GLAUCOMA IMAGE CLASSIFICATION USING DISCRETE ORTHOGONAL STOCKWELL TRANSFORM
DOI:
https://doi.org/10.29284/ijasis.3.1.2017.1-6Keywords:
Fundus Image, Glaucoma, Discrete Orthogonal Stockwell Transform, Random Forest ClassifierAbstract
Glaucoma is an eye condition which is caused by the improved blood pressure in the optic nerve. It causes a functional failure of the visual field and irreversible. A Computer Aided Diagnosis (CAD) can help the doctors to find glaucoma at the earliest. In this paper, a CAD system for glaucoma diagnosis using Discrete Orthogonal Stockwell Transform (DOST) is presented. DOST distribute its coefficients based on sample spacing paradigm where low frequencies have a lower sampling rate, and high frequencies have higher sampling rate. All DOST coefficients are considered for the diagnosis of glaucoma using Random Forest (RF) classifier. Results show that the glaucoma diagnosis system has 96% sensitivity, 92% specificity, and 94% accuracy using 100 fundus images of normal and glaucoma cases.
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References
U.R. Acharya, S. Dua, X. Du, and C.K. Chua, Automated diagnosis of glaucoma using texture and higher order spectra features, IEEE transactions on Information Technology in Biomedicine, Vol.15, No.3, 2011, pp.449-455.
A. Rajan, and G.P. Ramesh, Automated Early Detection of Glaucoma in Wavelet Domain Using Optical Coherence Tomography Images, Biosciences Biotechnology Research Asia, Vol.12, No.3, 2015, pp.2821-2828.
S.A. Hussain, and A.N. Holambe, Automated Detection and Classification of Glaucoma from Eye Fundus Images: A Survey, International Journal of Computer Science and Information Technologies, Vol.6, No.2, 2015, pp.1217-1224.
N. Annu, and J. Justin, Automated classification of glaucoma images by wavelet energy features, International Journal of Engineering and Technology, Vol.5, No.2, 2015, pp.1716-1721.
A.N.S. Byahatti, K. Sridevi, and R. Hegadi, Computer Based Diagnosis of Glaucoma using Digital Fundus Images, Proceedings of the World Congress on Engineering, Vol. 3, 2014, pp. 3-5.
G.O. Gajbhiye, and A.N. Kamthane, Automatic classification of glaucomatous images using wavelet and moment feature, IEEE India Conference, 2015, pp. 1-5.
S. Mohammad, and D.T. Morris, Texture analysis for glaucoma classification. IEEE International Conference on BioSignal Analysis, Processing and Systems, 2015, pp. 98-103.
A. Rajan, G.P. Ramesh, and J. Yuvaraj, Glaucomatous image classification using wavelet transform, IEEE International Conference on Advanced Communication Control and Computing Technologies, 2014, pp. 1398-1402.
D. Yadav, M.P. Sarathi, and M.K. Dutta, Classification of glaucoma based on texture features using neural networks, IEEE Seventh International Conference on Contemporary Computing, 2014, pp. 109-112.
F. Fink, K. Worle, P. Gruber, A.M. Tome, J.M. Gorriz-Saez, C.G. Puntonet, and E.W. Lang, ICA analysis of retina images for glaucoma classification, IEEE 30th Annual International Conference of the Engineering in Medicine and Biology Society, pp. 4664-4667.
K. Choudhary, and S. Wadhwa, Glaucoma detection using cross validation algorithm, IEEE Fourth International Conference on Advanced Computing & Communication Technologies, 2014, pp. 478-482.
R.G. Stockwell, A basis for efficient representation of the S-transform, Digital Signal Processing, Vol. 17, No. 1, 2007, pp. 371–393.
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