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Reseach Article

Segmentation of Parotid Lesions in CT Images using Wavelet-based Features

Published on May 2013 by Tung-ying Wu, Sheng-fuu Lin
Recent Trends in Pattern Recognition and Image Analysis
Foundation of Computer Science USA
RTPRIA - Number 1
May 2013
Authors: Tung-ying Wu, Sheng-fuu Lin
8eae7eb9-aa79-42e4-91e6-f3222498bcba

Tung-ying Wu, Sheng-fuu Lin . Segmentation of Parotid Lesions in CT Images using Wavelet-based Features. Recent Trends in Pattern Recognition and Image Analysis. RTPRIA, 1 (May 2013), 18-26.

@article{
author = { Tung-ying Wu, Sheng-fuu Lin },
title = { Segmentation of Parotid Lesions in CT Images using Wavelet-based Features },
journal = { Recent Trends in Pattern Recognition and Image Analysis },
issue_date = { May 2013 },
volume = { RTPRIA },
number = { 1 },
month = { May },
year = { 2013 },
issn = 0975-8887,
pages = { 18-26 },
numpages = 9,
url = { /specialissues/rtpria/number1/11798-1004/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Recent Trends in Pattern Recognition and Image Analysis
%A Tung-ying Wu
%A Sheng-fuu Lin
%T Segmentation of Parotid Lesions in CT Images using Wavelet-based Features
%J Recent Trends in Pattern Recognition and Image Analysis
%@ 0975-8887
%V RTPRIA
%N 1
%P 18-26
%D 2013
%I International Journal of Computer Applications
Abstract

Automatic segmentation of parotid glands for computer-aided diagnosis in clinical practice is still a challenging task, especially when there are lesions needing to be outlined. In the applications of image-based diagnosis and computer-aided lesion detection, image segmentation is an important procedure. Features extracted from image analysis in companion with image segmentation algorithms are used to provide region-based information for clinical evaluation procedures. In this paper, we describe a method for segmenting the parotid regions with skeptical lesions in the head and neck CT images. At first, à trous, a modified discrete wavelet transform algorithm, is introduced to decompose an image into sub-bands, and the feature descriptors effective for soft tissues characteristics are computed using the derived coefficients in the sub-bands. Then, clustering algorithms are proposed to connect the pixels corresponding to similar features into several regions of the soft tissues, and so do the tissues of the lesions. In this paper, a comparative study of feature-based segmentation with three methods is carried on, and the extracted regions are compared with the segmentation from the experts for evaluating the performance.

References
  1. Duay, V. , Houhou, N. , Bach Cuadra, M. , Schick, U. , Becker, M. , Allal, A. S. and Thiran, J. P. 2009. Segmentation of head and neck Lymph node regions for radiotherapy planning using active contour-based atlas registration. IEEE Journal of selected topics in signal processing, Vol. 3, No. 1 (Feb. 2009), 135-147.
  2. Teng, C. C. , Shapiro, L. G. and Kalet, I. 2006. Automatic Segmentation of Neck CT Images. Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems.
  3. Xu, X. Q. , Lee, D. J. , Antani, S. and Long, L. R. 2004. Proceedings of the 17th IEEE Symposium on Computer-based Medical Systems.
  4. Feulner1, J. , Zhou. S. K. . , Hammon, M. , Hornegger, J. and Comaniciu, D. 2011. Segmentation-based features for lymph Node Detection from 3-D Chest CT. Proceeding MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
  5. Barbu, A. , Suehling, M. , Xun, X. , Liu, D. , Zhou, S. K. and Comaniciu, D. 2012. Automatic Detection and Segmentation of Lymph Nodes From CT Data. IEEE Transactions of Medical Imaging, Vol. 31, No. 2(Feb. 2012), 240-250.
  6. Tara, A. W. , Henk, P. B. , Henriëtte E. W. and Johannes, A. L. 2009. Delineation guidelines for organs at risk involved in radiation –induced salivary dysfunction and xerostomia, Vol. 93, No. 3(Dec. 2009), 545-552.
  7. Duay, V. , Houhou, N. and Thiran, J. P. 2005. Atlas-based segmentation of medical images locally constrained by level sets. ICIP 2005. IEEE International Conference on Image Processing.
  8. Liliane, R. and Gr´egoire, M. 2010. Multi-atlas Based Segmentation: Application to the Head and Neck Region for Radiotherapy Planning. MICCAI Workshop Medical Image Analysis for the Clinic - A Grand Challenge.
  9. Gorthi, S. , Bach C. M. , Schick, U. , Tercier, P. A. , Allal, A. S. and Thiran, J. P. 2010. Multi-Atlas based Segmentation of Head and Neck CT Images using Active Contour Framework. MICCAI workshop on 3D Segmentation Challenge for Clinical Applications.
  10. Yang, J. , Zhang, Y. , Zhang, L. and Dong, L. 2010. Automatic Segmentation of Parotids from CT Scans Using Multiple Atlases. Medical Image Analysis for the Clinic - A Grand Challenge.
  11. Commowick, O. and Malandain, G. 2007. Efficient selection of the most similar image in a database for critical structures segmentation. Medical Image Computing and Computer Assisted Intervention.
  12. Lauric, R. and Frisken, S. 2007. Soft segmentation of CT brain data. Technical Report TR-2007-3 Tufts University, MA, Tech. Rep.
  13. Asmita, A. M. , Singhai, J. and Shrivastava, S. C. 2011. Automatic Threshold based Liver Lesion Segmentation in Abdominal 2D-CT Images. International Journal of Image Processing, Vol. 5, No. 2(Sep. 2011), 166-176.
  14. Semler, L. , Dettori, L. and Furst, J. 2005. Wavelet-based texture classification of tissues in computed tomography. Proceedings 18th IEEE Symposium on Computer-Based Medical Systems.
  15. Latif, G. , Kazmi, S. B. , Jaffar, M. A. and Anwar, M. M. 2010. Classification and Segmentation of Brain Tumor using Texture Analysis. International Conference on Recent advances in artificial Intelligence,Knowledge Engineering and Databases.
  16. Kruggel, F. , Paul, J. S. and Gertz, H. J. 2007. Texture-based segmentation of diffuse lesions of the brain's white matter. NeuroImage, Vol. 39(Oct. 2007), 987-996.
  17. Livens, S. , Scheunders, P. , Wouwer, G. and Dyck, D. 1997. Wavelets for texture analysis, an overview. International Conference on Image Processing and Its Applications.
  18. Arivazhagan, S. and Ganesan, L. 2003. Texture classification using wavelet transform. Pattern Recognition Letters, Vol. 24, No. 9(June 2003), 1513-1521.
  19. Fan, G. and Xia, X. G. 2003. Wavelet-based texture analysis and synthesis using hidden Markov models. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, Vol. 50, No. 1(Jan. 2003), 106-120.
  20. Bayram, K. and Watsuji, N. 2003. Using Wavelets For Texture Classification. IJCI Proceedings of International Conference on Signal Processing, Vol. 1, No. 2(Sep. 2003), 920-924.
  21. Wouwer, G. , Scheunders P. and Dyck, D. 1999. Statistical texture characterization from discrete wavelet representations. IEEE Transactions on Image Processing, Vol. 8, No. 4(Apr. 1999), 592-598.
  22. Laine, A. and Fan, J. 1993. Texture classification by wavelet packet signatures. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, No. 11(Nov. 1993), 1186-1191.
  23. Bashar, M. K. , Matsumoto, T. and Ohnishi, N. 2003. Wavelet transform-based locally orderless image for texture segmentation, Pattern Recognition Letters, Vol. 24, No. 15, (Nov. 2003), 2633–2650.
  24. Holschneider, M. , Kronland-Martinet, R. , Morlet, J. and Tchamitchian, P. 1989. A real-time algorithm for signal analysis with the help of the wavelet transform, In Wavelets, Time-Frequency Methods and Phase Space, 289–297.
  25. Cheng, Y. 1995. Mean Shift, Mode Seeking, and Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17, No. 8(Aug. 1995), 790-799.
  26. Comaniciu, D. and Meer, P. 2002. Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 5(May, 2002), 603-619.
  27. Nock, R. and Nielsen, F. 2006. On weighting clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 8(Aug. 2006), 1223-1235.
  28. Ahmed, M. , Yamany, S. M. , Mohamed, N. , Farag, A. A. and Moriarty, T. N. 2002. A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data, Vol. 21, No. 3(Mar. 2002), 193-199.
  29. Hartigan, J. A. 1975. Clustering Algorithms. John Wiley & Sons Inc. ISBN-13: 978-0471356455.
  30. Renato, C. A. and Boris, M. 2012. Metric, feature weighting and anomalous cluster initializing in K-Means clustering, Vol. 45, No. 3(Mar. 2012), 1061-1075.
Index Terms

Computer Science
Information Sciences

Keywords

Parotid Wavelet Computer Tomography Image Segmentation