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Retinal Image Segmentation by using Texture-based Gabor Filter Optimized by Gradient Descent Followed by Evolutionary Algorithm

by Upendra Kumar, Omjeet Singh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 148 - Number 4
Year of Publication: 2016
Authors: Upendra Kumar, Omjeet Singh
10.5120/ijca2016911096

Upendra Kumar, Omjeet Singh . Retinal Image Segmentation by using Texture-based Gabor Filter Optimized by Gradient Descent Followed by Evolutionary Algorithm. International Journal of Computer Applications. 148, 4 ( Aug 2016), 37-47. DOI=10.5120/ijca2016911096

@article{ 10.5120/ijca2016911096,
author = { Upendra Kumar, Omjeet Singh },
title = { Retinal Image Segmentation by using Texture-based Gabor Filter Optimized by Gradient Descent Followed by Evolutionary Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2016 },
volume = { 148 },
number = { 4 },
month = { Aug },
year = { 2016 },
issn = { 0975-8887 },
pages = { 37-47 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume148/number4/25749-2016911096/ },
doi = { 10.5120/ijca2016911096 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:52:28.703916+05:30
%A Upendra Kumar
%A Omjeet Singh
%T Retinal Image Segmentation by using Texture-based Gabor Filter Optimized by Gradient Descent Followed by Evolutionary Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 148
%N 4
%P 37-47
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Segmentation and localization of fundus image is a crucial step of pathologies in diagnosing the retinal diseases. Swelling in different parts of vasculature, as change in width along blood vessels and tortuosity may lead to eye-blindness. This process can be utilized in automated screening of the patients suffering from diabetic retinopathy. An attempt was made to apply texture based Gabor filter which captures the band-pass filter bank characteristics of the eye and its output was used to detect the discontinuities and derive statistical properties helping in segmenting and classifying retinal images. This work deals with a general problem of segmentation of multi-texture images using clustering of Gabor filter output features, required to be separated in order to get better classification efficiency. Therefore, an effort was done to formalize it as an objective function for tuning filter parameters with Gradient descent and Genetic Algorithm. The results showed both quantitative and qualitative segmentation results of retinal images with improved classification accuracy.

References
  1. Sinthanayothin, C., Boyce, J. F., Williamson, T. H., Cook, H. L., Mensah, E., Lal, S., and Usher, D. (2002). Automated detection of diabetic retinopathy on digital fundus images. Diabetic Medicine, 19, 105–112.
  2. Ahmed, W. R., Eswaran C., and Subhas, H., (2008). Diabetic Retinopathy: A Quadtree Based Blood Vessel Detection Algorithm Using RGB Components in Fundus Images, Journal of Medical Systems, 147-155.
  3. Dey, N., Roy, A.B., Pal, M., and Das, A.(2012). FCM Based Blood Vessel Segmentation Method for Retinal Images. International Journal of Computer Science and Network (IJCSN), Vol. 1 (3), 148-152.
  4. Teng, T., Lefley, M., Claremont, D. (2002). Progress towards automated diabetic ocular screening: a review of image analysis and intelligent systems for diabetic retinopathy. Medical and Biological Engineering and Computing 40 (2002), 2–13.
  5. Olson, J.A., Strachana, F.M., Hipwell, J.H. (2003). A comparative evaluation of digital imaging, retinal Photography and optometrist examination in screening for diabetic retinopathy. Diabetic Medicine, Vol. 20(7), 528–534.
  6. M. A. Palomera-Perez, M. E. Martinez-Perez, H. Benitez-Perez, J.L. Ortega-Arjona (2010). Parallel multiscale feature extraction and region growing: application in retinal blood vessel detection. IEEE Transactions on Information Technology in Biomedicine 14 (2010), 500–506.
  7. Ho1,C.-Y. & Pai1, T.-W. (2011). An automatic fundus image analysis system for clinical diagnosis of glaucoma. International Conference on Complex, Intelligent, and Software Intensive Systems, Seoul, 559-564.
  8. Dunn, Dennis and Higgins, William E. and Joseph, W. (1994). Texture Segmentation Using 2-D Gabor Elementary Functions. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 16 (2), 130- 149.
  9. Dunn, Dennis and Higgins, William E. (1995). Optimal Gabor Filters for Texture Segmentation, IEEE Transactions on Image Processing, Vol. 4 (7), 947-964.
  10. Bovik, A. C. and Clark, M. and Geisler, Wilson S. (1990). Multichannel Texture Analysis using localised spatial filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12 (1), 55-73.
  11. Das, S. and Kumar, G.P. and Yegnanarayana, B. (1998). One Dimensional Gabor Filtering for Texture Edge Detection. Computer Vision, and book-title: Graphics and Image Processing - Recent Advances, 231 - 237.
  12. Chaudhuri S., Chatterjee S., Katz N., Nelson M., and Goldbaum M. (1989). Detection of blood vessels in retinal images using two dimensional matched filters, IEEE transactions on Medical Imaging, Vol. 8(3), 263–269.
  13. Kulikowski, J. J. and Marcelja, S. and Bishop P. (1982). Theory of spatial position and spatial frequency relations in the receptive fields of simple cells in the visual cortex. Biological Cybern, Vol. 43, 187-198.
  14. Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Reading: Addison-Wesley.
  15. Kinnear, Jr., K. E. (1994). A Perspective on the Work in this Book. In K. E. Kinnear (Ed.), Advances in Genetic Programming (pp. 3-17). Cambridge: MIT Press.
  16. DRIVE- Retinal Image Database http://www.isi.uu.nl/Research/Databases/DRIVE/download.php
  17. Lingel, N. J., Care of the patient with diabetic retinopathy, Pacific On-Line Optometry Education.
Index Terms

Computer Science
Information Sciences

Keywords

Retinal fundus images blood vessels segmentation Genetic Algorithm Diabetic Retinopathy Gabor Filter Gaussian Post Filter.

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