Abstract
The inherent complexity and non-homogeneity of texture makes classification in medical image analysis a challenging task. In this paper, we propose a combined approach for meningioma subtype classification using subband texture (macro) features and micro-texture features. These are captured using the Adaptive Wavelet Packet Transform (ADWPT) and Local Binary Patterns (LBPs), respectively. These two different textural features are combined together and used for classification. The effect of various dimensionality reduction techniques on classification performance is also investigated. We show that high classification accuracies can be achieved using ADWPT. Although LBP features do not provide higher overall classification accuracies than ADWPT, it manages to provide higher accuracy for a meningioma subtype that is difficult to classify otherwise.
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Keywords
- Support Vector Machine
- Local Binary Pattern
- Wavelet Packet
- Dimensionality Reduction Technique
- Wavelet Packet Transform
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References
Kleihues, P., Cavenee, W.K.: World Health Organization Classification of Tumours. In: Pathology and Genetics. Tumours of the Nervous System. IARC Press (2000)
Burger, P.: What is an oligodendroglioma? Brain Pathol. 12, 257–259 (2002)
Lessmann, B., Hans, V., Degenhard, A., Nattkemper, T.W.: Feature space exploration of pathology images using content-based database visualization. In: Proceedings SPIE Medical Imaging (2006)
Wirjadi, O., Breuel, T., Feiden, W., Kim, Y.J.: Automated feature selection for the classification of meningioma cell nuclei. In: Handels, H., Ehrhardt, J., Horsch, A., Meinzer, H.P., Tolxdorff, T. (eds.) Bildverarbeitung für die Medizin, pp. 76–80. Informatik Aktuell, Springer (2006)
Unser, M., Eden, M.: Multiresolution feature extraction and selection for texture segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11(7), 717–728 (1989)
Leung, M.M., Peterson, A.M.: Scale and rotation invariant texture classification. In: Record of The Twenty-Sixth Asilomar Conference on Signals, Systems and Computers (1992)
Porter, R., Canagarajah, N.: Gabor filters for rotation invariant texture classification. In: Proceedings of 1997 IEEE International Circuits and Systems (1997)
Valkealahti, K., Oja, E.: Reduced multidimensional co-occurrence histograms in texture classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(1), 90–94 (1998)
Rajpoot, N.: Local discriminant wavelet packet basis for texture classification. In: Proceedings SPIE Wavelets X, San Diego, California (2003)
Coifman, R.R., Wickerhauser, M.V.: Entropy-based algorithms for best basis selection. IEEE Transactions on Information Theory 38(2), 713–718 (1992)
Ojala, T., Pietikainen, M.: Unsupervised texture segmentation using feature distributions. Pattern Recognition 32, 477–486 (1999)
Ruiz, A., Sertel, O., Ujaldon, M., Catalyurek, U., Saltz, J., Gurcan, M.: Pathological image analysis using the gpu: Stroma classification for neuroblastoma. In: Proc. of IEEE Int. Conf. on Bioinformatics and Biomedicine (BIBM), pp. 78–85 (2007)
Sertel, O., Kong, J., Shimada, H., Catalyurek, U., Saltz, J., Gurcan, M.: Computr-aided prognosis of neuroblastoma: Classification of stromal development on whole-slide images. In: Proc. of SPIE Medical Imaging (2008)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. on PAMI 24, 971–987 (2002)
Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: A review. IEEE Trans. on PAMI 22, 4–37 (2000)
Duda, R.O., Hart, P.E., Stork, G.: Pattern Classification. John Wiley, Chichester (2001)
Coifman, R., Lafon, S.: Diffusion maps. Applied and Computational Harmonic Analysis. Special Issue on diffusion maps and wavelets 21, 5–30 (July 2006)
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Qureshi, H., Sertel, O., Rajpoot, N., Wilson, R., Gurcan, M. (2008). Adaptive Discriminant Wavelet Packet Transform and Local Binary Patterns for Meningioma Subtype Classification. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008. MICCAI 2008. Lecture Notes in Computer Science, vol 5242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85990-1_24
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DOI: https://doi.org/10.1007/978-3-540-85990-1_24
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