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Evaluating the Efficiency of different Feature Sets on Brain Tumor Classification in MR Images

by Engy N. Eltayeb, Nancy M. Salem, Walid Al-Atabany
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 38
Year of Publication: 2018
Authors: Engy N. Eltayeb, Nancy M. Salem, Walid Al-Atabany
10.5120/ijca2018917008

Engy N. Eltayeb, Nancy M. Salem, Walid Al-Atabany . Evaluating the Efficiency of different Feature Sets on Brain Tumor Classification in MR Images. International Journal of Computer Applications. 180, 38 ( May 2018), 1-7. DOI=10.5120/ijca2018917008

@article{ 10.5120/ijca2018917008,
author = { Engy N. Eltayeb, Nancy M. Salem, Walid Al-Atabany },
title = { Evaluating the Efficiency of different Feature Sets on Brain Tumor Classification in MR Images },
journal = { International Journal of Computer Applications },
issue_date = { May 2018 },
volume = { 180 },
number = { 38 },
month = { May },
year = { 2018 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number38/29376-2018917008/ },
doi = { 10.5120/ijca2018917008 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:04:31.210591+05:30
%A Engy N. Eltayeb
%A Nancy M. Salem
%A Walid Al-Atabany
%T Evaluating the Efficiency of different Feature Sets on Brain Tumor Classification in MR Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 38
%P 1-7
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, a study for evaluating the efficacy of different feature sets that used brain tumor classification is presented. Different features sets are extracted as shape, 1st order texture features (FOS), 2nd order (GLCM, GLRLM), boundary features, and wavelet-based features. The brain tumors are extracted using the k-means clustering algorithm. Then different classifiers such as Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) were used in the classification process. A set of 65 real and simulated (Flair modality) MRI images from multimodal brain tumor image segmentation benchmark (BRATS) organized by MICCAI 2012 challenge is used for performance evaluation. The overall segmentation results for the 65 volumes are 90.15±0.12. For the Feature sets efficacy step, the highest accuracy of 94.74% is achieved by the SVM when using the wavelet–based features. The lowest accuracy achieved by the three classifiers obtained when using the second order texture features..

References
  1. A. Drevelegas and N. Papanikolaou, "Imaging modalities in brain tumors," in Imaging of Brain Tumors with Histological Correlations: Springer, pp. 13-33, (2002).
  2. B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, Y. Burren, N. Porz, J. Slotboom, and R. Wiest, "The multimodal brain tumor image segmentation benchmark (BRATS)," IEEE Transactions on Medical Imaging, vol. 34, pp. 1993-2024,(2015).
  3. K. Usman and K. Rajpoot, "Brain tumor classification from multi-modality MRI using wavelets and machine learning," Pattern Analysis and Applications, pp. 1-11, (2014).
  4. J. Liu, M. Li, J. Wang, F. Wu, T. Liu, and Y. Pan, "A survey of MRI-based brain tumor segmentation methods," Tsinghua Science and Technology, vol. 19, pp. 578-595, (2014).
  5. H. Tang, E. Wu, Q. Ma, D. Gallagher, G. Perera, and T. Zhuang, "MRI brain image segmentation by multi-resolution edge detection and region selection," Computerized Medical Imaging and Graphics, vol. 24, pp. 349-357, (2000).
  6. M. Jafari and S. Kasaei, "Automatic brain tissue detection in MRI images using seeded region growing segmentation and neural network classification," Australian Journal of Basic and Applied Sciences, vol. 5, pp. 1066-1079, (2011).
  7. E.-S. A. El-Dahshan, H. M. Mohsen, K. Revett, and A.-B. M. Salem, "Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm," Expert systems with Applications, vol. 41, pp. 5526-5545, (2014).
  8. Q. Ain, M. A. Jaffar, and T.-S. Choi, "Fuzzy anisotropic diffusion based segmentation and texture based ensemble classification of brain tumor," Applied Soft Computing, vol. 21, pp. 330-340.
  9. S. Bandyopadhyay, "MRI brain image segmentation by fuzzy symmetry based genetic clustering technique," Evol. Comput, pp. 4417-4424, (2007)
  10. J. Selvakumar, A. Lakshmi, and T. Arivoli, "Brain tumor segmentation and its area calculation in brain MR images using K-mean clustering and Fuzzy C-mean algorithm," in Advances in Engineering, Science and Management (ICAESM), 2012 International Conference on, pp. 186-190, (2012).
  11. M.-N. Wu, C.-C. Lin, and C.-C. Chang, "Brain tumor detection using color-based k-means clustering segmentation," in Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2007, pp. 245-250, (2007).
  12. M. M. Ahmed and D. B. Mohamad, "Segmentation of brain MR images for tumor extraction by combining kmeans clustering and perona-malik anisotropic diffusion model," International Journal of Image Processing, vol. 2, pp. 27-34, (2008).
  13. F. Zarandi, M.H., Zarinbal, M., Izadi, M.: Systematic image processing for diagnosing brain tumors: a type-II fuzzy expert system approach. Appl. Soft Comput. 11(1), 285–294, (2011).
  14. C. Arizmendi, A. Vellido, E. Romero, “Binary classification of brain tumors using discrete wavelet transform and energy criteria. In: Proceedings of IEEE Second Latin American Symposium on Circuits and Systems, pp. 1–4, (2011).
  15. Georgiadis, P., Cavouras, D., Kalatzis, I., Daskalakis, A., Kagadis, G.C., Sifaki, K., Malamas, M., Nikiforidis, G., Solomou, E.:Improving brain tumor characterization on MRI by probabilistic neural network and non-linear transformation of textural features. Comput. Methods Programs Biomed. 89(1), 24–32, (2008).
  16. Reddy,Megha P.Arakeriand G.Ram Mohana, Computer-aided diagnosis system for tissue characterization. Signal, Image and Video processing vol 9, pp.409-425, (2015).
  17. Verme N., Cowperthwaite M.C., Burnett M.G., Markey M.K. (2014) Image Analysis Techniques for the Quantification of Brain Tumors on MR Images. In: Suzuki K. (eds) Computational Intelligence in Biomedical Imaging. Springer, New York, NY
  18. Kassimi, M.A., El beqqali, O., "3D model retrieval based on semantic and shape indexes". Int. J. Comput. Sci. Issues 8(1), 172–181, (2011).
  19. N. Nabizadeh and M. Kubat, "Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features," Computers & Electrical Engineering, vol. 45, pp. 286-301, (2015).
  20. M. B. A. Haghighat, A. Aghagolzadeh, and H. Seyedarabi, "A non-reference image fusion metric based on mutual information of image features," Computers & Electrical Engineering, vol. 37, pp. 744-756, (2011).
  21. C. Connolly and T. Fleiss, "A study of efficiency and accuracy in the transformation from RGB to CIELAB color space," IEEE Transactions on Image Processing, vol. 6, pp. 1046-1048, (1997).
  22. Akilandeswari, U., Nithya, R., Santhi, B.:Reviewon feature extraction methods in pattern classification. Euro. J. Sci. Res. 71(2), 265–272, (2012).
  23. Haralick, R.M., Shanmugam, K., Dinstein, I.: Texture features forimage classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621, (1973).
  24. Conners RW, Harlow CA. A theoretical comparison of texture algorithm. IEEE Trans Pattern Anal Mach Intell 1980;2:204–22, (1980).
  25. Iftekharuddin, K.M., Jia, W., Marsh, R.: Fractal analysis of tumor in brain MR images. Mach. Vis. Appl. 13, 352–362, (2003)
  26. Haykin, S.O.: Neural networks and Learning Machines, 3rd edn,Prentice Hall (2008)
  27. Song, Y., Huang, J., Zhou, D., Zha, H., Giles, C.L.: IKNN: informative k-nearest neighbor pattern classification, LNCS, Vol. 4702, Springer, pp. 248–264, (2007).
  28. http://www2.imm.dtu.dk/projects/BRATS2012/
Index Terms

Computer Science
Information Sciences

Keywords

Brain tumor segmentation Feature extraction Wavelet Transform.

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