IJMTES – DETECTION OF TUMOUR IN MRI USING WAVELETS AND NEUROFUZZY ALGORITHM

Journal Title : International Journal of Modern Trends in Engineering and Science

Paper Title : DETECTION OF TUMOUR IN MRI USING WAVELETS AND NEUROFUZZY ALGORITHM

Author’s Name : S.Nandhini | A.Sagaya Selvaraj  unnamed

Volume 03 Issue 10 2016

ISSN no:  2348-3121

Page no: 103-107

Abstract – The main objective of this project is to develop an efficient segmentation algorithm. The first step is to preprocess the image. In the preprocessing, we normalize the image and filtration is applied. Diffusion filter is used since the edges are well preserved and inner parts of the images are smoothened. Skull stripping is done to increase the accuracy of brain tumor detection. An algorithm combining thresholding and morphological operations are used for skull stripping. Features are extracted using Stationary Wavelet Transform (SWT).The extracted features are trained using Adaptive Neuron Fuzzy Interface System (ANFIS) which integrates both the neural network and fuzzy logic principles. The trained samples are mapped into the Self Organizing Map (SOM). Finally the segmentation is performed using trained SOM and the tumor is detected.   

Keywords— Stationary Wavelet Transform (SWT), Adaptive Neuro Fuzzy Interface System (ANFIS), Self Organizing Map (SOM)

Reference

  1. A.Demirhan, Ankara, M.Toru, I.Guler, “Segmentation of Tumour and Edema Along With Healthy Tissues of Brain Using Wavelets and Neural Networks”, IEEE Journal of Biomedical and Health Informatics, vol. 19, no. 4,pp.1451- 1458, July 2015.
  2. K.Jafari-Khouzani, “MRI Up sampling Using Feature-Based Nonlocal Means Approach”, IEEE Transactions on Biomedical Engineering, Vol . 33, no. 11, pp. 1969 – 1985, June 2014.
  3. A.Islam, S.M.S Reza and K.M.Iftekharuddin, “Multifractal Texture Estimation for Detection and Segmentation of Brain Tumors”, IEEE Transactions on Biomedical Engineering,Vol. 60, no. 11,pp .3204-3215, Nov 2013.
  4. Hamamci, Istanbul, N.Kucuk, K.Karaman, “Tumor-Cut:Segmentation of Brain Tumors on Contrast Enhanced MR Images for Radiosurgery Apllications”,IEEE Transactions on Medical Imaging, Vol. 31,no. 3, pp. 790-804, March 2012.
  5. T. Wang, A .B. Edmonton, I. Cheng, “Fluid Vector Flow and Applications in Brain Tumor Segmentation” , IEEE Transactions on Biomedical Engineering, Vol. 56, no. 3, pp.781-789, March 2009.
  6. F. Bogalhas, Orsay, L.Menard, S.Bonzom, “Performance of an Intraoperative Beta Probe Dedicated to Glioma Radioguided Surgery”,IEEE Transactions on Nuclear Science, vol. 55, no 3, pp.8333-841, June 2008.
  7. J.J Corso, E.Sharon, S. Dube,S. El-Saden, “Efficient Multilevel Brain Tumour Segmentation With Integrated Bayesian Model Classificstion”, IEEE Transactions on Medical Imaging , Vol. 27 , no. 5, pp .629-640, May 2008.
  8. S.Bonzom, L. Menard, S. Pitre, M. A. Duval, “An Intraoperative Beta Probe Dedicated to Glioma Surgery: Design and Feasibility”, IEEE Transactions on Nuclear Science, Vol 54, no.1, pp .1035-1041, Feb 2007.
  9. D.Feng, Y.Lee, R.Tayor, “Matching Visual Saliency to Cofidence in Plots of Uncertain Data”, IEEE Transactions on Visualisation and Computer Graphics,Vol. 16 , no. 6 , pp.980-989, Dec. 2010.
  10. J.G. Kim, Mengna Xia, Hanli Liu, “Extinction coefficients of hemoglobin for near – infrared spectroscopy of tissue”,IEEE Engineering in Medicine and Biology Magazine, Vol 24, no .2,pp. 891-897,March 2005.