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Optimal Hyper Analytic Wavelet Transform for Glaucoma Detection in Fundal Retinal Images
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 Title & Authors
Optimal Hyper Analytic Wavelet Transform for Glaucoma Detection in Fundal Retinal Images
Raja, C.; Gangatharan, N.;
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 Abstract
Glaucoma is one of the most common causes of blindness which is caused by increase of fluid pressure in the eye which damages the optic nerve and eventually causing vision loss. An automated technique to diagnose glaucoma disease can reduce the physicians’ effort in screening of Glaucoma in a person through the fundal retinal images. In this paper, optimal hyper analytic wavelet transform for Glaucoma detection technique from fundal retinal images is proposed. The optimal coefficients for transformation process are found out using the hybrid GSO-Cuckoo search algorithm. This technique consists of pre-processing module, optimal transformation module, feature extraction module and classification module. The implementation is carried out with MATLAB and the evaluation metrics employed are accuracy, sensitivity and specificity. Comparative analysis is carried out by comparing the hybrid GSO with the conventional GSO. The results reported in our paper show that the proposed technique has performed well and has achieved good evaluation metric values. Two 10- fold cross validated test runs are performed, yielding an average fitness of 91.13% and 96.2% accuracy with CGD-BPN (Conjugate Gradient Descent- Back Propagation Network) and Support Vector Machines (SVM) respectively. The techniques also gives high sensitivity and specificity values. The attained high evaluation metric values show the efficiency of detecting Glaucoma by the proposed technique.
 Keywords
Glaucoma;Hyper analytic transform;GSO;Cuckoo search;Support vector machines;
 Language
English
 Cited by
 References
1.
Jun Cheng, Jiang Liu, Yanwu Xu and Fengshou Yin, “Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening”, IEEE transactions on Medical Imaging, vol. 32, no. 6, pp. 1019-1032, 2013. crossref(new window)

2.
Kim P.Y, Iftekharuddin K.M, Davey P.G. and Toth. M, “Novel Fractal Feature-Based Multiclass Glaucoma Detectionand Progression Prediction”, IEEE Journal of Biomedical and Health Informatics, vol. 17, no. 2, pp. 269 - 276, 2013. crossref(new window)

3.
Dua. S, Acharya. U. R, Chowriappa. P and Sree. S.V, “Wavelet-Based Energy Features for Glaucomatous Image classification”, IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 1, pp. 80 - 87, 2012. crossref(new window)

4.
Rajendra Acharya, Dua.S, Xian Du and Vinitha Sree. S, “Automated Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features”, IEEE Transactions on Information Technology in Biomedicine, vol. 15, no. 3, pp. 449 - 455, 2011. crossref(new window)

5.
Muthu Rama Krishnan. M, Rajendra Acharya. U, Lim Choo Mina, Andrea Petznick, Jasjit S. Suri, “Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features”, Knowledge-Based Systems, vol. 33, pp. 73-82, 2012. crossref(new window)

6.
Muthu Rama Krishnan. M And Oliver Faust, J “Automated Glaucoma Detection Using Hybrid Feature Extraction In Retinal Fundus Images.” Mech. Med. Biol, vol. 13, no. 1, pp. 1350011- 1-21, 2012.

7.
Patton. N, Aslam. T.M, et al., “Retinal image analysis: Concepts, applications and potential”, Progress in Retinal and Eye Research, vol. 25, no. 1, pp. 99-127, 2006. crossref(new window)

8.
Anderson. D. R, “The Optic Nerve in Glaucoma” Duane’s Ophthalmology, chapter 48, 2009.

9.
Chrastek. R, Wolf. M, et al, “Automated segmentation of the optic nerve head for diagnosis of glaucoma”, Journal of Medical Image Analysis in Elsevier, Functional Imaging and Modeling of the Heart”, vol. 9, no. 4, pp. 297-314, 2005.

10.
Ravishankar. S, Jain. A, Mittal. A, “Automated feature extraction for early detection of diabetic retinopathy in fundus images”, in proceedings of IEEE con-ference on Computer Vision and Pattern Recognition, Miami, FL, pp. 210-217, 2009.

11.
K. Narasimhan, K. Vijayarekha, “An Efficient Automated System For Glaucoma Detection Using Fundus Image”, Journal of Theoretical and Applied Information Technology”, vol. 33, no.1, pp. 104-110, 2011.

12.
Rüdiger Bock, Jörg Meier, László G. Nyúl, Georg Michelson, Joachim Hornegge, “Retina Image Analysis System for Glaucoma Detection”, in proceedings of German Society for Biomedical Engineering, pp.26-29, 2007.

13.
K. R. Sung, Jong. S. Kim, et al., “Imaging of the retinal nerve fiber layer with spectral domain optical coherence tomography forglaucoma diagnosis,” Br. J. Ophthalmol., vol. 95, no. 7, pp. 909-914, 2010.

14.
B. Brown, “Structural and functional imaging of the retina: New ways to diagnose and assess retinal disease,” Clin. Exp. Optometry, vol. 91, no. 6, pp. 504-514, 2008. crossref(new window)

15.
Ioana Adam, Corina Nafornita, Jean-Marc Boucher, AlexandruIsar, “A Bayesian Approach of Hyper-analytic Wavelet Transform Based Denoising”, in proceedings of IEEE International Symposium on Intelligent Signal Processing, 2007, pp. 1 - 6, 2007.

16.
C. Raja and N. Gangatharan, "Appropriate sub-band selection in wavelet packet decomposition for automated glaucoma diagnosis." International Journal of Automation and computing, 2015. DOI 10.1007/s11633-014-0858-6. crossref(new window)

17.
C. Raja and N. Gangatharan, “Incorporating Phase Information for efficient Glaucoma Diagnosis through Hyper Analytic Wavelet Transform”, in Proceedings of Fourth International Conference on Soft Computing for Problem Solving, Advances in Intelligent Systems and Computing, Vol. 2, pp. 325-339.

18.
S. He, Q. H. Wu, and J. R. Saunders, “Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior”, in IEEE transactions on evolutionary computation, vol. 13, no. 5, pp. 973-990, 2009. crossref(new window)

19.
Xin She Yang and Deb, Suash, “Cuckoo search via Lévy flights”, in IEEE proceedings World Congress on Nature & Biologically Inspired Computing, pp. 210-214, 2009.

20.
Fumero.F, Alayon.S, et al., “RIM-ONE: An open retinal image database for optic nerve evaluation”, in proceedings of International Symposium on Computer-Based Medical Systems, pp. 1-6, 2011.

21.
Wen Zhu, Nancy Zeng, Ning Wang, “Sensitivity, Specificity, Accuracy, Associated Confidence Interval and ROC Analysis with Practical SAS Implementations”, in proceedings of the SAS Conference, Baltimore, Maryland, pages: 9, 2010.

22.
C.Raja and N.Gangatharan, “Glaucoma Detection in Fundal Retinal Images Using Trispectrum and Complex Wavelet-Based Features”, European Journal of Scientific Research, vol. 97, no. 1, pp. 159-171, 2013.

23.
Abdelhamid Daamouche, Latifa Hamami, et al, "A wavelet optimization approach for ECG signal classification." Biomedical Signal Processing and Control, vol.7, pp. 342-349, 2012. crossref(new window)

24.
P.P. Vaidyanathan, “Multirate Systems and Filter Banks”, Prentice-Hall, Englewood Cliffs, 1993.