Detection of Hard Exudates in Retinopathy Images
Abstract The tissue layer located at the back of the eye is known as retina which converts the incoming light into nerve signals and those signals are sent to the brain for understanding. The damage onto the retina is termed as retinopathy and that may lead to vision weakening or vision loss. The hard exudates are small white or yellowish white deposits with their edges being clear and sharp. In the proposed methods we take color image of retina then extract the green channel of that image then apply top hat transformation and bottom hat transformation on that image. The DIARETDB1 and High-Resolution Fundus (HRF) databases are used for performance evaluation of the proposed method. The proposed technique achieves accuracy 97%, sensitivity 95%, and specificity 96% and it takes average 5.6135 second for detection of hard exudates in an image.
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Abhilash G. M., et al. 2017. Semi-automated quantification of hard exudates in color fundus photographs diagnosed with diabetic retinopathy in BMC Ophthalmol 17, 172, doi:10.1186/s12886-017-0563-7
https://doi.org/10.1186/s12886-017-0563-7
Akram F, et al. 2017. Active contours driven by local and global fitted image models for image segmentation robust to intensity inhomogeneity.PLoS One12(4):e0174813
https://doi.org/10.1371/journal.pone.0174813
Diana L. Shechtmanet al. 2007. Hypertension: More Than Meets the Eye in Review of Optometry, Vol.No: 144:09.
DIARETDB1 - Standard Diabetic Retinopathy Database,https://www.it.lut.fi/project/imageret/diaretdb1/index.html
E. Aswini, et al. 2013. Mathematical morphology and bottom-hat filtering approach for crack detection on relay surfaces in International Conference on Smart Structures and Systems - ICSSS'13, Chennai, pp. 108-113. - https://doi.org/10.1109/ICSSS.2013.6623011
High-Resolution Fundus (HRF) Image Database, https://www5.cs.fau.de/research/data/fundus-images/
IqbaldeepK., et al. 2016. Automated Identification of Hard Exudates and Cotton Wool Spots using Biomedical Image Processing, International Journal of Computer Science And Technology, V. 7, I.4
Kauppi T. et al. 2013. Constructing benchmark databases and proto-cols for medical image analysis: Diabetic retinopathy.Computational and Mathematical Methods in Medicine, pp-1 - 15. - https://doi.org/10.1155/2013/368514
Kavitha, D. et al. 2005. Automatic detection of optic disc and exudates in retinal images, inInternational Conference on Intelligent Sensing and Information Processing, vol., no., pp.501-506.
Punnolil, A. 2013.A novel approach for diagnosis and severity grading of diabetic maculopathy in International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2013, vol., no., pp.1230-1235, 22-25. - https://doi.org/10.1109/ICACCI.2013.6637353
Rajashekar D.et al. 2016.Comprehensive retinal image analysis for aggressive posterior retinopathy of prematurity.PLoS One11(10)
https://doi.org/10.1371/journal.pone.0163923
Shengchun L. et al. 2019. Automatic Detection of Hard Exudates in Colour Retinal Images Using Dynamic Threshold and SVM Classification: Algorithm Development and Evaluation, in BioMed Research International, doi: 10.1155/2019/3926930
https://doi.org/10.1155/2019/3926930
Suma G. et al. 2018. Detection of Neovascularization in Proliferative, Diabetic Retinopathy Fundus Images, The International Arab Journal of Information Technology, 15, 1000-1009.
U. M. Akramet al. 2011. Automated Detection of Dark and Bright Lesions in Retinal Images for Early Detection of Diabetic Retinopathy, Springer Science Business Media, LLC. doi: 10.1007/s10916-011-9802-2
https://doi.org/10.1007/s10916-011-9802-2
Verma, S. et al. 2019.Contactless palmprint verification system using 2-D Gabor filter and principal component analysis.The International Arab Journal of Information Technology.16(1), pp-23-29.
Abhilash G. M., et al. 2017. Semi-automated quantification of hard exudates in color fundus photographs diagnosed with diabetic retinopathy in BMC Ophthalmol 17, 172, doi:10.1186/s12886-017-0563-7
https://doi.org/10.1186/s12886-017-0563-7
Akram F, et al. 2017. Active contours driven by local and global fitted image models for image segmentation robust to intensity inhomogeneity.PLoS One12(4):e0174813
https://doi.org/10.1371/journal.pone.0174813
Diana L. Shechtmanet al. 2007. Hypertension: More Than Meets the Eye in Review of Optometry, Vol.No: 144:09.
DIARETDB1 - Standard Diabetic Retinopathy Database,https://www.it.lut.fi/project/imageret/diaretdb1/index.html
E. Aswini, et al. 2013. Mathematical morphology and bottom-hat filtering approach for crack detection on relay surfaces in International Conference on Smart Structures and Systems - ICSSS'13, Chennai, pp. 108-113. - https://doi.org/10.1109/ICSSS.2013.6623011
High-Resolution Fundus (HRF) Image Database, https://www5.cs.fau.de/research/data/fundus-images/
IqbaldeepK., et al. 2016. Automated Identification of Hard Exudates and Cotton Wool Spots using Biomedical Image Processing, International Journal of Computer Science And Technology, V. 7, I.4
Kauppi T. et al. 2013. Constructing benchmark databases and proto-cols for medical image analysis: Diabetic retinopathy.Computational and Mathematical Methods in Medicine, pp-1 - 15. - https://doi.org/10.1155/2013/368514
Kavitha, D. et al. 2005. Automatic detection of optic disc and exudates in retinal images, inInternational Conference on Intelligent Sensing and Information Processing, vol., no., pp.501-506.
Punnolil, A. 2013.A novel approach for diagnosis and severity grading of diabetic maculopathy in International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2013, vol., no., pp.1230-1235, 22-25. - https://doi.org/10.1109/ICACCI.2013.6637353
Rajashekar D.et al. 2016.Comprehensive retinal image analysis for aggressive posterior retinopathy of prematurity.PLoS One11(10)
https://doi.org/10.1371/journal.pone.0163923
Shengchun L. et al. 2019. Automatic Detection of Hard Exudates in Colour Retinal Images Using Dynamic Threshold and SVM Classification: Algorithm Development and Evaluation, in BioMed Research International, doi: 10.1155/2019/3926930
https://doi.org/10.1155/2019/3926930
Suma G. et al. 2018. Detection of Neovascularization in Proliferative, Diabetic Retinopathy Fundus Images, The International Arab Journal of Information Technology, 15, 1000-1009.
U. M. Akramet al. 2011. Automated Detection of Dark and Bright Lesions in Retinal Images for Early Detection of Diabetic Retinopathy, Springer Science Business Media, LLC. doi: 10.1007/s10916-011-9802-2
https://doi.org/10.1007/s10916-011-9802-2
Verma, S. et al. 2019.Contactless palmprint verification system using 2-D Gabor filter and principal component analysis.The International Arab Journal of Information Technology.16(1), pp-23-29.
Verma, S. B., & Yadav, A. K. (2019). Detection of Hard Exudates in Retinopathy Images. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 8(4), 41–48. https://doi.org/10.14201/ADCAIJ2019844148
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