Abstract

Our Goal is to increase the accuracy of brain tumor detection and classification and thereby replace conventional invasive and time consuming techniques. Here a new technique is proposed to classify the brain MRI images and to detect the brain tumor using probabilistic neural network. The proposed methodology comprises of three phases. 1) Discrete wavelet transform 2) Modified region growing algorithm and 3) Probabilistic neural network. Initially, the input is subjected to discrete wavelet transform. It is used to extract the wavelet coefficients from the MRI images. Then the texture features are extracted using modified region growing algorithm from the input MRI brain images, which are obtained from the database. The texture features taken in to consideration are correlation and contrast. Soon after, the extracted features are fed as the input to the Hybrid ANN-PNN to classify the brain MRI images. Based on the features extracted the tumor will be detected and will be classified as Benign and malignant tumor. The proposed methodology will be implemented in MATLAB 7.12 with different datasets. The performance will be analyzed with existing detection methods and we will prove our efficiency in terms of accuracy.

Keywords:

Benign tumor, discrete wavelet transform, malignant tumor, modified region growing, MRI, probabilistic neural network,

References

1. Ahmed, M.M. and D.B. Mohammad, 2008. Segmentation of brain MR Images for tumor extraction by combining k-means clustering and Perona-Malik anisotropic diffusion mode. Int. J. Image Process., 2(1): 1-34.
   
2. Arizmendi, C., A. Vellido and E. Romero, 2012. Classification of human brain tumors from MRS data using discrete wavelet transform and Bayesian neural networks. Expert Syst. Appl., 39(5): 5223-5232.
   CrossRef    
3. Aslam, H.A., T. Ramashri and M.I.A. Ahsan, 2013. A new approach to image segmentation for brain tumor detection using pillar K-means algorithm. Int. J. Adv. Res. Comput. Commun. Eng., 2(3): 1429-1436.
   
4. Dhanalakshmi, P. and T. Kanimozhi, 2013. Automatic segmentation of brain tumor using K-means clustering and its area calculation. Int. J. Adv. Electr. Electron. Eng. (IJAEEE), 2(2): 2278-8948.
   
5. El-Dahshan, E.S.A., T. Hosny and A.B.M. Salem, 2010. Hybrid intelligent techniques for MRI brain images classification. Digit. Signal Process., 20(2): 433-441.
   CrossRef    
6. Gondal, A.H. and M.N.A. Khan, 2013. A review of fully automated techniques for brain tumor detection from MR Images. Int. J. Modern Educ. Comput. Sci., 5(2): 55-61.
   CrossRef    
7. Gonzalez-Navarro, F.F., L.A. Belanche-Munoz, E. Romero, A. Vellido, M. Julia-Sape and C. Arus, 2010. Feature and model selection with discriminatory visualization for diagnostic classification of brain tumors. Neurocomputing, 73(4-6): 622-632.
   CrossRef    
8. Hari Prasath, S.P., G. Kharmega Sundararaj and A. Jayachandran, 2012. Brain tumor segmentation of contras material applied MRI using enhanced fuzzy C-means clustering. Int. J. Eng. Innov. Technol., 1(2): 161-166.
   
9. John, P., 2012. Brain tumor classification using wavelet and texture based neural network. Int. J. Sci. Eng. Res., 3(10): 1-7.
   
10. Kabade, R.S. and M.S. Gaikwad, 2013. Segmentation of brain tumor and its area calculation in brain MR images using K-mean clustering and fuzzy C mean algorithm. Int. J. Comput. Sci. Eng. Technol., 4(5): 524-531.
    
11. Kang, H., A. Pinti, A. Taleb-Ahmed and X. Zeng, 2011. An intelligent generalized system for tissue classification on MR images by integrating qualitative medical knowledge. Biomed. Signal Proces., 6(1): 21-26.
    CrossRef    
12. Karpagam, S. and S. Gowri, 2012. Brain tumor growth and volume detection by ellipsoid-diameter technique using MRI data. Int. J. Comput. Sci., 9(1): 121-126.
    
13. Logeswari, T. and M. Karnan, 2010. An improved implementation of brain tumor detection using segmentation based on hierarchical self organizing map. Int. J. Comput. Theor. Eng., 2(4): 1793-8201.
    CrossRef    
14. Mohd Saad, N., S.A.R. Abu-Bakar, S. Muda, M. Mokji and A.R. Abdullah, 2012. Automated region growing for segmentation of brain lesion in diffusion-weighted MRI. Proceeding of the International Multi Conference of engineers and Computer Scientists, 1: 674-677.
    
15. Mustaqeem, A. and A. Javed, 2012. An efficient brain tumor detection algorithm using watershed & thresholding based segmentation. Int. J. Image Graph. Signal Process., 4(10): 34-39.
    CrossRef    
16. Nalbalwar, R., U. Majhi, R. Patil and S. Gonge, 2014. Detection of brain tumor by using ANN. Int. J. Res. Advent Technol., 2(4): 279-282.
    
17. Rajalakshmi, N. and V. LakshmiPrabha, 2013. Automated classification of brain MRI using color converted K-Means clustering segmentation and application of different kernel functions with multi-class SVM. Proceeding of International in Disciplinary Conference, 3: 799-806.
    
18. Rakesh, M. and T. Ravi, 2012. Image segmentation and detection of tumor objects in MR brain images using FUZZY C-MEANS (FCM) algorithm. Int. J. Eng. Res. Appl., 2(3): 2088-2094.
    
19. Rana, R., H.S. Bhadauria and A. Singh, 2013. Comparative study of segmentation techniques for extracting brain tumor from MRI image. Proceeding of the 2nd International Conference on Advances in Electronics, Electrical and Computer Engineering 3(6): 41-45.
    
20. Roslan, R., N. Jamil and R. Mahmud, 2011. Skull stripping magnetic resonance images brain images: Region growing versus mathematical morphology. Int. J. Comput. Inform. Syst. Ind. Manage. Appl., 3: 150-158.
    
21. Saha, B.N., N. Ray, R. Greinera, A. Murthab and H. Zhang, 2012. Quick detection of brain tumors and edemas: A bounding box method using symmetry. Comput. Med. Imag. Grap., 36(2): 95-107.
    CrossRef    PMid:21719256    
22. Sahu, M. and K. Parvathi, 2013. Segmentation of colour data base image by implementing k-means clustering. Int. J. Innov. Eng. Technol., 2(4): 229-234.
    
23. Tiwari, S., A. Bansal and R. Sagar, 2012. Identification of brain tumors in 2D MRI using automatic seeded region growing method. J. Educ., 2(1): 41-43.
    
24. Weizman, L., L. Ben Sira, L. Joskowicz, S. Constantini, R. Precel, B. Shofty and D. Ben Bashat, 2012. Automatic segmentation, internal classification, and follow-up of optic pathway gliomas in MRI. Med. Image Anal., 16(1): 177-188.
    CrossRef    PMid:21852179    
25. Ze-Xuan, J., S. Quan-Sen and X. De-Shen, 2011. A framework with modified fast FCM for brain MR images segmentation. Pattern Recogn., 44(5): 999-1013.
    CrossRef    
26. Zhan, Y., Z. Dong, L. Wu and S. Wang, 2011. A hybrid method for MRI brain image classification. Expert Syst. Appl., 38(8): 10049-10053.
    CrossRef    

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.