All Title Author
Keywords Abstract

Multiresolution Analysis Using Wavelet, Ridgelet, and Curvelet Transforms for Medical Image Segmentation

DOI: 10.1155/2011/136034

Full-Text   Cite this paper   Add to My Lib


The experimental study presented in this paper is aimed at the development of an automatic image segmentation system for classifying region of interest (ROI) in medical images which are obtained from different medical scanners such as PET, CT, or MRI. Multiresolution analysis (MRA) using wavelet, ridgelet, and curvelet transforms has been used in the proposed segmentation system. It is particularly a challenging task to classify cancers in human organs in scanners output using shape or gray-level information; organs shape changes throw different slices in medical stack and the gray-level intensity overlap in soft tissues. Curvelet transform is a new extension of wavelet and ridgelet transforms which aims to deal with interesting phenomena occurring along curves. Curvelet transforms has been tested on medical data sets, and results are compared with those obtained from the other transforms. Tests indicate that using curvelet significantly improves the classification of abnormal tissues in the scans and reduce the surrounding noise. 1. Introduction In the last decade, the use of 3D image processing has been increased especially for medical applications; this leads to increase the qualified radiologists’ number who navigate, view, analyse, segment, and interpret medical images. The analysis and visualization of the image stack received from the acquisition devices are difficult to evaluate due to the quantity of clinical data and the amount of noise existing in medical images due to the scanners itself. Computerized analysis and automated information systems can offer help dealing with the large amounts of data, and new image processing techniques may help to denoise those images. Multiresolution analysis (MRA) [1–3] has been successfully used in image processing specially with image segmentation, wavelet-based features has been used in various applications including image compression [4], denoising [5], and classification [6]. Recently, the finite ridgelet and curvelet transforms have been introduced as a higher dimensional MRA tool [7, 8]. Image segmentation requires extracting specific features from an image by distinguishing objects from the background. The process involves classifying each pixel of an image into a set of distinct classes, where the number of classes is much smaller. Medical image segmentation aims to separate known anatomical structures from the background such cancer diagnosis, quantification of tissue volumes, radiotherapy treatment planning, and study of anatomical structures. Segmentation can be manually performed by a human


[1]  J. L. Starck, M. Elad, and D. Donoho, “Redundant multiscale transforms and their application for morphological component separation,” Advances in Imaging and Electron Physics, vol. 132, pp. 287–348, 2004.
[2]  A. Mojsilovic, M. Popovic, S. Markovic, and M. Krstic, “Characterization of visually similar diffuse diseases from B-scan liver images using nonseparable wavelet transform,” IEEE Transactions on Medical Imaging, vol. 17, no. 4, pp. 541–549, 1998.
[3]  S. Alzu'bi and A. Amira, “3D medical volume segmentation using hybrid multiresolution statistical approaches,” Advances in Artificial Intelligence, vol. 2010, Article ID 520427, 15 pages, 2010.
[4]  C. Mulcahy, “Image compression using the Haar wavelet transform,” Spelman Science and Mathematics Journal, vol. 1, pp. 22–31, 1997.
[5]  W. Fourati, F. Kammoun, and M. S. Bouhlel, “Medical image denoising using wavelet thresholding,” Journal of Testing and Evaluation, vol. 33, no. 5, pp. 364–369, 2005.
[6]  B. Kara and N. Watsuji, “Using wavelets for texture classification,” in IJCI Proceedings of International Conference on Signal Processing, pp. 920–924, September 2003.
[7]  M. N. Do and M. Vetterli, “The finite ridgelet transform for image representation,” IEEE Transactions on Image Processing, vol. 12, no. 1, pp. 16–28, 2003.
[8]  M. Do and M. Vetterli, “Image denoising using orthonormal finite ridgelet transform,” in Wavelet Applications in Signal and Image Processing, vol. 4119 of Proceedings of SPIE, pp. 831–842, 2003.
[9]  E. Candes, L. Demanet, D. Donoho, and L. Ying, “Fast discrete curvelet transform,” SIAM: Multiscale Modeling and Simulation, vol. 5, no. 3, pp. 861–899, 2006.
[10]  I. Daubechies, “Wavelet transforms and orthonormal wavelet bases, different perspectives on wavelets,” in Proceedings of the Symposia in Applied Mathematics, vol. 47, pp. 1–33, American Mathematical Society, San Antonio, Tex, USA, 1993.
[11]  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.
[12]  K. Rajpoot and N. Rajpoot, “Hyperspectral colon tissue cell classiffication,” in Medical Imaging, Proceedings of SPIE, 2004.
[13]  I. S. Uzun and A. Amira, “Design and fpga implementation of finite ridgelet transform,” in Proceedings of the IEEE International Symposium on Circuits and Systems, (ISCAS '05), vol. 6, pp. 5826–5829, May 2005.
[14]  E. J. Stollnitz, T. D. DeRose, and D. H. Salesin, “Wavelets for computer graphics: a primer, part 1,” IEEE Computer Graphics and Applications, vol. 15, no. 3, pp. 76–84, 2002.
[15]  A. Haar, “Theorie der Orthogonalen Funkt Ionensysteme,” Mathematische Annalen, vol. 69, no. 3, pp. 331–371, 1910.
[16]  D. L. Donoho, “Ridge functions and orthonormal ridgelets,” Journal of Approximation Theory, vol. 111, no. 2, pp. 143–179, 2001.
[17]  M. N. Do and M. Vetterli, “Orthonormal finite ridgelet transform for image compression,” in Proceedings of the International Conference on Image Processing, (ICIP '00), pp. 367–370, September 2000.
[18]  E. Cand'es and D. Donoho, A Surprisingly Effective Non adaptive Representation for Objects With Edges, Curves and Surfaces, Vanderbilt University Press, Nashville, Tenn, USA, 2000.
[19]  E. Candes, Ridgelets: theory and application, Ph.D. thesis, Department of Statistics, Stanford University, Stanford, Calif, USA.
[20]  E. J. Candes and D. L. Donoho, “Ridgelets: a key to higher-dimensional intermittency?” Philosophical Transactions of the Royal Society A, vol. 357, no. 1760, pp. 2495–2509, 1999.
[21]  J. L. Starck, E. J. Candes, and D. L. Donoho, “The curvelet transform for image denoising,” IEEE Transactions on Image Processing, vol. 11, no. 6, pp. 670–684, 2002.
[22]  J. He, “A characterization of inverse Radon transform on the Laguerre hypergroup,” Journal of Mathematical Analysis and Applications, vol. 318, no. 1, pp. 387–395, 2006.
[23]  L. Dettori and L. Semler, “A comparison of wavelet, ridgelet, and curvelet-based texture classification algorithms in computed tomography,” Computers in Biology and Medicine, vol. 37, no. 4, pp. 486–498, 2007.
[24]  Computed Tomography Scanner, King Abdullah University Hospital, Ramtha, Jordan, 2009.
[25]  International Electrotechnical Commission (IEC), Tech. Rep. 61675-1, Geneva, Switzerland, 1998.
[26]  National Electrical Manufacturers Association (NEMA), Standards Publication NU2, Washington, DC, USA, 2001.
[27]  L. Boubchir and J. Fadili, “Multivariate statistical modelling of images with the curvelet transform,” in Proceedings of the 8th International Conference on Signal Processing, Pattern Recognition, and Applications, pp. 747–750, 2005.
[28]  I. Sumana, Image retrival using discrete curvelet transform, M.S. thesis, Monash University, Australia, 2008.
[29]  L. Demanet, “The curvelet Organization,”


comments powered by Disqus