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Reseach Article

Study of Macular Degeneration with Respect to Artifacts on Retinal Images

Published on February 2013 by Srikanth Prabhu, Chandan Chakraborty, R. N. Banerjee, A. K. Ray
International Conference on Electronic Design and Signal Processing
Foundation of Computer Science USA
ICEDSP - Number 2
February 2013
Authors: Srikanth Prabhu, Chandan Chakraborty, R. N. Banerjee, A. K. Ray
a143943b-bd82-4b75-b172-f00e07b78f17

Srikanth Prabhu, Chandan Chakraborty, R. N. Banerjee, A. K. Ray . Study of Macular Degeneration with Respect to Artifacts on Retinal Images. International Conference on Electronic Design and Signal Processing. ICEDSP, 2 (February 2013), 1-10.

@article{
author = { Srikanth Prabhu, Chandan Chakraborty, R. N. Banerjee, A. K. Ray },
title = { Study of Macular Degeneration with Respect to Artifacts on Retinal Images },
journal = { International Conference on Electronic Design and Signal Processing },
issue_date = { February 2013 },
volume = { ICEDSP },
number = { 2 },
month = { February },
year = { 2013 },
issn = 0975-8887,
pages = { 1-10 },
numpages = 10,
url = { /specialissues/icedsp/number2/10352-1010/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 International Conference on Electronic Design and Signal Processing
%A Srikanth Prabhu
%A Chandan Chakraborty
%A R. N. Banerjee
%A A. K. Ray
%T Study of Macular Degeneration with Respect to Artifacts on Retinal Images
%J International Conference on Electronic Design and Signal Processing
%@ 0975-8887
%V ICEDSP
%N 2
%P 1-10
%D 2013
%I International Journal of Computer Applications
Abstract

The role of segmentation in image processing is to separate foreground from background. In this process, the features become clearly visible when appropriate filters are applied on the image. In this paper emphasis has been laid on segmentation of biometric retinal images to filter out the vessels explicitly for evaluating the bifurcation points and features for diabetic retinopathy. Segmentation on images is performed by calculating ridges or morphology. Ridges are those areas in the images where there is sharp contrast in features. Morphology targets the features using structuring elements. Structuring elements are of different shapes like disk, line which is used for extracting features of those shapes. When segmentation was performed on retinal images problems were encountered during image pre processing stage. Also edge detection techniques have been deployed to find out the contours of the retinal images. After the segmentation has been performed, it has been seen that artifacts of the retinal images have been minimal when ridge based segmentation technique was deployed. In the field of Health Care Management, image segmentation has an important role to play as it determines whether a person is normal or having any disease specially diabetes. In India alone more than 5 million people are affected by diabetes. During the process of segmentation, diseased features are classified as diseased one's or artifacts. The problem comes when artifacts are classified as diseased one's. This results in misclassification which has been discussed in the analysis section. Macular Degeneration is one of the diseases in diabetic retinopathy which will never be classified as artifacts due to the size of exudates. In this paper an attempt has been made to evaluate macular degeneration.

References
  1. Subhasish, C. , Shankar, C. , Norman, K. , et-al(1989). Detection of Blood Vessels in Retinal Images using Two Dimensional Matched Filters, IEEE Transactions on Medical Imaging, Vol. 8 , No 3, pp 34-45.
  2. . Luo ,G. , Opas, C. , Shankar, M. K. (2009). Detection and Measurement of Retinal Vessels in Fundus Images using Amplitude Modified Second Order Gaussian Filter, IEEE Transactions on Biomedical Engineering, Vol. 49, No 2, pp23-32.
  3. . Axel, P. , Stefan, B. , Peter, D. , et-al(1998). Mapping the Human Retina, IEEE, Transactions on Medical Imaging, Vol. 17, No 4, pp 45-56.
  4. . Fredric, Z. , Jean, C. K. (2009). Segmentation of Vessel like Patterns using Mathematical Morphology and Curvature Evaluation, IEEE Transactions on Image Processing, Vol. 10, No 7, pp 56-67.
  5. . Stiliyan, N. K. , Joes, S. , Bart, M. , et-al. (2001). A Computational Method for Segmenting Topological Point - Sets and Application to Image Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No 5, pp 34-42.
  6. . Xiaoi, J. , Daniel, M. (2009). Adaptive Local Thresholding by Verification based Multi Threshold Probing with application to Vessel Detection in Retinal Images, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No 1, pp 23-30.
  7. . Joes, S. , Michael, D. A. , et-al. (2004). Ridge based Vessel Segmentation Color Images of the Retina, IEEE Transactions on Medical Imaging, Vol. 23, No 4, pp 23-29.
  8. . Jayadevan, R, Jayant, V K. , et-al. (2006). A New Ridge Orientation based Method of Computation for Feature Extraction from Fingerprint Images, Journal of Medical Imaging, World Academy of Science, Engineering and Technology, Vol 22, No 3, pp 45-56.
  9. . Vratislav, H. , et-al. (2003). Automated Landmarks Detection for Retinal Image Registration, Doctoral Thesis, Supervisor Radim Kolar.
  10. . Zakaria, B. S. , Laurent, D. C. , Gérard, M. , and Gabriel, C. (2010). A New Approach of Geodesic Reconstruction for Drusen Segmentation in Eye Fundus Images, IEEE Transactions on Medical Imaging, Vol 20, No. 12, pp 34-45.
  11. Oliver, F. , Acharya, U. R. , Ng, E. Y. K. , Ng, K. H. ,Jasjit, S. S. (2010). Algorithms for the Automated Detection of Diabetic Retinopathy Using Digital Fundus Images: A Review,Journal of Medical Systems, Vol 23, No 5, pp 23-34.
  12. Qin, L, Jane, Y, Lei, Z, Prabir, B(2008). Automated Retinal Vessel Segmentation Using Multiscale Analysis and Adaptive Thresholding,Proceedings of IEEE Conference.
  13. Harihar, N. , James, M. B. , Bahram, K. , Badrinath, R. (2010), Automatic Identification of Retinal Arteries and Veins From Dual-Wavelength Images Using Structural and Functional Features,IEEE Transactions on Information Technology and Biomedicine, Vol 54, No 8, pp 1427-1435.
  14. Carmen, A. L. , Domenico, T. , Emanuele, T. (2010). Retinal Vessel Segmentation Using AdaBoost,IEEE Transaction on Information Technology and Biomedicine, Vol 14, No 5 ,pp 1267-1274.
  15. Harihar, N. , Vijay, M. , James, M. B. , and Badrinath, R. (2011). Improved Detection of the Central Reflex in Retinal Vessels using a Generalized Dual-Gaussian Model and Robust Hypothesis Testing,IEEE Transaction on Information Science , Vol 12, No 3, pp 405-410.
  16. Aliaa, A. A. Y. , Atef, Z. G. , Amr, A. S. Abdel, R. G. (2010). Optic Disc Detection From Normalized Digital Fundus Images by Means of a VesselsDirection Matched Filter, IEEE Transaction on Information Technology and Biomedicine.
Index Terms

Computer Science
Information Sciences

Keywords

Pupil Sclera Limbus Diabetes Micro-aneurysms Exudates Gabor Log Bifurcation Sobel Gray Level Decision Tree Knn