DIABETIC RETINOPATHY LESION DETECTION FROM MULTISPECTRAL RETINAL IMAGES THROUGH NEURAL NETWORK
DOI:
https://doi.org/10.53808/KUS.2020.17.1and2.2001-EKeywords:
Detection, Classification, Multispectral Image, Neural network, Diabetic RetinopathyAbstract
Diabetic Retinopathy (DR) is one of the fastest growing dysfunctions of human retina. Significant research has been conducted using RGB fundus imaging for automatic detection of retinal lesions affected by DR. However, due to only three imaging bands, the accuracy from RGB fundus images is unlikely to improve any further. In contrast to RGB imaging, multispectral imaging has the key advantage of multiple narrow wavelength bands that can be used as spectral features to improve the detection accuracy. Nevertheless, the inter and intra-retinal variation of color, contrast, and illumination is a challenge to process the multispectral images. In this study, a complete framework is proposed to develop and evaluate methods for automatic detection of DR lesions. A multispectral retinal image database, DIARETSPECDB1, is investigated in order to detect the most common DRs such as Microaneurysms (MA), Hard Exudates (HE) and Hemorrhages (HEM). The reflectance values of the spectral bands are used as features of a three-layer basic neural network (NN) to determine the baseline performance of multispectral data instead of any advanced model. According to the results, the model outperforms existing technique producing overall accuracy 94.5%, and the obtained specificity/sensitivity is 0.95/0.89, 0.97/0.89, and 0.88/0.84 for MAs, HEs and HEMs, respectively.
Downloads
References
Acharya, U. R., Lim, C. M., Ng, E. Y. K., Chee, C., & Tamura, T. (2009). Computer-based detection
of diabetes retinopathy stages using digital fundus images. Proceedings of the institution of
mechanical engineers, part H: journal of engineering in medicine, 223(5), 545-553. doi:
1243/09544119JEIM486
Calcagni, A., Gibson, J. M., Styles, I. B., Claridge, E., &Orihuela-Espina, F. (2011). Multispectral
retinal image analysis: a novel non-invasive tool for retinal imaging. Eye, 25(12),
–1569. doi: 10.1038/eye.2011.202 DOI: https://doi.org/10.1038/eye.2011.202
Drake, L. (2007). Prevention of Blindness from Diabetes Mellitus--Report of a WHO Consultation in DOI: https://doi.org/10.7748/ns2007.04.21.32.30.b604
Geneva, Switzerland, 9-11 November 2005. Nursing Standard, 21(32), 30-31.
Fawcett, T. (2006). An Introduction to ROC Analysis. Pattern Recognition Letters. 27 (8): 861–874. DOI: https://doi.org/10.1016/j.patrec.2005.10.010
Fält, P., Hiltunen, J., Hauta-Kasari, M., Sorri, I., Kalesnykiene, V., & Uusitalo, H. (2009, June).
Extending diabetic retinopathy imaging from color to spectra. In Scandinavian Conference on Image Analysis (pp. 149-158). Springer, Berlin, Heidelberg. doi:10.1007/978-3-642-02230-2_16. DOI: https://doi.org/10.1007/978-3-642-02230-2_16
García, M., López, M. I., Álvarez, D., & Hornero, R. (2010). Assessment of four neural network
based classifiers to automatically detect red lesions in retinal images. Medical engineering
& physics, 32(10), 1085-1093. doi: 10.1016/j.medengphy.2010.07.014. DOI: https://doi.org/10.1016/j.medengphy.2010.07.014
Gardner, G. G., Keating, D., Williamson, T. H., & Elliott, A. T. (1996). Automatic detection of
diabetic retinopathy using an artificial neural network: a screening tool. British journal
of Ophthalmology, 80(11), 940-944. doi: 10.1136/bjo.80.11.940. DOI: https://doi.org/10.1136/bjo.80.11.940
He, Y., Jiao, W., Shi, Y., Lian, J., Zhao, B., Zou, W., … Zheng, Y. (2020). Segmenting Diabetic
Retinopathy Lesions in Multispectral Images Using Low-Dimensional SpatialSpectral Matrix Representation. IEEE Journal of Biomedical and Health Informatics, 24(2), 493–502. doi: 10.1109/jbhi.2019.2912668. DOI: https://doi.org/10.1109/JBHI.2019.2912668
Kauppi, T. (2010). Eye fundus image analysis for automatic detection of diabetic retinopathy. ISBN:
, PhD Thesis 176, Lappeenranta University of Technology.
Kauppi, T., Kämäräinen, J. K., Lensu, L., Kalesnykiene, V., Sorri, I., Uusitalo, H., &Kälviäinen, H.
(2013). Constructing benchmark databases and protocols for medical image analysis:
Diabetic retinopathy. Computational and Mathematical Methods in Medicine, Vol. 2013, DOI: https://doi.org/10.1155/2013/932794
Article ID 368514, https://doi.org/10.1155/2013/368514. DOI: https://doi.org/10.1155/2013/368514
Koronyo-Hamaoui, M., Koronyo, Y., Ljubimov, A. V., Miller, C. A., Ko, M. K., Black, K. L., ... &
Farkas, D. L. (2011). Identification of amyloid plaques in retinas from Alzheimer's
patients and noninvasive in vivo optical imaging of retinal plaques in a mouse
model. Neuroimage, 54, S204-S217. doi: 10.1016/j.neuroimage.2010.06.020. DOI: https://doi.org/10.1016/j.neuroimage.2010.06.020
Lu, G., & Fei, B. (2014). Medical hyperspectral imaging: a review. Journal of Biomedical Optics, 19(1),
doi: 10.1117/1.JBO.19.1.010901. DOI: https://doi.org/10.1117/1.JBO.19.1.010901
Liu, Z., &Zerubia, J. (2015). Skin image illumination modeling and chromophore identification for DOI: https://doi.org/10.1088/0031-9155/60/9/3415
melanoma diagnosis. Physics in Medicine & Biology, 60(9), 3415.
Mookiah, M. R. K., Acharya, U. R., Chua, C. K., Lim, C. M., Ng, E. Y. K.,& Laude, A. (2013).
Computer-aided diagnosis of diabetic retinopathy: A review. Computers in Biology and
Medicine, 43(12), 2136-2155. doi: 10.1016/j.compbiomed.2013.10.007. DOI: https://doi.org/10.1016/j.compbiomed.2013.10.007
Møller, M. F. (1993). A scaled conjugate gradient algorithm for fast supervised learning. Neural
Networks, 6(4), 525-533. doi: 10.1016/S0893-6080(05)80056-5. DOI: https://doi.org/10.1016/S0893-6080(05)80056-5
Abdullah, M., Fraz, M. M., & Barman, S. A. (2016). Localization and segmentation of optic disc in
retinal images using circular Hough transform and grow-cut algorithm. PeerJ, 4,
e2003.
Nayak, J., Bhat, P. S., Acharya, R., Lim, C. M., &Kagathi, M. (2008). Automated identification of
diabetic retinopathy stages using digital fundus images. Journal of Medical Systems, 32(2),
-115. doi: 10.1007/s10916-007-9113-9. DOI: https://doi.org/10.1007/s10916-007-9113-9
Niemeijer, M., Van Ginneken, B., Staal, J., Suttorp-Schulten, M. S., &Abràmoff, M. D. (2005).
Automatic detection of red lesions in digital color fundus photographs. IEEE
Transactions on Medical Imaging, 24(5), 584-592. doi: 10.1109/TMI.2005.843738. DOI: https://doi.org/10.1109/TMI.2005.843738
Niemeijer, M., van Ginneken, B., Russell, S. R., Suttorp-Schulten, M. S., & Abramoff, M. D. (2007).
Automated detection and differentiation of drusen, exudates, and cotton-wool spots
in digital color fundus photographs for diabetic retinopathy diagnosis. Investigative
Ophthalmology & Visual Science, 48(5), 2260-2267. doi: 10.1167/iovs.06-0996. DOI: https://doi.org/10.1167/iovs.06-0996
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Khulna University Studies
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
