Appropriate Sub-band Selection in Wavelet Packet Decomposition for Automated Glaucoma Diagnoses

Chandrasekaran Raja; Narayanan Gangatharan

doi:10.1007/s11633-014-0858-6

August 2015

Article Contents

Chandrasekaran Raja and Narayanan Gangatharan. Appropriate Sub-band Selection in Wavelet Packet Decomposition for Automated Glaucoma Diagnoses. International Journal of Automation and Computing, vol. 12, no. 4, pp. 393-401, 2015. doi: 10.1007/s11633-014-0858-6

Cite as: Chandrasekaran Raja and Narayanan Gangatharan. Appropriate Sub-band Selection in Wavelet Packet Decomposition for Automated Glaucoma Diagnoses. International Journal of Automation and Computing, vol. 12, no. 4, pp. 393-401, 2015. doi: 10.1007/s11633-014-0858-6

Appropriate Sub-band Selection in Wavelet Packet Decomposition for Automated Glaucoma Diagnoses

Department of Electronics and Communication Engineering, Anjalai Ammal Mahalingam Engineering College, Kovilvenni, India;
Department of Electronics and Communication Engineering, R.M.K. College of Engineering and Technology, Puduvoyal 601206, India

Received: 2013-10-28

Abstract

The most common reason for blindness among human beings is Glaucoma. The increase of fluid pressure damages the optic nerve which gradually leads to irreversible loss of vision. A technique for automated screening of Glaucoma from the fundal retinal images is presented in this paper. This paper intends to explore the significance of both the approximate and detail coefficients through wavelet packet decomposition (WPD). Decomposition is done with “db3” wavelet function and the images are decomposed up to level-3 producing 84 sub-bands. Two features, the energy and the entropy are calculated for each sub-band producing two feature matrices (158 images × 84 features). The above step is purely a statistical measure based on WPD. To enhance the diagnostic accuracy, the second phase considers the structural (biological) region of interest (ROI) in the image and then extracts the same features. It is worthy to note that direct biological features are not extracted to eliminate the drawbacks of segmentation whereas the biologically significant region is taken as biological-ROI. Interestingly, the detailed coefficient sub-bands (prominent edges) show more significance in the biological-ROI phase. Apart from enhancing the diagnostic accuracy by feature reduction, the paper intends to mark the significance indices, uniqueness and discrimination capability of the significant features (sub-bands) in both the phases. Then, the crisp inputs are fed to the classifier ANN. Finally, from the significant features of the biological-ROI feature matrices, the accuracy is raised to 85% which is notable than the accuracy of 79% achieved without considering the ROI.
- Glaucoma,
- wavelet packet decomposition,
- feature reduction,
- feature significance,
- artificial neural networks

References

[1]	M. R. K. Mookiah, U. R. Acharya, C. M. Lim, A. Petznick, J. S. Suri. Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features. Knowledge-based Systems, vol. 33, no. 1, pp. 73-82, 2012.
[2]	J. Nayak, U. R. Acharya, P. S. Bhat, N. Shetty, T. C. Lim. Automated diagnosis of glaucoma using digital fundus images. Journal of Medical Systems, vol. 33, no. 5, pp. 337-346, 2009.
[3]	M. R. K. Mookiah, O. Faust. Automated glaucoma detection using hybrid feature extraction in retinal fundus images. Journal of Mechanics in Medicine and Biology, vol. 13, no. 1, Article number 1350011, 2013.
[4]	U. R. Acharya, S. Dua, X. Du, S. V. Sree, 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, pp. 449-455, 2011.
[5]	R. S. Asamwar, K. M. Bhurchandi, A. S. Gandhi. Interpolation of images using discrete wavelet transform to simulate image resizing as in human vision. International Journal of Automation and Computing, vol. 7, no. 1, pp. 9-16, 2010.
[6]	X. Yang. Wear state recognition of drills based on K-means cluster and radial basis function neural network. International Journal of Automation and Computing, vol. 7, no. 3, pp. 271-276, 2010.
[7]	N. N. Tsiaparas, S. Golemati, I. Andreadis, J. S. Stoitsis, I. Valavanis, K. S. Nikita. Comparison of multiresolution features for texture classification of carotid atherosclerosis from B-mode ultrasound. IEEE Transactions on Information Technology in Biomedicine, vol. 15, no. 1, pp. 130-137, 2011.
[8]	K. Huang, S. Aviyente. Wavelet feature selection for image classification. IEEE Transactions on Image Processing, vol. 17, no. 9, pp. 1709-1770, 2008.
[9]	S. Dua, U. R. Acharya, P. Chowriappa, S. VinithaSree. Wavelet-based energy features for glaucomatous image classification. IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 1, pp. 80-87, 2012.
[10]	A. M. Hasan, K. Samsudin, A. R. Ramli. Intelligently tuned wavelet parameters for GPS/INS error estimation. International Journal of Automation and Computing, vol. 8, no. 4, pp. 411-420, 2011.
[11]	J. E. W. Koh, M. R. K. Mookiah, N. A. Kadri. Application of Multiresolution analysis for the detection of glaucoma. Journal of Medical Imaging and Health Informatics, vol.3, no. 3, pp. 401-408, 2013.
[12]	N. Rajpoot. Local discriminant wavelet packet basis for texture classification. In Proceedings of SPIE, Wavelets: Applications in Signal and Image Processing X, Bellingham, USA, vol. 5207, pp. 774-783, 2003.
[13]	G. L. Fan, X. G. Xia. Wavelet-based texture analysis and synthesis using hidden Markov models. IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications, vol. 50, no. 1, pp. 106-120, 2003.
[14]	H. Chen, P. Varshney. Feature subset selection with applications to hyperspectral data. In Proceedings of IEEE International Conference on Acoustics, Speech, Signal Processing, IEEE, Syracuse, USA, vol. 2, pp. 249-252, 2005.
[15]	R. R. Coifman, M. V. Wickerhauser. Entropy-based algorithms for best basis selection. IEEE Transactions on Information Theory, vol. 38, no. 2, pp. 713-718, 1992.
[16]	K. Huang, S. Aviyente. Statistical partitioning of wavelet subbands for texture classification. In Proceedings of International Conference on Image Processing, IEEE, Michigan State University, East Lansing, USA, vol. 1, pp. I-441-I-444, 2005.
[17]	S. Z. Mahmoodabadi, A. Ahmadian, M. D. Abolhasani. ECG feature extraction using daubechies wavelets. In Proceedings of the 5th IASTED International Conference on Visualization, Imaging and Image Processing, Spain, pp. 343-348, 2005.
[18]	C. Raja, N. Gangatharan. Glaucoma detection in fundal retinal images using trispectrum and complex waveletbased features. European Journal of Scientific Research, vol. 97 no. 1, pp. 159-171, 2013.
[19]	C. Muramatsu, T. Nakagawa, A. Sawada, Y. Hatanaka, T. Yamamoto, H. Fujita, Automated determination of Cupto-disc ratio for classification of glaucomatous and normal eyes on stereo retinal fundus images. Journal of Biomedical Optics, vol. 16, no. 9, Article number 096009, 2011.
[20]	W. Zhu, N. Zeng, N.Wang. Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS Implementations. In Proceedings of the NESUG Health Care and Life Sciences, Baltimore, USA, pp. 1-9, 2010.

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Appropriate Sub-band Selection in Wavelet Packet Decomposition for Automated Glaucoma Diagnoses

Department of Electronics and Communication Engineering, Anjalai Ammal Mahalingam Engineering College, Kovilvenni, India;

Department of Electronics and Communication Engineering, R.M.K. College of Engineering and Technology, Puduvoyal 601206, India

Received: 2013-10-28

Key words:

Abstract: The most common reason for blindness among human beings is Glaucoma. The increase of fluid pressure damages the optic nerve which gradually leads to irreversible loss of vision. A technique for automated screening of Glaucoma from the fundal retinal images is presented in this paper. This paper intends to explore the significance of both the approximate and detail coefficients through wavelet packet decomposition (WPD). Decomposition is done with “db3” wavelet function and the images are decomposed up to level-3 producing 84 sub-bands. Two features, the energy and the entropy are calculated for each sub-band producing two feature matrices (158 images × 84 features). The above step is purely a statistical measure based on WPD. To enhance the diagnostic accuracy, the second phase considers the structural (biological) region of interest (ROI) in the image and then extracts the same features. It is worthy to note that direct biological features are not extracted to eliminate the drawbacks of segmentation whereas the biologically significant region is taken as biological-ROI. Interestingly, the detailed coefficient sub-bands (prominent edges) show more significance in the biological-ROI phase. Apart from enhancing the diagnostic accuracy by feature reduction, the paper intends to mark the significance indices, uniqueness and discrimination capability of the significant features (sub-bands) in both the phases. Then, the crisp inputs are fed to the classifier ANN. Finally, from the significant features of the biological-ROI feature matrices, the accuracy is raised to 85% which is notable than the accuracy of 79% achieved without considering the ROI.

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Reference (20)

[1]	M. R. K. Mookiah, U. R. Acharya, C. M. Lim, A. Petznick, J. S. Suri. Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features. Knowledge-based Systems, vol. 33, no. 1, pp. 73-82, 2012.
[2]	J. Nayak, U. R. Acharya, P. S. Bhat, N. Shetty, T. C. Lim. Automated diagnosis of glaucoma using digital fundus images. Journal of Medical Systems, vol. 33, no. 5, pp. 337-346, 2009.
[3]	M. R. K. Mookiah, O. Faust. Automated glaucoma detection using hybrid feature extraction in retinal fundus images. Journal of Mechanics in Medicine and Biology, vol. 13, no. 1, Article number 1350011, 2013.
[4]	U. R. Acharya, S. Dua, X. Du, S. V. Sree, 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, pp. 449-455, 2011.
[5]	R. S. Asamwar, K. M. Bhurchandi, A. S. Gandhi. Interpolation of images using discrete wavelet transform to simulate image resizing as in human vision. International Journal of Automation and Computing, vol. 7, no. 1, pp. 9-16, 2010.
[6]	X. Yang. Wear state recognition of drills based on K-means cluster and radial basis function neural network. International Journal of Automation and Computing, vol. 7, no. 3, pp. 271-276, 2010.
[7]	N. N. Tsiaparas, S. Golemati, I. Andreadis, J. S. Stoitsis, I. Valavanis, K. S. Nikita. Comparison of multiresolution features for texture classification of carotid atherosclerosis from B-mode ultrasound. IEEE Transactions on Information Technology in Biomedicine, vol. 15, no. 1, pp. 130-137, 2011.
[8]	K. Huang, S. Aviyente. Wavelet feature selection for image classification. IEEE Transactions on Image Processing, vol. 17, no. 9, pp. 1709-1770, 2008.
[9]	S. Dua, U. R. Acharya, P. Chowriappa, S. VinithaSree. Wavelet-based energy features for glaucomatous image classification. IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 1, pp. 80-87, 2012.
[10]	A. M. Hasan, K. Samsudin, A. R. Ramli. Intelligently tuned wavelet parameters for GPS/INS error estimation. International Journal of Automation and Computing, vol. 8, no. 4, pp. 411-420, 2011.
[11]	J. E. W. Koh, M. R. K. Mookiah, N. A. Kadri. Application of Multiresolution analysis for the detection of glaucoma. Journal of Medical Imaging and Health Informatics, vol.3, no. 3, pp. 401-408, 2013.
[12]	N. Rajpoot. Local discriminant wavelet packet basis for texture classification. In Proceedings of SPIE, Wavelets: Applications in Signal and Image Processing X, Bellingham, USA, vol. 5207, pp. 774-783, 2003.
[13]	G. L. Fan, X. G. Xia. Wavelet-based texture analysis and synthesis using hidden Markov models. IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications, vol. 50, no. 1, pp. 106-120, 2003.
[14]	H. Chen, P. Varshney. Feature subset selection with applications to hyperspectral data. In Proceedings of IEEE International Conference on Acoustics, Speech, Signal Processing, IEEE, Syracuse, USA, vol. 2, pp. 249-252, 2005.
[15]	R. R. Coifman, M. V. Wickerhauser. Entropy-based algorithms for best basis selection. IEEE Transactions on Information Theory, vol. 38, no. 2, pp. 713-718, 1992.
[16]	K. Huang, S. Aviyente. Statistical partitioning of wavelet subbands for texture classification. In Proceedings of International Conference on Image Processing, IEEE, Michigan State University, East Lansing, USA, vol. 1, pp. I-441-I-444, 2005.
[17]	S. Z. Mahmoodabadi, A. Ahmadian, M. D. Abolhasani. ECG feature extraction using daubechies wavelets. In Proceedings of the 5th IASTED International Conference on Visualization, Imaging and Image Processing, Spain, pp. 343-348, 2005.
[18]	C. Raja, N. Gangatharan. Glaucoma detection in fundal retinal images using trispectrum and complex waveletbased features. European Journal of Scientific Research, vol. 97 no. 1, pp. 159-171, 2013.
[19]	C. Muramatsu, T. Nakagawa, A. Sawada, Y. Hatanaka, T. Yamamoto, H. Fujita, Automated determination of Cupto-disc ratio for classification of glaucomatous and normal eyes on stereo retinal fundus images. Journal of Biomedical Optics, vol. 16, no. 9, Article number 096009, 2011.
[20]	W. Zhu, N. Zeng, N.Wang. Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS Implementations. In Proceedings of the NESUG Health Care and Life Sciences, Baltimore, USA, pp. 1-9, 2010.

Appropriate Sub-band Selection in Wavelet Packet Decomposition for Automated Glaucoma Diagnoses

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