Abstract
The research presented in this article is aimed at developing an automated imaging system for classification of tissues in medical images obtained from Computed Tomography (CT) scans. The article focuses on using multi-resolution texture analysis, specifically: the Haar wavelet, Daubechies wavelet, Coiflet wavelet, and the ridgelet. The algorithm consists of two steps: automatic extraction of the most discriminative texture features of regions of interest and creation of a classifier that automatically identifies the various tissues. The classification step is implemented using a cross-validation Classification and Regression Tree approach. A comparison of wavelet-based and ridgelet-based algorithms is presented. Tests on a large set of chest and abdomen CT images indicate that, among the three wavelet-based algorithms, the one using texture features derived from the Haar wavelet transform clearly outperforms the one based on Daubechies and Coiflet transform. The tests also show that the ridgelet-based algorithm is significantly more effective and that texture features based on the ridgelet transform are better suited for texture classification in CT medical images.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Xu, D., Lee, J., Raicu, D.S., Furst, J.D., Channin, D.: Texture Classification of Normal Tissues in Computed Tomography. In: The 2005 Annual Meeting of the Society for Computer Applications in Radiology (2005)
Channin, D., Raicu, D.S., Furst, J.D., Xu, D.H., Lilly, L., Limpsangsri, C.: Classification of Tissues in Computed Tomography using Decision Trees. Poster and Demo. In: The 90th Scientific Assembly and Annual Meeting of Radiology Society of North America (2004)
Semler, L., Dettori, L., Furst, J.: Wavelet-Based Texture Classification of Tissues in Computed Tomography. In: Proceedings of the 18th IEEE International Symposium on Computer-Based Medical Systems, pp. 265–270. IEEE Computer Society Press, Los Alamitos (2005)
Do, M.N., Vetterli, M.: The Finite Ridgelet Transform for Image Representation. IEEE Transactions on Image Processing 12, 16–28 (2003)
LeBorgne, H.L., O’Connor, N, Natural Scene Classification and Retrieval Using Ridgelet-based Image Signatures. Advanced Concepts for Intelligent Vision Systems, pp. 20–23 (2005)
Starck, J.L., Donoho, D.L., Candes, E.J.: Astronomical Image Representation by the Curvelet Transform. Astronomy &Astrophysics 398, 785–800 (1999)
Semler, L., Dettori, L., Kerr, B.: Ridgelet-Based Texture Classification in Computed Tomography. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (submitted)
Dara, B., Watsuji, N.: Using Wavelets for Texture Classification. In: IJCI Proceedings of International Conference on Signal Processing, ISN 1304-2386 (2003)
Mulcahy, C.: Image Compression Using the Haar Wavelet Transform. Spelman Science & Math Journal 1, 22–31 (1997)
Li, J.: A Wavelet Approach to Edge Detection, Master of Science thesis, Sam Houston State University. Huntsville, Texas (2003)
Do, M., Vetterli, M.: Image Denoising Using Orthonormal Finite Ridgelet Transform. Proceedings of SPIE: Wavelet Applications in Signal and Image Processing 4119, 831–842 (2003)
Haralick, R.M., Shanmugam, D., Dinstein, I.: Texture Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics 3(6), 610–621 (1973)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Semler, L., Dettori, L. (2007). A Comparison of Wavelet-Based and Ridgelet-Based Texture Classification of Tissues in Computed Tomography. In: Braz, J., Ranchordas, A., Araújo, H., Jorge, J. (eds) Advances in Computer Graphics and Computer Vision. Communications in Computer and Information Science, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75274-5_16
Download citation
DOI: https://doi.org/10.1007/978-3-540-75274-5_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75272-1
Online ISBN: 978-3-540-75274-5
eBook Packages: Computer ScienceComputer Science (R0)