Citation: | 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. https://doi.org/10.1007/s11633-014-0858-6 |
[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.
|