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Image Classification and Clustering using Wavelet Based Radial Basis Neural Network

Authors(1):

P. Sathish
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Accurate image segmentation and classification, is essential for medical diagnosis of scans. Of late, magnetic resonance (MR) images have become the commonest tool of clinical investigation. In this study we address to clarify brain tumor images into normal, non cancerous (benign) brain tumor and cancerous (malignant) brain tumor by collecting the complete history of the patients in terms of their food habit, life style and the severity of the age. The proposed method follows three steps, (1) wavelet decomposition, (2) texture feature extraction and (3) classification. Discrete Wavelet Transform is first employed using Daubechies wavelet (db4), for decomposing the MR image into different levels of approximate and detailed coefficients and then the gray level co-occurrence matrix is formed, from which the texture statistics such as energy, contrast, correlation, homogeneity and entropy are obtained. The results of co-occurrence matrices are then fed into a radial basis neural network for further classification and clustering for tumor detection. The proposed method will be applied on real MR images, and on all types cancer images and the accuracy of classification using radial basis neural network will be rigorously evaluated and the physician can diagnose and design the better therapies.

P. Sathish

Wavelet Decomposition, Co-Occurrence Matrix, Gray Level Spatial Dependence Matrix (GLSDM)

  1. Ahmed kharrat, KarimGasmi, et.al, "A Hybrid Approach for Automatic Classification of Brain MRI Using Genetic Algorithm and Support Vector Machine", Leonardo Journal of Sciences, July-Dec2010, pp.71-82.
  2. Ahmed Kharrat, Mohamed Ben Messaoud, et.al,"Detection of Brain Tumor in Medical Images" International Conference on Signals, Circuits and Systems", IEEE, 2009.
  3. Carlos Arizmendi, Alfredo Vellido, Enrique Romero, "Binary Classification of Binary Tumors using a Discrete Wavelet Transform and Energy Criteria", IEEE, 2011.
  4. Frank Z. Brill., Donald E. Brown., and Worthy N. Martin, "Fast Genetic Selection of features for Neural Network Classifiers", IEEE Trans.,on Neural Networks, Vol 3, No 2, 1992,pp 324-328.
  5. Gunduz, C., B. Yener, and S. H. Gultekin "The cell graphs of cancer" Bioinformatics. 20: i145-i151, 2004.
  6. Jude Hemanth, C. KeziSelvaVijila,et.al, "A survey on Artificial Intelligence based Brain Pathology Identification Techniques in Magnetic Resonance Images", International Journal of Reviews in Computing, 2009.
  7. Samir Kumar Bandyopadhyay, "Detection of Brain Tumor-A Proposed Method", Journal of Global Research in Computer Science, Volume 2, No. 1, January 2011.
  8. Ganesan, N, "Application of Neural Networks in Diagnosing Cancer Disease Using Demographic Data". International Journal of Computer Applications.
  9. Vijay Kumar et al., "Biological Early Brain Cancer Detection Using Artificial Neural Network", International Journal on Computer Science and Engineering Vol. 02, No. 08, 2010, pp. 2721-2725.
  10. Ibrahiem M.M. El Emary, et.al., "On the Application of Various Radial basis neural networks in Solving Different Pattern Classification Problems", World Applied Sciences Journal 4 (6): 772-780, 2008 ISSN 1818-4952, pp 772-780.
  11. Kadam D. B, Gade S. S,et.al, "Neural Network based Brain Tumor Detection using MR Images",International Journal of Computer Science and Communication, Vol. 2, No. 2, July-December 2011, pp. 325-331.
  12. Mehdi Jafari, ShohrehKasaei, "Automatic Brain Tissue Detection in MR Images Using Seeded Region Growing Segmentation and Neural Network Classification", Australian Journal of Basic and Applied Sciences, 5(8): 1066-1079, 2011.
  13. MohdFauzi Bin Othman, NoramalinaBt Abdullah, et.al., "MRI Brain      Classification using Support Vector Machine", IEEE, 2011.
  14. MohdFauzi Othman and MohdAriffananMohdBasri, "Radial basis neural network for Brain Tumor Classification", Second International Conference on Intelligent Systems, Modelling and Simulation. IEEE, ISMS.2011.32, 2011.
  15. MeysamTorabi, Reza Vaziri, et.al.,"A Wavelet-Packet-Based Approach For Breast Cancer Classification", 33rdInternationalConferenceofIEEE, Aug 30 – Sept 3, 2011.
  16. HemaRajini, R.Bhavani,"Classification of MRI Brain Images using k- Nearest Neighbor and Artificial Neural Network", International Conference on Recent Trends in Information Technology. IEEE, 2011.
  17. Qurat-Ul-Ain, GhazanfarLatif, "Classification and Segmentation of Brain Tumor using Texture Analysis", Recent Advances In Artificial Intelligence, Knowledge Engineering And Data Bases, pp 147-155.
  18. SalimLahmiri and MounirBoukadoum,et.al., "Classification of Brain MRI using the LH and HL Wavelet Transform Sub-bands", IEEE, 2011.
  19. N. Deepa, et.al., "Second Order Sequential Minimal Optimization for Brain Tumor Classification" European Journal of Scientific Research ISSN 1450-216X Vol.64 No.3, 2011, pp. 377-386
  20. Tourassi G D, " Journey towards computer aided Diagnosis – Role of Image Texture Analysis", Radiology, Vol 213, 1999, No 2, pp 317 – 320.
  21. Zhang, S. Wang, and L. Wu, "A Novel Method for Magnetic Resonance Brain Image Classification Based On Adaptive Chaotic PSO", Progress In Electromagnetics Research, Vol. 109,2010,pp. 325-343.
  22. S.Suchitha Goswami, Mr.Lalit Kumar P.Bhaiya, "Brain Tumour Detection using Unsupervised Learning based Neural Network", International Conference on Communication Systems and Network Technologies,IEEE,2013.
  23. Junjie Hu, Daniel S.Yeung, "Incremental Learning using Error and Sensitivity Analysis of MCS for Image Classification", International Conference on Wavelet Analysis and Pattern Recognition, Tianjin, IEEE, 14-17 July, 2013.
  24. Sridhar, N.Murali Krishna" Brain Tumor Classification Using Discrete Cosine Transform and Probabilistic Neural Network" International Conference on Signal Processing, Image Processing and Pattern Recognition ICSIPR],IEEE, 2013.
  25. Pauline John "Brain Tumor Classification Using Wavelet and Texture Based Neural Network",International Journal of Scientific & Engineering Research Volume 3, Issue 10, October-2012

Publication Details

Published in : Volume 2 | Issue 5 | September-October - 2016
Date of Publication Print ISSN Online ISSN
2016-10-30 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
222-225 IJSRSET162560   Technoscience Academy

Cite This Article

P. Sathish, "Image Classification and Clustering using Wavelet Based Radial Basis Neural Network", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 5, pp.222-225, September-October-2016.
URL : http://ijsrset.com/IJSRSET162560.php

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