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Arabian Journal for Science and Engineering

, Volume 39, Issue 2, pp 767–776 | Cite as

Segmentation and Classification of Brain CT Images Using Combined Wavelet Statistical Texture Features

  • A. PadmaEmail author
  • R. Sukanesh
Research Article - Computer Engineering and Computer Science

Abstract

A computer software system is designed for the segmentation and classification of benign and malignant tumor slices in brain computed tomography images. In this paper, we present a method to find and select both the dominant run length and co-occurrence texture features of the wavelet approximation tumor region of each slice to be segmented by support vector machine. Two dimensional discrete wavelet decomposition is performed on the tumor image to remove the noise. The images considered for this study belong to 192 benign and malignant tumor slices. A total of 17 features are extracted and six features are selected using Student’s t test. The reduced optimal features are used to model and train the probabilistic neural network classifier and the classification accuracy is evaluated using k fold cross validation method. The segmentation results are also compared with the experienced radiologist ground truth. Quantitative analysis between ground truth and segmented tumor is presented in terms of segmentation accuracy and the overlap similarity measure of Jaccard index. The proposed system provides some newly found texture features that have important contribution in classifying benign and malignant tumor slices efficiently and accurately. The experimental results show that the proposed system is able to achieve high segmentation and classification accuracy effectiveness as measured by sensitivity and specificity.

Keywords

Feature selection Classification Segmentation Dominant run length texture features 

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References

  1. 1.
    Wei, K.; He, B.; Zhang, T.; Shen, X.: A novel method for segmentation of CT head images. In: International Conference on Bio Informatics and Biomedical Engineering, pp. 717–720 (2007)Google Scholar
  2. 2.
    Lauric, A.; Frisken, S.: Soft segmentation of CT brain data. Technical Report TR-2007-3, Tuffs University, MAGoogle Scholar
  3. 3.
    Sharma N., Ray A.K., Sharma S., Shukla K.K., Pradhan S., Aggarwal L.M.: Segmentation and classification of medical images using texture primitive features: application of BAM-type artificial neural network. J. Med. Phys. 33, 119–126 (2008)CrossRefGoogle Scholar
  4. 4.
    Lee T.H., Faizal M., Fauzi A., Komiya R.: Segmentation of CT brain Images using unsupervised clustering’s. J. Vis. 12, 31–138 (2009)Google Scholar
  5. 5.
    Rajendran, P.; Madheswaran, M.: An improved image mining technique for brain tumor classification using efficient classifier. Int. J. Comput. Sci. Netw. Secur. 6, 107–116 (2009)Google Scholar
  6. 6.
    Ganesan R., Radhakrishnan R.: Segmentation of computed tomography brain images using genetic algorithm. Int. J. Soft Comput. 4, 157–161 (2009)Google Scholar
  7. 7.
    Choplet, S.; Patnaik, L.M.; Jaganathan, N.R.: Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed. Signal Process. Control. 1, 86–92 (2006)Google Scholar
  8. 8.
    Kharrat, A.; Gasmi, K.; Ben Messaoud, M.; Benamrane, N.; Abid, M.: An hybrid approach for automatic classification of brain MRI using genetic algorithm and support vector machine. Leonardo J. Sci. 17, 71–82 (2010)Google Scholar
  9. 9.
    Padma A., Sukanesh R.: Texture feature based analysis of segmenting soft tissues from brain CT images using BAM-type artificial neural network. J. Inf. Eng. Appl. 1, 34–42 (2011)Google Scholar
  10. 10.
    Zhang, Y.; Dong, Z.; Wu, L.; Wang, S.; Zhou, Z.: Feature extraction of brain MRI by stationary wavelet transform and its applications. J. Biol. Syst. 18, 115–132 (2010)Google Scholar
  11. 11.
    Zhang, Y.; Dong, Z.; Wu, L.; Wang, S.: A hybrid method for MRI brain image classification. J. Exp. Syst. Appl. 38, 10049–10053 (2011)Google Scholar
  12. 12.
    El-Naqa I., Yang Y., Wernick M.N., Galatsanos N.P., Nishikawa R.M.: A support vector machine approach for detection of micro calcifications. IEEE Trans. Med. Imaging. 21, 1552–1563 (2002)CrossRefGoogle Scholar
  13. 13.
    Van, G.; Wouver, P.; Scheunders.; Van Dyck, D.: Statistical texture characterization from discrete wavelet representation. IEEE Trans. Image Process. 8, 592–598 (1999)Google Scholar
  14. 14.
    Tang X.: Texture information in run length matrices. IEEE Trans. Image Process. 7, 234–243 (1998)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Tang, X.: Dominant run length method for image classification. Woods Hole Oceanographic Institution. Woods Hole Report, pp. 97–107 (1998)Google Scholar
  16. 16.
    Khuzi, M.; Besar, R.; Wan Zaki, W.M.D.; Ahmad, N.N.: Identification of masses in digital mammogram using gray level co-occurrence matrices. Biomed. Imaging Interv. J. 5, 109–119 (2009)Google Scholar
  17. 17.
    Haralick R.M., Shanmugam K., Dinstein I.: Texture features for Image classification. IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973)CrossRefGoogle Scholar
  18. 18.
    Levner, I.; Bulitko, V.; Lin, G.: Feature extraction for classification of proteomic mass spectra: a comparative study. Springer-Verlag Berlin Heidelberg. Stud Fuzz 207, 607–624 (2006)Google Scholar
  19. 19.
    Soper, D.S.: P-Value Calculator for a Student t test (Online Software). http://www.danielsoper.com/statcalc3 (2011).
  20. 20.
    Mao K.Z., Tan K.C., Ser W.: Probabilistic neural-network structure determination for pattern classification. IEEE Trans. Neural Netw. 11, 1009–1016 (2000)CrossRefGoogle Scholar
  21. 21.
    Yin-Yin Liao, R.; Po-Hsiang, T.; Chih-Kuang, Y.: Classification of benign and malignant breast tumors by ultrasound B-scan and Nakagami-based images. J. Med. Biol. Eng. 30, 307–312 (2009)Google Scholar
  22. 22.
    Kim J.K., Park H.W.: Statistical textural features for detection of micro calcifications in digitized mammograms. IEEE Trans. Med. Imaging. 18, 231–238 (1999)CrossRefzbMATHGoogle Scholar
  23. 23.
    Mellors, R.C.: Biological characteristics of benign and malignant neoplasms. NeoplasiaGoogle Scholar
  24. 24.
    Ben Ayed I., Mitiche A., Belhadj Z.: Multi Level set partitioning on synthetic aperture radar image. IEEE Trans. Patt. Anal. Machine Intell. 27, 793–800 (2005)CrossRefGoogle Scholar

Copyright information

© King Fahd University of Petroleum and Minerals 2013

Authors and Affiliations

  1. 1.Trichy Anna UniversityTrichyIndia
  2. 2.Department of Electronics and Communication EngineeringThiagarajar College of EngineeringMaduraiIndia

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