Diabetic Retinopathy and Age related Macular Degeneration are two major retinal diseases which are creating serious concern in today’s age. Detection of preliminary signs of abnormalities due to these diseases is hard and time consuming for the ophthalmologists as the abnormal objects are very fine and small in size. As early detection of abnormalities can prevent permanent vision loss, a semi automated system is developed to detect the affected portion of retina and is tested with some retinal images. A training image set is used to train a support vector machine classifier. A test image set is given to the classifier for automatic detection of disease type. Efficiency of the classifier is tested comparing the actual value and predicted value by the classifier.
| Published in | Science Journal of Circuits, Systems and Signal Processing (Volume 5, Issue 1) |
| DOI | 10.11648/j.cssp.20160501.11 |
| Page(s) | 1-7 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2016. Published by Science Publishing Group |
Hard Exudates (HE), Soft Exudates (SE), Micro Aneurysms (MA), Hemorrhages (HAM), Fuzzy C Means (FCM)
| [1] | A. Roy Chowdhury, R. Saha, S. Banerjee, Detection of different types of Diabetic Retinopathy and Age related Macular Degeneration, Computer Science and Application (CSA) 2015 Proceedings, pp: 71-76. |
| [2] | M. Niemeijer, B. V. Ginneken, S. R. Russell, M. S. A. Suttorp-Schulten, and M. D. Abramoff, Automated Detection and Differentiation of Drusen, Exudates, and Cotton-Wool Spots in Digital Color Fundus Photographs for Diabetic Retinopathy Diagnosis, Vol. 48, No. 5 (Invest Ophthalmol Visual Science., May 2007). |
| [3] | A. Sopharak, M. N. Dailey, B. Uyyanonvara, S. Barman, T. Williamson, K. T. Nwe and Y. A. Moe, Machine learning approach to automatic exudate detection in retinal images from diabetic patients, Vol. 57(2), pp:124-135, 2009 (Journal of Modern Optics). |
| [4] | S. Banerjee, A. Roy Chowdhury, Case Based Reasoning in the Detection of Retinal Abnormalities using Decision Trees, International Conference on Information and Communication Technologies (ICICT 2014). |
| [5] | A. Sopharak, B. Uyyanonvara and S. Barman, Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering, Vol. 9, pp: 2148-2161, March 2009 (Sensors (1424-8220)). |
| [6] | T. Akila, G. Kavitha, Detection and Classification of Hard Exudates in Human Retinal Fundus Images Using Clustering and Random Forest Methods, Vol. 4, Special Issue 2, April (International Journal of Emerging Technology and Advanced Engineering, 2014). |
| [7] | C. Aravind, M. PonniBala, S. Vijayachitra, Automatic Detection of Microaneurysms and Classification of Diabetic Retinopathy Images using SVM Technique, (International Journal of Computer Applications (0975–8887)). |
| [8] | K. Wisaeng, N. Hiransakolwong, E. Pothiruk, Automatic Detection of Retinal Exudates using a Support Vector Machine, Vol. 32, No. 1/2013, pp: 33-42 (Applied Medical Informatics, 2013). |
| [9] | G. B. Kande, T. S. Savithri and P. V. Subbaiah, Automatic Detection of Microaneurysms and Hemorrhages in Digital Fundus Images, Vol 23, No 4 (August), pp 430-437 (Journal of Digital Imaging, 2010). |
| [10] | B. Ramasubramanian, G. Prabhakar, An Early Screening System for the Detection of Diabetic Retinopathy using Image Processing, Vol. 61– No.15, January 2013(International Journal of Computer Applications (0975–8887)). |
| [11] | B. Ramasubramanian, G. Mahendran, An Efficient Integrated Approach for the Detection of Exudates and Diabetic Maculopathy in Colour fundus Images, Advanced Computing: An International Journal ( ACIJ ), Vol.3, No.5, September 2012, DOI: 10.5121/acij.2012.3509 83. |
| [12] | Data Mining: Practical Machine Learning Tools and Techniques, Ian H. Witten, Eibe Frank, Mark A. Hall. |
APA Style
Amrita Roy Chowdhury, Sreeparna Banerjee. (2016). Detection of Abnormalities of Retina Due to Diabetic Retinopathy and Age Related Macular Degeneration Using SVM. Science Journal of Circuits, Systems and Signal Processing, 5(1), 1-7. https://doi.org/10.11648/j.cssp.20160501.11
ACS Style
Amrita Roy Chowdhury; Sreeparna Banerjee. Detection of Abnormalities of Retina Due to Diabetic Retinopathy and Age Related Macular Degeneration Using SVM. Sci. J. Circuits Syst. Signal Process. 2016, 5(1), 1-7. doi: 10.11648/j.cssp.20160501.11
AMA Style
Amrita Roy Chowdhury, Sreeparna Banerjee. Detection of Abnormalities of Retina Due to Diabetic Retinopathy and Age Related Macular Degeneration Using SVM. Sci J Circuits Syst Signal Process. 2016;5(1):1-7. doi: 10.11648/j.cssp.20160501.11
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.cssp.20160501.11,
author = {Amrita Roy Chowdhury and Sreeparna Banerjee},
title = {Detection of Abnormalities of Retina Due to Diabetic Retinopathy and Age Related Macular Degeneration Using SVM},
journal = {Science Journal of Circuits, Systems and Signal Processing},
volume = {5},
number = {1},
pages = {1-7},
doi = {10.11648/j.cssp.20160501.11},
url = {https://doi.org/10.11648/j.cssp.20160501.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cssp.20160501.11},
abstract = {Diabetic Retinopathy and Age related Macular Degeneration are two major retinal diseases which are creating serious concern in today’s age. Detection of preliminary signs of abnormalities due to these diseases is hard and time consuming for the ophthalmologists as the abnormal objects are very fine and small in size. As early detection of abnormalities can prevent permanent vision loss, a semi automated system is developed to detect the affected portion of retina and is tested with some retinal images. A training image set is used to train a support vector machine classifier. A test image set is given to the classifier for automatic detection of disease type. Efficiency of the classifier is tested comparing the actual value and predicted value by the classifier.},
year = {2016}
}
TY - JOUR T1 - Detection of Abnormalities of Retina Due to Diabetic Retinopathy and Age Related Macular Degeneration Using SVM AU - Amrita Roy Chowdhury AU - Sreeparna Banerjee Y1 - 2016/05/19 PY - 2016 N1 - https://doi.org/10.11648/j.cssp.20160501.11 DO - 10.11648/j.cssp.20160501.11 T2 - Science Journal of Circuits, Systems and Signal Processing JF - Science Journal of Circuits, Systems and Signal Processing JO - Science Journal of Circuits, Systems and Signal Processing SP - 1 EP - 7 PB - Science Publishing Group SN - 2326-9073 UR - https://doi.org/10.11648/j.cssp.20160501.11 AB - Diabetic Retinopathy and Age related Macular Degeneration are two major retinal diseases which are creating serious concern in today’s age. Detection of preliminary signs of abnormalities due to these diseases is hard and time consuming for the ophthalmologists as the abnormal objects are very fine and small in size. As early detection of abnormalities can prevent permanent vision loss, a semi automated system is developed to detect the affected portion of retina and is tested with some retinal images. A training image set is used to train a support vector machine classifier. A test image set is given to the classifier for automatic detection of disease type. Efficiency of the classifier is tested comparing the actual value and predicted value by the classifier. VL - 5 IS - 1 ER -
Email: