Hard exudate detection in retinal fundus images using supervised learning

© 2019, Springer-Verlag London Ltd., part of Springer Nature. The patients with diabetes have a chance to develop diabetic retinopathy (DR) which affects to the eyes. DR can cause blindness if the patients do not control diabetes. The patients with DR will have an impairment of metabolism of glucose...

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Main Authors: Nipon Theera-Umpon, Ittided Poonkasem, Sansanee Auephanwiriyakul, Direk Patikulsila
Format: Journal
Published: 2019
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http://cmuir.cmu.ac.th/jspui/handle/6653943832/66640
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-666402019-09-16T12:50:36Z Hard exudate detection in retinal fundus images using supervised learning Nipon Theera-Umpon Ittided Poonkasem Sansanee Auephanwiriyakul Direk Patikulsila Computer Science © 2019, Springer-Verlag London Ltd., part of Springer Nature. The patients with diabetes have a chance to develop diabetic retinopathy (DR) which affects to the eyes. DR can cause blindness if the patients do not control diabetes. The patients with DR will have an impairment of metabolism of glucose causing a high glucose level in blood vessel called hyperglycemia. It leads to abnormal blood vessel and ultimately results in leakage of blood or fluid like lipoproteins, which are deposited under macular edema called hard exudates. They are normally white or yellowish-white with margins. Hard exudates are often arranged in clumps or circinate rings and located in the outer layer of the retina. The aim of this research was to detect hard exudates by applying several image processing techniques and classify them by using supervised learning methods including support vector machines and some neural network approaches, i.e., multilayer perceptron (MLP) network, hierarchical adaptive neurofuzzy inference system (hierarchical ANFIS), and convolutional neuron networks. DIARETDB1 which contains 89 fundus images is exploited as a dataset for evaluation. Hard exudate candidates are extracted by morphological techniques and classified by the classifiers trained by extracted patches with the corresponding ground truths. The tenfold cross-validation is applied to assure the generalization of the results. The proposed method achieves the area under the curve (AUC) of 0.998 when the MLP network is applied. The AUCs for all four classifiers are more than 0.95. This shows that the combination of image processing techniques and suitable classifiers can perform very well in hard exudate detection problem. 2019-09-16T12:50:36Z 2019-09-16T12:50:36Z 2019-01-01 Journal 14333058 09410643 2-s2.0-85070215221 10.1007/s00521-019-04402-7 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85070215221&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/66640
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Computer Science
spellingShingle Computer Science
Nipon Theera-Umpon
Ittided Poonkasem
Sansanee Auephanwiriyakul
Direk Patikulsila
Hard exudate detection in retinal fundus images using supervised learning
description © 2019, Springer-Verlag London Ltd., part of Springer Nature. The patients with diabetes have a chance to develop diabetic retinopathy (DR) which affects to the eyes. DR can cause blindness if the patients do not control diabetes. The patients with DR will have an impairment of metabolism of glucose causing a high glucose level in blood vessel called hyperglycemia. It leads to abnormal blood vessel and ultimately results in leakage of blood or fluid like lipoproteins, which are deposited under macular edema called hard exudates. They are normally white or yellowish-white with margins. Hard exudates are often arranged in clumps or circinate rings and located in the outer layer of the retina. The aim of this research was to detect hard exudates by applying several image processing techniques and classify them by using supervised learning methods including support vector machines and some neural network approaches, i.e., multilayer perceptron (MLP) network, hierarchical adaptive neurofuzzy inference system (hierarchical ANFIS), and convolutional neuron networks. DIARETDB1 which contains 89 fundus images is exploited as a dataset for evaluation. Hard exudate candidates are extracted by morphological techniques and classified by the classifiers trained by extracted patches with the corresponding ground truths. The tenfold cross-validation is applied to assure the generalization of the results. The proposed method achieves the area under the curve (AUC) of 0.998 when the MLP network is applied. The AUCs for all four classifiers are more than 0.95. This shows that the combination of image processing techniques and suitable classifiers can perform very well in hard exudate detection problem.
format Journal
author Nipon Theera-Umpon
Ittided Poonkasem
Sansanee Auephanwiriyakul
Direk Patikulsila
author_facet Nipon Theera-Umpon
Ittided Poonkasem
Sansanee Auephanwiriyakul
Direk Patikulsila
author_sort Nipon Theera-Umpon
title Hard exudate detection in retinal fundus images using supervised learning
title_short Hard exudate detection in retinal fundus images using supervised learning
title_full Hard exudate detection in retinal fundus images using supervised learning
title_fullStr Hard exudate detection in retinal fundus images using supervised learning
title_full_unstemmed Hard exudate detection in retinal fundus images using supervised learning
title_sort hard exudate detection in retinal fundus images using supervised learning
publishDate 2019
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85070215221&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/66640
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