Abstract

A digital imaging technique is utilized in almost all the fields. Based on image processing concept image particle shape can be analyzed in detail. Nowadays, in eye clinics, imaging of the eye fundus with modern technology is in high demand because of its worth and expected lifetime. Eye fundus imaging is considered a non-invasive and painless route to screen and monitor the micro vascular distinction of diabetes and diabetic retinopathy. In general, Optic Disc (OD) signifies the creation of the optic nerve. It is the point where the axons of retinal ganglion cells gain nearer. The Optic Disc is an access point of major blood vessels which provides the retina. In this study a method is introduced to automatically detect the position of the OD in digital retinal fundus images. The OD detection algorithm is based on the identical expected directional pattern of the retinal blood vessels. In this study two types of filters are proposed, one is Gaussian based bilateral filter, to reduce/eliminate the noise of the fundus images and another is a Haar filter to detect the diabetic retinopathy in the fundus images. The most excellent method to segment the images is thresholding based connected component pixels. The results have been taken from many diabetic retinopathy images. In this study for implementation efficient image filtering was used and named as OpenCV 2.4.9.0 and cvblobslib to accomplish successful result. In future development, the fovea detection will be applied.

Keywords:

Bilateral filter, connected component pixels, diabetic retinopathy, digital image processing , fundus images, Haar filter, optic nerve,

References

1. Akara, S., U. Bunyarit and B. Sarah, 2008. Automatic exudates detection from non-dilated diabetic retinopathy retinal images using fuzzy C-means clustering. Comput. Med. Imag. Grap., 32: 720-727.
   CrossRef    PMid:18930631    
2. Amrutkar, N., Y. Bandgar, S. Chitalkar and S.L. Tade, 2013. Retinal blood vessel segmentation algorithm for diabetic retinopathy and abnormality detection using image subtraction. Int. J. Adv. Res. Electr. Electron. Instrum. Eng., 2(4).
   
3. Badawy, S., A.S. El-Sherbeny, A. El Saadany and M.A. Fkirin, 2013. K7. Retinal blood vessel image segmentation using rotating filtration to help in early diagnosis and management diabetic retinopathy. Proceeding of the 30th National Radio Science Conference, Egypt.
   
4. Chandrashekar, M.P., 2013. An approach for the detection of vascular abnormalities in diabetic retinopathy. Int. J. Data Min. Techn. Appl., 2: 246-250.
   
5. Claudio, A.P., A.S. Daniel, C.M. Aravena, C.I. Perez and T.J. Verdaguer, 2013. A new method for online retinal optic-disc detection based on cascade classifiers. Proceeding of the IEEE International Conference on Systems, Man and Cybernetics (SMC, 2013), pp: 4300-4304.
   
6. Faust, O., A.U. Rajendra, E.Y.K. Ng, K.H. Ng and J.S. Suri, 2012. Algorithms for the automated detection of diabetic retinopathy using digital fundus images: A review. J. Med. Syst., 36(1).
   CrossRef    
7. Habashy, S.M., 2013. Identification of diabetic retinopathy stages using fuzzy C-means classifier. Int. J. Comput. Appl., 77(9).
   
8. Hashim, F.A., N.M. Salem and A.F. Seddik, 2013. Preprocessing of color retinal fundus images. Proceeding of the 2nd International Japan-Egypt Conference on Electronics, Communications and Computers (JEC-ECC), pp: 258-261.
   CrossRef    
9. Joshi, M.S., P. Rekha and H. Aravind, 2009. Automated detection and quantification of haemorrhages in diabetic retinopathy images using image arithmetic and mathematical morphology methods. Int. J. Recent Trends Eng., 2(6).
   
10. Kaur, J. and H.P. Sinha, 2012. Automated detection of diabetic retinopathy using fundus image analysis. Int. J. Comput. Sci. Inform. Technol., 3(4): 4794-4799.
    
11. Lazar, I. and A. Hajdu, 2013. Retinal microaneurysm detection through local rotating cross-section profile analysis. IEEE T. Med. Imaging, 32(2).
    
12. Patil, J.D. and A.L. Chaudhari, 2012. Tool for the detection of diabetic retinopathy using image enhancement method in DIP. Int. J. Appl. Inform. Syst., 3(3).
    
13. Subramani, P., R. Sahu and S. Verma, 2006. Feature selection using Haar wavelet power spectrum. BMC Bioinformatics, 7: 432.
    CrossRef    PMid:17022808 PMCid:PMC1618414    
14. Tripathi, S., K.K. Singh, B.K. Singh and A. Mehrotra, 2013. Automatic detection of exudates in retinal fundus images using differential morphological profile. Int. J. Eng. Technol., 5(3): 2024-2029.
    
15. Zhang, B., B.V. Kumar and D. Zhang, 2014. Detecting diabetes mellitus and nonproliferative diabetic retinopathy using tongue color, texture, and geometry features. IEEE T. Bio-Med. Eng., 61(2): 491-501.
    

Competing interests

The authors have no competing interests.

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