Call for Paper - August 2022 Edition
IJCA solicits original research papers for the August 2022 Edition. Last date of manuscript submission is July 20, 2022. Read More

Retinal Image Segmentation by using Gradient Descent Method

Print
PDF
International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 86 - Number 10
Year of Publication: 2014
Authors:
Pushpendra Kumar
Rekha Pandit
Vineet Richhariya
10.5120/15018-3306

Pushpendra Kumar, Rekha Pandit and Vineet Richhariya. Article: Retinal Image Segmentation by using Gradient Descent Method. International Journal of Computer Applications 86(10):1-7, January 2014. Full text available. BibTeX

@article{key:article,
	author = {Pushpendra Kumar and Rekha Pandit and Vineet Richhariya},
	title = {Article: Retinal Image Segmentation by using Gradient Descent Method},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {86},
	number = {10},
	pages = {1-7},
	month = {January},
	note = {Full text available}
}

Abstract

Localization and segmentation are important task in medical image analysis. As we know detection of optic nerves is also a major problem in automated retinal image analysis system. Image segmentation of medical image is very complex and crucial step, in this series segmentation of retinal image is more complex in comparison of others. For the retinal image segmentation we use gradient descent method. Recent research is focus on better accuracy rate. This paper gives a bird's eye over all the detection technique toward fair segmentation of optic nerves using gradient descent method (GDM). For initialization of local contour we use Signed pressure force function (SPF) which is region-based active contour model.

References

  • Jihene Malek, Mariem Ben Abdallah, Asma Mansour, Rached Tourki, "Automated Optic Disc Detection in Retinal Images by Applying Region-based Active Aontour Model in a Variational Level Set Formulation", in proc. IEEE, 2012.
  • A. Osareh, M. Mirmehdi, B. Thomas, and R. Markham, Automated identification of diabetic retinal exudates in digital colour images, Br. J. Ophthalmol. , vol. 87, 2003.
  • C. Sinthanayothin, J. F. Boyce, H. L. Cook, and T Williamson, Automated localization of the optic disc, fovea, and retinal blood vessels from digital colour Retinal images, Br. J. Ophthalmol. , vol. 83, 1999.
  • Xiao-Feng Wang, De-ShuangHuanga, HuanXu, "An efficient local Chan–Vese model for image segmentation", in proc. Elsevier, 2009.
  • H. Li and O. Chutatape, A model-based approach for automated feature extraction in Retinal images, in Proc. 9th IEEE Int. Conf. Comput. Vis. (ICCV03), 2003, vol. 1.
  • A. Hoover and M. Goldbaum, Fuzzy convergence, in Proc. IEEE, Comp. Soc. Conf. Comput. Vis. Pattern Recognit, Santa Barbara, CA, 1998.
  • A. Hoover and M. Goldbaum, Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels, IEEE Trans. Med. Imag. , vol. 22, no. 8, Aug. 2003.
  • M. Foracchia, E. Grisan, and A. Ruggeri, Detection of optic disc in retinal images by means of a geometrical model of vessel structure,IEEE Trans. Med. Imag. , vol. 23, no. 10, Oct. 2004.
  • A. R. Youssif, A. Z. Ghalwash, and A. R. Ghoneim, Optic disc detection from normalized digital Retinal images by means of a vessels direction matched filter, IEEE Trans. Med. Imag. , vol. 27, 2008.
  • P. C. Siddalingaswamy, K. G. Prabhu , Automatic localization and boundary detection of optic disc using implicit active contours, International Journal of Compuzter Applications, Vol. 1, pp. 1-5, 2010.
  • A. Aquino, M. E. Gegndez-Arias, and D. Marn, Automated optic disc detection in retinal images of patients with diabetic retinopathy and risk of macular edema, International Journal of Biological and Life Sciences 8:2, pp. 87-92, 2012.
  • J Xua" 0 Chutatapeb, E Sungc, C Zhengd, P. C. T Kuand,Optic disk feature extraction via modified deformable model technique for glaucoma analysis , Pattern Recognition, Vol. 40, 2007.
  • A. Osareh, M. Mirmehdi, B. Thomas, and R. Markham, Comparison of colour spaces for optic disc localisation in retinal images, in Proc. 16thInt. Conf. Pattern Recognit. , 2002, pp. 743746.
  • Chunming Li, Chiu-Yen Kao, John C. Gore, and Zhaohua Ding, minimization of region-scalable fitting energy for image segmentation, ieee transactions on image processing, vol. 17, no. 10, october 2008.
  • H. Li, O. Chutatape, Automatic location of optic disk in retinal images, in: Proceings of the International Conference on Image Processing, vol. 2, October 2001.
  • Thord Andersson, Gunnar Lathen, Reiner Lenz, and Magnus Borga. A fast optimization method for level set segmentation. In Proceedings of the 16:th Scandinavian Conference on Image Analysis (SCIA), volume 5575 of Lecture Notes in Computer Science, Oslo, Norway, June 2009, Springer.