A fractal dimension based optimal wavelet packet analysis technique for classification of meningioma brain tumours

Proceedings - International Conference on Image Processing, ICIP

Volumn , Issue , 2009, Pages 4177-4180

  Al Kadi, Omar S ^a  

^a UNIVERSITY OF SUSSEX   (United Kingdom)

Author keywords

Bayesian classification; Fractal dimension; Multiresolution representation; Texture analysis; Wavelet packet

Indexed keywords

ADAPTIVE ALGORITHMS; BAYESIAN NETWORKS; FINITE DIFFERENCE METHOD; IMAGE PROCESSING; IMAGING SYSTEMS; OPTIMIZATION; PACKET NETWORKS; PARTIAL DISCHARGES; SIGNAL PROCESSING; TEXTURES; TREES (MATHEMATICS);

BAYESIAN CLASSIFICATION; BEST BASIS; BEST BASIS SELECTION; BRAIN TUMOURS; CLASSIFICATION ACCURACY; CO-OCCURRENCE-MATRIX; FEATURE VECTORS; FRACTAL SIGNATURES; FREQUENCY CHANNELS; HISTOPATHOLOGICAL IMAGES; INPUT SIGNAL; MENINGIOMAS; MULTI RESOLUTION REPRESENTATION; MULTIRESOLUTION WAVELETS; OPTIMAL BASIS; OPTIMAL WAVELETS; SELECTION CRITERIA; SUB-BANDS; TEXTURE ANALYSIS; TISSUE TEXTURES; WAVELET PACKET;

FRACTAL DIMENSION;

EID: 77951969434     PISSN: 15224880     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIP.2009.5414534     Document Type: Conference Paper

References (21)

1. F. DeMonte, M. R. Gilbert, A. Mahajan, and I. E. McCutcheon Tumors of the Brain and Spine 1ed.: Springer, 2007.
2. A. D. Ramsay, "Errors in histopathology reporting: detection and avoidance," Histopathology, vol. 34, pp. 481-490, Jun 1999.
3. R. R. Coifman and M. V. Wickerhauser, "Entropy-based algorithms for best basis selection," IEEE Trans. Inform. Theory, vol. 38, pp. 713-718, Mar 1992.
4. A. Laine and J. Fan, "Texture classification by wavelet packet signatures," IEEE Trans. Pattern Anal. and Machine Intell., vol. 15, pp. 1186-1191, Nov 1993.
5. N. Saito, R. R. Coifman, F. B. Geshwind, and F. Warner, "Discriminant feature extraction using empirical probability density estimation and a local basis library," Pattern Recognition, vol. 35, pp. 2841-2852, Dec 2002.
6. N. Rajpoot, "Local discriminant wavelet packet basis for texture classification," in Proc. SPIE Wavelets X, San Diego, California, 2003, pp. 774-783.
7. F. G. Meyer and J. Chinrungrueng, "Analysis of event-related fMRI data using best clustering bases," IEEE Trans. Med. Imaging, vol. 22, pp. 933-939, Aug 2003.
8. T. Chang and C. C. J. Kuo, "Texture analysis and classification with tree-structured wavelet transform," IEEE Trans. Image Processing, vol. 2, pp. 429-441, 1993.
9. M. Acharyya, R. K. De, and M. K. Kundu, "Extraction of features using M-band wavelet packet frame and their neuro-fuzzy evaluation for multitexture segmentation," IEEE Trans. Pattern Anal. and Machine Intell., vol. 25, pp. 1639-1644, Dec 2003.
10. B. Lessmann, T. W. Nattkemper, V. H. Hans, and A. Degenhard, "A method for linking computed image features to histological semantics in neuropathology," J. Biomed. Inform., vol. 40, pp. 631-641, 2007.
11. H. Qureshi, O. Sertel, N. Rajpoot, R. Wilson, and M. Gurcan, "Adaptive Discriminant Wavelet Packet Transform and Local Binary Patterns for Meningioma Subtype Classification," in Proc. MICCAI, 2008, vol. 5242, pp. 196-204, 2008.
12. H. Qureshi, N. Rajpoot, R. Wilson, T. Nattkemper, and V. Hans, "Comparative analysis of discriminant wavelet packet features and raw image features for classification of meningioma subtypes," in Proc. MIUA, UK, 2007.
13. O. S. Al-Kadi and D. Watson, "Texture Analysis of Aggressive and non-Aggressive Lung Tumor CE CT Images," IEEE Trans. Biomed. Eng., vol. 55, pp. 1822-1830, 2008.
14. M. E. Mavroforakis, H. V. Georgiou, N. Dimitropoulos, D. Cavouras, and S. Theodoridis, "Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers," Artificial Intell. in Med., vol. 37, pp. 145-162,2006.
15. K. M. Iftekharuddin, W. Jia, and R. Marsh, "Fractal analysis of tumor in brain MR images," Machine Vision and Applications, vol. 13, pp. 352-362, Mar 2003.
16. W.-L. Lee, Y. Chen, and K. Hsieh, "Ultrasonic liver tissue classification by fractal feature vector based on M-band wavelet transform," IEEE Trans. Med. Imaging, vol. 22, pp. 382-392,2003.
17. S. G. Mallat, "A theory for multiresolution signal decomposition - the wavelet representation," IEEE Trans. Pattern Anal. and Machine Intell., vol. 11, pp. 674-693, Jul 1989.
18. I. Daubechies, "The wavelet transform, time-frequency localization and signal analysis," IEEE Trans. Inform. Theory, vol. 36, pp. 961-1005, 1990.
19. C. C. Chen, J. S. Daponte, and M. D. Fox, "Fractal Feature Analysis and Classification in Medical Imaging," IEEE Trans. Med. Imaging, vol. 8, pp. 133-142, 1989.
20. P. Domingos and M. Pazzani, "On the optimality of the simple Bayesian classifier under zero-one loss," Machine Learning, vol. 29, pp. 103-130, Nov-Dec 1997.
21. A. K. Jain, R. P. W. Duin, and J. C. Mao, "Statistical pattern recognition: A review," IEEE Trans. Pattern Anal. and Machine Intell., vol. 22, pp. 4-37, 2000.