Optical Disc and Blood Vessel Segmentation in Retinal Fundus Images

  • Amira B. Sallow Department of Computer Science, Nawroz University, Duhok, Kurdistan Region - Iraq
  • Hawkar Kh. Shaikha Department of Computer Science, Faculty of Science, Zakho University, Duhok, Kurdistan Region – Iraq

Abstract

Segmentation of optical disk (OD) and blood vessel is one of the significant steps in automatic diabetic retinopathy (DR) detecting. In this paper, a new technique is presented for OD segmentation that depends on the histogram template matching algorithm and OD size. In addition, Kirsch method is used for Blood Vessel (BV) segmentation which is one of the popular methods in the edge detection and image processing technique. The template matching algorithm is used for finding the center of the OD. In this step, the histogram of each RGB (Red, Green, and Blue) planes are founded and then the cross-correlation is founded between the template and the original image, OD location is the point with maximum cross-correlation between them. The OD size varies according to the camera field of sight and the resolution of the original image. The rectangle size of OD is not the same for various databases, the estimated size for DRIVE, STARE, DIARTDB0, and DIARTDB1 are 80×80, 140×140, 190×190, and 190×190 respectively. After finding the OD center and rectangle size of OD, a binary mask is created with Region of Interest (ROI) for segmenting the OD. The DIARTDB0 is used to evaluate the proposed technique, the result is robust and vital with an accuracy of 96%.

Downloads

Download data is not yet available.

References

1. S. Lu, “Accurate and Efficient Optic Disk Detection and Segmentation by a Circular Transformation,” IEEE Trans. Med. Imaging, vol. 30, no. 12, pp. 2126–2133, 2011.
2. K. W. Tobin, E. Chaum, V. Priya Govindasamy, and T. P. Karnowski, “Detection of anatomic structures in human retinal imagery,” IEEE Trans. Med. Imaging, vol. 26, no. 12, pp. 1729–1739, 2007.
3. E. J. Carmona, M. Rincón, J. García-Feijoó, and J. M. Martínez-de-la-Casa, “Identification of the optic nerve head with genetic algorithms,” Artif. Intell. Med., vol. 43, no. 3, pp. 243–259, 2008.
4. A. A. H. A. R. Youssif, A. Z. Ghalwash, and A. A. S. A. R. Ghoneim, “Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter,” IEEE Trans. Med. Imaging, vol. 27, no. 1, pp. 11–18, 2008.
5. A. A. A. Youssif, A. Z. Ghalwash, and A. S. Ghoneim, “Comparative Study of Contrast Enhancement and Illumination Equalization Methods for Retinal Vasculature Segmentation,” Cairo Int. Biomed. Eng. Conf., no. DECEMBER, p. 5, 2006.
6. T. Kauppi, V. Kalesnykiene, J. Kamarainen, L. Lensu, and I. Sorri, “DIARETDB0 : Evaluation Database and Methodology for Diabetic Retinopathy Algorithms,” Mach. Vis. Pattern Recognit. Res. Group, Lappeenranta Univ. Technol. Finland., pp. 1–17, 2006.
7. M. Kuivalainen, “Retinal image analysis using machine vision,” p. 96, 2005.
8. R. Sharma, S. Sharma, and M. T. Scholar, "Robust Watermarking of Color Images using RST Invariant Features," vol. 2, no. 6, pp. 142–149, 2014.
9. S. Lu and J. H. Lim, “Automatic optic disc detection from retinal images by a line operator,” IEEE Trans. Biomed. Eng., vol. 58, no. 1, pp. 88–94, 2011.
10. A. Aquino, M. E. Gegúndez-Arias, and D. Marín, “Detecting the Optic Disc Boundary in Digital Fundus Images Using Morphological, Edge Detection, and Feature Extraction Techniques,” IEEE Trans. Med. Imaging, vol. 29, no. 11, pp. 1860–1869, Nov. 2010.
11. A. Dehghani, H. Moghaddam, and M. Moin, “Optic disc localization in retinal images using histogram matching,” EURASIP J. Image …, pp. 1–11, 2012.
12. A. Aquino, M. E. Geg, and D. Mar, “Automated optic disk detection in retinal images of patients with diabetic retinopathy and risk of macular edema,” Int. J. Biol. Life Sci., vol. 8, no. 2, pp. 87–92, 2012.
13. P. R. Wankhede and K. B. Khanchandani, "Optic disc detection using histogram-based template matching," in 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), 2016, pp. 182–185.
14. A. Issac, M. Parthasarthi, and M. K. Dutta, “An adaptive threshold based algorithm for optic disc and cup segmentation in fundus images,” 2nd Int. Conf. Signal Process. Integr. Networks, SPIN 2015, pp. 143–147, 2015.
15. H. Yu, C. Agurto, S. Echegaray, and M. S. Pattichis, “Fast Localization and Segmentation of Optic Disk in Retinal Images Using Directional Matched Filtering," no. May 2012.
16. P. Liskowski and K. Krawiec, “Segmenting Retinal Blood Vessels With _newline Deep Neural Networks,” IEEE Transactions on Medical Imaging, vol. 35, no. 11, pp. 2369–2380, 2016.
17. E. Tuba, L. Mrkela, and M. Tuba, “Retinal blood vessel segmentation by support vector machine classification,” 2017 27th International Conference Radioelektronika (RADIOELEKTRONIKA), 2017.
18. H. B. H. Bhadauria, “Vessels Extraction from Retinal Images,” IOSR Journal of Electronics and Communication Engineering, vol. 6, no. 3, pp. 79–82, 2013
Published
2019-08-31
How to Cite
SALLOW, Amira B.; SHAIKHA, Hawkar Kh.. Optical Disc and Blood Vessel Segmentation in Retinal Fundus Images. Academic Journal of Nawroz University, [S.l.], v. 8, n. 3, p. 67-75, aug. 2019. ISSN 2520-789X. Available at: <http://journals.nawroz.edu.krd/index.php/ajnu/article/view/398>. Date accessed: 15 nov. 2019. doi: https://doi.org/10.25007/ajnu.v8n3a398.
Section
Articles