted-by":"crossref","unstructured":"Li, W., Qian, Y., Loomes, M., Gao, X.: The application of kaze features to the classification echocardiogram videos. In: International Workshop on Multimodal Retrieval in the Medical Domain, pp. 61\u201372. Springer (2015)","DOI":"10.1007\/978-3-319-24471-6_6"},{"key":"587_CR27","doi-asserted-by":"crossref","unstructured":"Sanchez-Morillo, D., Gonz\u00e1lez, J., Garc\u00eda-Rojo, M., Ortega, J.: Classification of breast cancer histopathological images using kaze features. In: International Conference on Bioinformatics and Biomedical Engineering, pp. 276\u2013286. Springer (2018)","DOI":"10.1007\/978-3-319-78759-6_26"},{"issue":"3","key":"587_CR28","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1007\/s11554-018-0793-9","volume":"15","author":"JM Haut","year":"2018","unstructured":"Haut, J.M., Paoletti, M.E., Plaza, J., Plaza, A.: Fast dimensionality reduction and classification of hyperspectral images with extreme learning machines. Journal of Real-Time Image Processing 15(3), 439\u2013462 (2018)","journal-title":"Journal of Real-Time Image Processing"},{"key":"587_CR29","unstructured":"Kauppi\u00a0et al., T.: Diaretdb0: Evaluation database and methodology for diabetic retinopathy algorithms. Machine Vision and Pattern Recognition Research Group, Lappeenranta University of Technology, Finland 73, 1\u201317 (2006)"},{"key":"587_CR30","unstructured":"Kalesnykiene\u00a0et al., V.: the diaretdb1 diabetic retinopathy database and evaluation protocol (2012)"},{"key":"587_CR31","doi-asserted-by":"crossref","unstructured":"Decenci\u00e8re\u00a0et al., E.: Teleophta: Machine learning and image processing methods for teleophthalmology. Irbm 34(2), 196\u2013203 (2013)","DOI":"10.1016\/j.irbm.2013.01.010"},{"key":"587_CR32","doi-asserted-by":"crossref","unstructured":"Decenci\u00e8re\u00a0et al, E.: Feedback on a publicly distributed image database: the messidor database 33(3), 231\u2013234 (2014)","DOI":"10.5566\/ias.1155"},{"issue":"9","key":"587_CR33","doi-asserted-by":"publisher","first-page":"1813","DOI":"10.1016\/S0031-3203(00)00109-6","volume":"34","author":"J Weickert","year":"2001","unstructured":"Weickert, J.: Efficient image segmentation using partial differential equations and morphology. Pattern Recognition 34(9), 1813\u20131824 (2001)","journal-title":"Pattern Recognition"},{"key":"587_CR34","doi-asserted-by":"crossref","unstructured":"Giancardo, L., Meriaudeau, F., Karnowski, T.P., Li, Y., Tobin, K.W., Chaum, E.: Automatic retina exudates segmentation without a manually labelled training set. In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1396\u20131400. IEEE (2011)","DOI":"10.1109\/ISBI.2011.5872661"}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-022-00587-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-022-00587-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-022-00587-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T05:45:31Z","timestamp":1726638331000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-022-00587-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,9]]},"references-count":35,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["587"],"URL":"https:\/\/doi.org\/10.1007\/s10278-022-00587-x","relation":{},"ISSN":["0897-1889","1618-727X"],"issn-type":[{"value":"0897-1889","type":"print"},{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,9]]},"assertion":[{"value":"7 August 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 November 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 February 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}