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Feature Extraction using Contourlet Transform for Glaucomatous Image Classification
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 95 - Number 18 |
| Year of Publication: 2014 |
| Authors: F. Fanax Femy, S. P. Victor |
10.5120/16694-6819
|
F. Fanax Femy, S. P. Victor . Feature Extraction using Contourlet Transform for Glaucomatous Image Classification. International Journal of Computer Applications. 95, 18 ( June 2014), 20-24. DOI=10.5120/16694-6819
Abstract
Glaucoma is one of the leading causes of blindness. It is caused by increased Intra Ocular Pressure (IOP) due to the malfunction of the drainage structure of the eyes. There are several methods to detect the Glaucoma from a human eye in the initial stages. In this paper a new system is proposed, which automatically detect Glaucoma disease in the human eye from the fundus database images. The feature extraction within images is based on Contourlet Transform (CT) and the classification is based on Support Vector Machine (SVM). In the existing system Discrete Wavelet Transform is used for feature extraction of Glaucoma images. Discrete Wavelet Transform have less number of information and the features obtained are only in three directions(vertical, horizontal, diagonal). Hence for better resolution and more directions Contourlet Transform is used. The better classification is arrived by extracting and selecting the best features from the contourlet coefficients of the fundus image and the outputs are used as an input to the Support Vector Machine classifier for classification with high accuracy. The proposed system classifies the input fundus images as normal or abnormal with very high accuracy.
References
- U. R. Acharya, S. Dua, X. Du, V. S. Sree, and C. K. Chua, "Automated diagnosis of glaucoma using texture and higher order spectra features,"IEEE Trans. Inf. Technol. Biomed. , vol. 15, no. 3, pp. 449–455, May 2011.
- H. Liu and R. Setiono, "Chi2: Feature selection and discretization ofnumeric attributes," in Proc. IEEE 7th Int. Conf. Tools WithArtif. Intell. ,2005,pp. 338-391
- Ke Huang and Selin Aviyente, "Wavelet Feature Selection for Image Classification", IEEE Transactions on image Processing, Vol. 17,No. 9,Sep 2008.
- V. RajendraAcharya. U,Sumeet Dua, Xian Du,VinithaSree S, and Chua KuangChua,"Automated Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features ",IEEE transactions On Information Technology In Biomedicine,Vol. 15,No. 3,May 2011.
- E. A Essock, Y. Zheng, and P. Gunvant, "Analysis of GDx- VCC Poarimetry data by Wavelet-Fourier analysis across gluacomastages",Invest . Ophthalmol. V is. Sci…,Volume 46,PP. 28382-2847,Aug 2005
- A. Khotanzad, J. Lu, "Classification of Invariant Image Representations Using a Neural Network", IEEE Trans Acoustics, Speech, and Signal Processing, Vol. 38, 1990, pp. 1028-1038.
- R. O. Duncan et al. , "Retinotopic organization of primary visual cortexin glaucoma: A method for comparing cortical function with damage tothe optic disk," Invest. Ophthalmol. Vis. Sci. , vol. 48, pp. 733–744, Feb. 2007.
- K. Hild,D. Erdogmus, K. Torkkola, and J. Principe, "Feature extraction using information-theoretic learning,"IEEETrans. PatternAnal. Mach. Intell. ,vol. 28,no. 9,pp. 1385-1392,sep. 2012.
- . X. Zhang, N. H. Younan, and C. G. O'Hara, "Wavelet do- main statistical hyperspectral soil texture classi?cation," IEEE Trans. Geosci. Remote Sensing, vol. 43, no. 3, pp. 615 – 618, 2005.
- . J. Ham, Y. Chen, M. Crawford, and J. Ghosh, "Investiga- tion of the random forest framework for classi?cation of hyperspectral data," IEEE Trans. Geosci. Remote Sens- ing, vol. 43, no. 3, pp. 492–501, 2005.
- S. S. Keerthi et al. , "Improvements to Platt's SMO algorithm for SVMclassi?er design," Neural Comput. , vol. 13, pp. 637–649, 2001.
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