ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Segmentation of Bright Region of The Optic Disc for Eye Disease Prediction


Affiliations
1 Department of Computer Science and Engineering, Walchand College of Engineering, India
     

   Subscribe/Renew Journal


Eye is a vital organ of vision of the human body. Eyes are used almost in every activity whether in reading, affect developmental learning, working and in other untold ways. But, the eye diseases like Cataracts, Macular degeneration, Retinopathy, Glaucoma etc. gradually influence on the eye and leads to blindness. For the early detection of symptoms of eye diseases the ophthalmologist uses the manual observation method. But, that is time consuming and error prone. In this paper, to save the time and reduce the probability of the error, eye disease prediction approach for Glaucoma is developed. For this eye disease prediction approach firstly, the continuous and non-continuous Blood Vessels are segmented using the Coye Filter Approach. Secondly, the bright region of the Optic Disc is segmented using the MRF and Compensation Factor Method. Finally, the channels intensities of the bright region of Optic Disc is compared with the range of channels intensity of the set of bright region of the healthy Retinal images for prediction of the Glaucoma affection. The range for each channel consist of the intensity value starts from minimum to maximum intensities from the set of healthy Retinal images. For this, the Retinal Fundus image is captured by digital Fundus camera with the field of view between 35o to 50o. The Coye Filter Approach, MRF and Compensation Factor Method is applied for the Diaretdb1 and DRIVE which successfully segment the Blood Vessels as well as Optic Disc and also the eye disease prediction approach is applied for the 10 Glaucoma images which correctly predict for the Glaucoma affection.

Keywords

Retinal Fundus Image, Glaucoma, Optic Disc, Blood Vessels, Retinopathy.
Subscription Login to verify subscription
User
Notifications
Font Size

  • A. Salazar-Gonzalez, “Segmentation of the Blood Vessels and Optic Disk in Retinal Images”, IEEE Journal of Biomedical and Health Informatics, Vol. 18, No. 6, pp. 1874-1886, 2014.

  • Tyler Coye, “A Novel Retinal Blood Vessel Segmentation Algorithm for Fundus Images”, Available at: https://in.mathworks.com/matlabcentral/fileexchange/50839-a-novel-retinal-blood-vessel-segmentation-algorithm-for-fundus-images.

  • Alan V. Oppenheim, “Discrete-Time Signal Processing”, Pearson Education, 1999.

  • Contrast Limited Adaptive Histogram Equalization, Available at: https://in.mathworks.com/help/images/ref/adapthisteq.html.

  • X. Zhu, R.M. Rangayyan and A.L. Ells, “Digital Image Processing for Ophthalmology: Detection of the Optic Nerve Head”, Synthesis Lectures on Biomedical Engineering, Vol. 6, No. 1, pp. 100-106, 2011.

  • Chun-Yuan Yu and Shyr-Shen Yu, “Automatic Localization of the Optic Disc Based on Iterative Brightest Pixels Extraction”, Proceedings of IEEE International Symposium on Computer, Consumer and Control, pp. 121-124, 2014.

  • X. Zhu, R.M. Rangayyan and A.L. Ells, “Detection of the Optic Nerve Head in Fundus Images of the Retina using the Hough Transform for Circles”, Journal of Digital Imaging, Vol. 23, No. 3, pp. 332-341, 2010.

  • Alireza Osareh et al., “Comparison of Colour Spaces for Optic Disc Localization in Retinal Images”, Proceedings of 16th International Conference on Pattern Recognition, pp. 231-235, 2002.

  • Shijian Lu, “Automatic Optic Disc Detection using Retinal Background and Retinal Blood Vessels”, Proceedings of 3rd International Conference on Biomedical Engineering and Informatics, pp. 412-415, 2010.

  • C. Sinthanayothin et.al., “Automated Localization of the Optic Disc, Fovea, and Retinal Blood Vessels from Digital Colour Fundus Images”, British Journal of Ophthalmology, Vol. 83, No. 8, pp. 902-910, 1999.

  • Mira Park, Jesse S. Jin and Suhuai Luo, “Locating the Optic Disc in Retinal Images”, Proceedings of International Conference on Computer Graphics, Imaging and Visualization, pp. 87-91, 2006.

  • Thomas Walter and Jean-Claude Klein, “Segmentation of Color Fundus Images of the Human Retina: Detection of the Optic Disc and the Vascular Tree using Morphological Techniques”, Proceedings of International Symposium on Biological and Medical Data Analysis, pp. 282-287, 2001.

  • Muhammad Zubair, Abubakar Yamin and Shoab A. Khan, “Automated Detection of Optic Disc for the Analysis of Retina using color Fundus Image”, Proceedings of IEEE International Conference on Imaging Systems and Techniques, pp. 302-307, 2013.

  • Frank Ter Haar, “Automatic Localization of the Optic Disc in Digital Colour Images of the Human Retina”, Master Thesis, Department of Computer Science, Utrecht University, 2005.

  • Joes Staal, et al., “Ridge-based Vessel Segmentation in Color Images of the Retina”, IEEE Transactions on Medical Imaging, Vol. 23, No. 4, pp. 501-509, 2004.

  • Jae S. Lim, “Two-Dimensional Signal and Image Processing”, Prentice Hall, 1990.

  • J. Liu, et al., “ARGALI: An Automatic Cup-to-Disc Ratio Measurement System for Glaucoma Detection and Analysis Framework”, Proceedings of International Conference on Medical Imaging, pp. 663-667, 2009.

  • Quan Wang, “HMRF-EM-Image: Implementation of the Hidden Markov Random Field Model and its Expectation-Maximization Algorithm”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 1-4, 2012.


Abstract Views: 205

PDF Views: 3




  • Segmentation of Bright Region of The Optic Disc for Eye Disease Prediction

Abstract Views: 205  |  PDF Views: 3

Authors

Rahul Jadhav
Department of Computer Science and Engineering, Walchand College of Engineering, India
Manish Narnaware
Department of Computer Science and Engineering, Walchand College of Engineering, India

Abstract


Eye is a vital organ of vision of the human body. Eyes are used almost in every activity whether in reading, affect developmental learning, working and in other untold ways. But, the eye diseases like Cataracts, Macular degeneration, Retinopathy, Glaucoma etc. gradually influence on the eye and leads to blindness. For the early detection of symptoms of eye diseases the ophthalmologist uses the manual observation method. But, that is time consuming and error prone. In this paper, to save the time and reduce the probability of the error, eye disease prediction approach for Glaucoma is developed. For this eye disease prediction approach firstly, the continuous and non-continuous Blood Vessels are segmented using the Coye Filter Approach. Secondly, the bright region of the Optic Disc is segmented using the MRF and Compensation Factor Method. Finally, the channels intensities of the bright region of Optic Disc is compared with the range of channels intensity of the set of bright region of the healthy Retinal images for prediction of the Glaucoma affection. The range for each channel consist of the intensity value starts from minimum to maximum intensities from the set of healthy Retinal images. For this, the Retinal Fundus image is captured by digital Fundus camera with the field of view between 35o to 50o. The Coye Filter Approach, MRF and Compensation Factor Method is applied for the Diaretdb1 and DRIVE which successfully segment the Blood Vessels as well as Optic Disc and also the eye disease prediction approach is applied for the 10 Glaucoma images which correctly predict for the Glaucoma affection.

Keywords


Retinal Fundus Image, Glaucoma, Optic Disc, Blood Vessels, Retinopathy.

References