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Journal of Medical Engineering
Volume 2013 (2013), Article ID 104684, 13 pages
Hybrid Discrete Wavelet Transform and Gabor Filter Banks Processing for Features Extraction from Biomedical Images
Department of Computer Science, University of Quebec at Montreal, 201 President-Kennedy, Local PK-4150,
Montreal, QC, Canada H2X 3Y7
Received 13 December 2012; Revised 12 March 2013; Accepted 27 March 2013
Academic Editor: Ying Zhuge
Copyright © 2013 Salim Lahmiri and Mounir Boukadoum. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- A. Khademi and S. Krishnan, “Shift-invariant discrete wavelet transform analysis for retinal image classification,” Medical and Biological Engineering and Computing, vol. 45, no. 12, pp. 1211–1222, 2007.
- N. Lee, A. F. Laine, and T. R. Smith, “Learning non-homogenous textures and the unlearning problem with application to drusen detection in retinal images,” in Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI '08), pp. 1215–1218, Paris, France, May 2008.
- A. Dong and B. Wang, “Feature selection and analysis on mammogram classification,” in Proceedings of the IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM '09), pp. 731–735, Victoria, BC, Canada, August 2009.
- A. Tirtajaya and D. D. Santika, “Classification of microcalcification using dual-tree complex wavelet transform and support vector machine,” in Proceedings of the 2nd International Conference on Advances in Computing, Control and Telecommunication Technologies (ACT '10), pp. 164–166, Jakarta, Indonesia, December 2010.
- F. Moayedi, Z. Azimifar, R. Boostani, and S. Katebi, “Contourlet-based mammography mass classification using the SVM family,” Computers in Biology and Medicine, vol. 40, no. 4, pp. 373–383, 2010.
- S. Chaplot, L. M. Patnaik, and N. R. Jagannathan, “Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network,” Biomedical Signal Processing and Control, vol. 1, no. 1, pp. 86–92, 2006.
- Y. Zhang, S. Wang, and L. Wu, “A novel method for magnetic resonance brain image classification based on adaptive chaotic PSO,” Progress in Electromagnetics Research, vol. 109, pp. 325–343, 2010.
- Y. Zhang, Z. Dong, L. Wu, and S. Wang, “A hybrid method for MRI brain image classification,” Expert Systems with Applications, vol. 38, no. 8, pp. 10049–10053, 2011.
- M. E. Celebi, H. Iyatomi, G. Schaefer, and W. V. Stoecker, “Lesion border detection in dermoscopy images,” Computerized Medical Imaging and Graphics, vol. 33, no. 2, pp. 148–153, 2009.
- Q. Abbas, M. E. Celebi, and I. F. García, “Skin tumor area extraction using an improved dynamic programming approach,” Skin Research and Technology, vol. 18, pp. 133–142, 2012.
- Q. Li, F. Li, and K. Doi, “Computerized detection of lung nodules in thin-section CT images by use of selective enhancement filters and an automated rule-based classifier,” Academic Radiology, vol. 15, no. 2, pp. 165–175, 2008.
- A. El-Bazl, M. Nitzken, E. Vanbogaertl, G. Gimel'jarb, R. Falfi, and M. Abo El-Ghar, “A novel shaped-based diagnostic approach for early diagnosis of lung nodules,” in Proceedings of the IEEE International Symposium in Biomedical Imaging (ISBI '11), pp. 137–140, Chicago, Ill, USA, 2011.
- M. T. Coimbra and J. P. S. Cunha, “MPEG-7 visual descriptors—contributions for automated feature extraction in capsule endoscopy,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 16, no. 5, pp. 628–636, 2006.
- B. Li and M. Q. H. Meng, “Texture analysis for ulcer detection in capsule endoscopy images,” Image and Vision Computing, vol. 27, no. 9, pp. 1336–1342, 2009.
- C. K. Chui, An Introduction to Wavelets, Academic Press, San Diego, Calif, USA, 1992.
- M. Vetterli and C. Herley, “Wavelets and filter banks: theory and design,” IEEE Transactions on Signal Processing, vol. 40, no. 9, pp. 2207–2232, 1992.
- J. G. Daugman, “Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters,” Journal of the Optical Society of America A, vol. 2, no. 7, pp. 1160–1169, 1985.
- I. W. Selesnick, R. G. Baraniuk, and N. G. Kingsbury, “The dual-tree complex wavelet transform,” IEEE Signal Processing Magazine, vol. 22, no. 6, pp. 123–151, 2005.
- E. Candès and D. Donoho, “Ridgelets: a key to higher-dimensional intermittency?” Philosophical Transactions of the London Royal Society, vol. 357, pp. 2495–2509, 1999.
- E. J. Candès and D. L. Donoho, “Continuous curvelet transform—I. Resolution of the wavefront set,” Applied and Computational Harmonic Analysis, vol. 19, no. 2, pp. 162–197, 2005.
- M. N. Do and M. Vetterli, “The contourlet transform: an efficient directional multiresolution image representation,” IEEE Transactions on Image Processing, vol. 14, no. 12, pp. 2091–2106, 2005.
- R. J. Ferrari, R. M. Rangayyan, J. E. L. Desautels, and A. F. Frère, “Analysis of asymmetry in mammograms via directional filtering with Gabor wavelets,” IEEE Transactions on Medical Imaging, vol. 20, no. 9, pp. 953–964, 2001.
- Z. Cui and G. Zhang, “A novel medical image dynamic fuzzy classification model based on ridgelet transform,” Journal of Software, vol. 5, no. 5, pp. 458–465, 2010.
- T. Gebäck and P. Koumoutsakos, “Edge detection in microscopy images using curvelets,” BMC Bioinformatics, vol. 10, article 75, 2009.
- J. Ma and G. Plonka, “The curvelet transform: a review of recent applications,” IEEE Signal Processing Magazine, vol. 27, no. 2, pp. 118–133, 2010.
- N. Kingsbury, “Complex wavelets and shift invariance,” in Proceedings of the IEEE Seminar on Time-Scale and Time-Frequency Analysis and Applications, pp. 501–510, London, UK, 2000.
- Y. L. Qiao, C. Y. Song, and C. H. Zhao, “M-band ridgelet transform based texture classification,” Pattern Recognition Letters, vol. 31, no. 3, pp. 244–249, 2010.
- F. Gómez and E. Romero, “Texture characterization using a curvelet based descriptor,” Lecture Notes in Computer Science, vol. 5856, pp. 113–120, 2009.
- H. Shan and J. Ma, “Curvelet-based geodesic snakes for image segmentation with multiple objects,” Pattern Recognition Letters, vol. 31, no. 5, pp. 355–360, 2010.
- R. Eslami and H. Radha, “New image transforms using hybrid wavelets and directional filter banks: analysis and design,” in Proceedings of the IEEE International Conference on Image Processing (ICIP '05), pp. 733–736, Genova, Italy, September 2005.
- O. O. V. Villegas and V. G. C. Sánchez, “The wavelet based contourlet transform and its application to feature preserving image coding,” Lecture Notes in Computer Science, vol. 4827, pp. 590–600, 2007.
- S. Lahmir and M. Boukadoum, “Classification of brain MRI using the LH and HL wavelet transform sub-bands,” in Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS '11), pp. 1025–1028, Rio de Janeiro, Brazil, May 2009 2011.
- S. Lahmir and M. Boukadoum, “Brain MRI classification using an ensemble system and LH and HL wavelet Sub-bands Features,” in Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI '11), pp. 1–7, Paris, France, April 2011.
- S. Lahmir and M. Boukadoum, “Hybrid Cosine and Radon Transform-based processing for Digital Mammogram Feature Extraction and Classification with SVM,” in Proceedings of the 33rd IEEE Annual International Conference on Engineering in Medecine and Biology Society (EMBS '11), pp. 5104–5107, Boston, Mass, USA, 2011.
- S. Lahmir and M. Boukadoum, “DWT and RT-Based Approach for Feature Extraction and classification of Mammograms with SVM,” in Proceedings of the IEEE Biomedical Circuits and Systems Conference (BioCAS '11), pp. 412–415, San Diego, Calif, USA, November 2011.
- V. N. Vapnik, The Nature of Statistical Learning Theory, Springer, 1995.
- S. Lahmiri and M. Boukadoum, “Hybrid discret wavelet transform and Gabor filter banks processing for mammogram features extraction,” in Proceedings of the IEEE New Circuits and Systems (NEWCAS '11), pp. 53–56, Bordeaux, France, June 2011.
- L. M. Bruce and N. Shanmugam, “Using neural networks with wavelet transforms for an automated mammographic mass classifier,” in Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 985–987, Chicago, Ill, USA, July 2000.
- S. M. H. Jamarani, G. Rezai-rad, and H. Behnam, “A novel method for breast cancer prognosis using wavelet packet based neural network,” in Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society (IEEE-EMBS '05), pp. 3414–3417, Shanghai, China, September 2005.
- C. I. O. Martins, F. N. S. Medeiros, R. M. S. Veras, F. N. Bezerra, and R. M. Cesar Jr., “Evaluation of retinal vessel segmentation methods for microaneurysms detection,” in Proceedings of the IEEE International Conference on Image Processing (ICIP '09), pp. 3365–3368, Cairo, Egypt, November 2009.
- C. Plant, S. J. Teipel, A. Oswald et al., “Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease,” NeuroImage, vol. 50, no. 1, pp. 162–174, 2010.
- J. Meier, R. Bock, L. G. Nyúl, and G. Michelson, “Eye fundus image processing system for automated glaucoma classification,” in Proceedings of the 52nd Internationales Wissenschaftliches Kolloquium, Technische Universität Ilmenau, 2007.
- E. Sakka, A. Prentza, I. E. Lamprinos, and D. Koutsouris, “Microcalcification detection using multiresolution analysis based on wavelet transform,” in Proceedings of the IEEE International Special Topic Conference on Information Technology in Biomedicine, Ioannina, Greece, October 2006.
- S. G. Mallat, “Theory for multiresolution signal decomposition: the wavelet representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 7, pp. 674–693, 1989.
- J. Suckling, J. Parker, and D. R. Dance, “The mammographic image analysis society digital mammogram database,” in Proceedings of the the 2nd International Workshop on Digital Mammography, A. G. Gale, S. M. Astley, D. D. Dance, and A. Y. Cairns, Eds., pp. 375–378, Elsevier, York, UK, 1994.
- H. J. Chiou, C. Y. Chen, T. C. Liu et al., “Computer-aided diagnosis of peripheral soft tissue masses based on ultrasound imaging,” Computerized Medical Imaging and Graphics, vol. 33, no. 5, pp. 408–413, 2009.
- J. K. Kim, J. M. Park, K. S. Song, and H. W. Park, “Adaptive mammographic image enhancement using first derivative and local statistics,” IEEE Transactions on Medical Imaging, vol. 16, no. 5, pp. 495–502, 1997.
- H. S. Sheshadri and A. Kandaswamy, “Breast tissue classification using statistical feature extraction of mammograms,” Medical Imaging and Information Sciences, vol. 23, no. 3, pp. 105–107, 2006.
- N. Cristianini and J. Shawe-Taylor, Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press, Cambridge, UK, 2000.
- L. Chen, X. Mao, Y. Xue, and L. L. Cheng, “Speech emotion recognition: features and classification models,” Digital Signal Processing, vol. 22, pp. 1154–1160, 2012.
- F. Palmieri, U. Fiore, A. Castiglione, and A. De Santis, “On the detection of card-sharing traffic through wavelet analysis and Support Vector Machines,” Applied Soft Computing, vol. 13, no. 1, pp. 615–627, 2013.
- A. Azadeh, M. Saberi, A. Kazem, V. Ebrahimipour, A. Nourmohammadzadeh, and Z. Saberi, “A flexible algorithm for fault diagnosis in a centrifugal pump with corrupted data and noise based on ANN and support vector machine with hyper-parameters optimization,” Applied Soft Computing, vol. 13, no. 3, pp. 1478–1485, 2013.
- R. J. Martis, U. R. Acharya, K. M. Mandana, A. K. Ray, and C. Chakraborty, “Cardiac decision making using higher order spectra,” Biomedical Signal Processing and Control, vol. 8, pp. 193–203, 2013.
- M. R. Mohammad, “Chi-square distance kernel of the gaits for the diagnosis of Parkinson’s disease,” Biomedical Signal Processing and Control, vol. 8, pp. 66–70, 2013.
- R. Vandenberghe, N. Nelissen, E. Salmon et al., “Binary classification of 18F-flutemetamol PET using machine learning: comparison with visual reads and structural MRI,” NeuroImage, vol. 64, no. 1, pp. 517–525, 2013.