Email this article 
Open Access
Subscription or Fee Access
Diagnosing the Retinal Disease using Scanning Laser Ophthalmoscope by ANFIS Classifier
Abstract
Scanning Laser Ophthalmoscopes (SLOs) can be used for early detection of retinal diseases. With the advent of latest screening technology, the advantage of using SLO is its wide field of view, which can image a large part of the retina for better diagnosis of the retinal diseases. On the other hand, during the imaging process, artifacts such as eyelashes and eyelids are also imaged along with the retinal area. This brings a big challenge on how to exclude these artifacts. In this paper, we propose a novel approach to automatically extract out true retinal area from an SLO image based on image processing and machine learning approaches. To reduce the complexity of image processing tasks and provide a convenient primitive image pattern, I have grouped pixels into different regions based on the regional size and compactness, called superpixels. The framework then calculates image based features reflecting textural and structural information and classifies between retinal area and artifacts. The experimental evaluation results have shown good performance with an overall accuracy of 92%.
Keywords
References
M. S. Haleem, L. Han, J. van Hemert, and B. Li, “Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review,” Comput. Med. Imag. Graph. vol. 37, pp. 581–596, 2013.
Optos. (2014). [Online]. Available: www.optos.com
R. C. Gonzalez and R. E. Woods, Eds., Digital Image Processing, 3rd ed.Englewood Cliffs, NJ, USA: Prentice-Hall, 2006.
M. J. Aligholizadeh, S. Javadi, R. S. Nadooshan, and K. Kangarloo, “Eyelid and eyelash segmentation based on wavelet transform for iris recognition,” in Proc. 4th Int. Congr. Image Signal Process. 2011, pp. 1231–1235.
D. Zhang, D. Monro, and S. Rakshit, “Eyelash removal method for human iris recognition,” in Proc. IEEE Int. Conf. Image Process., 2006,
jyh-Shing Roger Jang Adaptive networks based fuzzy Interface system.-May-june 2013
D. Lakshmi Research Scholar, Sathyabama University, Chennai, Tamil Nadu, India.
KAPIL BAMNE, department of communication Eng. ,uecu,Ujjain,NEHA SHARMA,department of communication Eng.,uecu,Ujjain, Ujjain,India- may 2015
Hamed Sahraie*, Ali Ghaffari1, Majid Amidpour1 National Iranian Southfield Oil Co Mechanical Engineering Department, K.N.Toosi University of Technology, Iran, Tehran.
Iris database. (2005). [Online]. Available: http://www.cbsr.ia.ac.cn/ IrisDatabase.htm
H. Davis, S. Russell, E. Barriga,M.Abramoff, and P. Soliz, “Vision-based, real-time retinal image quality assessment,” in Proc. 22nd IEEE Int. Symp. Comput.-Based Med. Syst., 2009, pp. 1–6.
H. Yu, C. Agurto, S. Barriga, S. C. Nemeth, P. Soliz, and G. Zamora, “Automated image quality evaluation of retinal fundus photographs in diabetic retinopathy screening,” in Proc. IEEE Southwest Symp. Image Anal. Interpretation, 2012, pp. 125–128.
J. A. M. P. Dias, C. M. Oliveira, and L. A. d. S. Cruz, “Retinal image quality assessment using generic image quality indicators,” Inf. Fusion, vol. 13, pp. 1–18, 2012.
M. Barker and W. Rayens, “Partial least squares for discrimination,” J. Chemom., vol. 17, pp. 166–173, 2003.
J. Paulus, J.Meier, R. Bock, J. Hornegger, and G.Michelson, “Automated quality assessment of retinal fundus photos,” Int. J. Comput. Assisted Radiol. Surg., vol. 5, pp. 557–564, 2010.
R. Pires, H. Jelinek, J.Wainer, and A. Rocha, “Retinal image quality analysis for automatic diabetic retinopathy detection,” in Proc. 25th SIBGRAPI Conf. Graph., Patterns Images, 2012, pp. 229–236.
R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. S¨usstrunk, “Slic superpixels compared to state-of-the-art super pixel methods,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 11, pp. 2274–2282, Nov. 2012.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.