An efficient magnetic resonance brain image classifier using tetrolet transform and kernel support vector machine based on OTSU binarization

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Classification of tumor from cancer causing to non-cancer causing, it plays a main role in diagnosing the disease effectively without any flaws. In this proposed paper, a novel methodology is presented in order  to classify a given Magnetic resonant brain image as normal or abnormal using OTSU binarization segmentation with help of tetrolate transform. By replacing wavelet transform with tetrolate transform the classification is made efficient in case of images with geometric shapes. A good number of features are extracted by using OTSU binarization from edge-based segmentation, the more number of features makes the classification for effective and accurate. The image is finely segmented pixel by pixel for good accuracy and about 12 features like We calculate four different type of accuracy like RBF (Radial Basis Function), linear, polygonal and quadratic based on image features. We performed our proposed methods with four different kernels LIN (Linear), HPOL (Homogeneous Polynomial), and IPOL and GRB (Gaussian Radial Basis function) kernel to achieve the highest classification accuracy. The work is added with advancement by using Graphical user interface (GUI), which makes the user comfortable in accessing the method where most of the users are from clinical background and are not aware of any software and their usag.


  • Keywords


    Image Features; Kernel Vector Machine; Magnetic Resonance Image; Otsu Binarization; Tetrolate Transform.

  • References


      [1] Donald W. McRobbie, Elizabeth A. Moore, Martin J. Graves, Martin R. Prince, MRI from Picture to Proton, Cambridge University Press, 2007, ISBN 1139457195page 1

      [2] Surawicz, T.S., Davis, F., Freels, S. et al. J Neurooncol (1998) 40: 151. https://doi.org/10.1023/A:1006091608586

      [3] Sheila K. Singh, Ian D. Clarke, Mizuhiko Terasaki, Victoria E. Bonn, Cynthia Hawkins, Jeremy Squire and Peter B. Dirks. “Identification of a Cancer Stem Cell in Human Brain Tumors.” DOI: Published September 2003

      [4] Faith G. Davis, Ph.D., Sally Freels, Ph.D., James Grutsch, Ph.D., Suna Barlas, M.S., and StevenBrem, M.D. “Survival rates in patients with primary malignant brain tumors stratified by patient age and tumor histological type: an analysis based on Surveillance, Epidemiology, and End Results (SEER) data,” January 1998 / Vol. 88 / No. 1 / Pages 1-10

      [5] Brain Tumors Lisa M. DeAngelis, M.D. January 11, 2001. N Engl J Med 2001; 344:114-123. DOI: 10.1056/NEJM200101113440207.

      [6] "General Information About Adult Brain Tumors". NCI. 14 April 2014. Archived from the original on 5 July 2014. Retrieved 8 June 2014.

      [7] An adaptive edge-preserving image denoising technique using tetrolet transforms Jain, P. & Tyagi, V. Vis Comput (2015) 31: 657. https://doi.org/10.1007/s00371-014-0993-7

      [8] Jens Krommweh, “Tetrolet transform: A new adaptive Haar wavelet algorithm for sparse image representation,” Journal of Visual Communication and Image Representation, vol. 21, no. 4, pp. 364–374, May 2010. 1

      [9] D.L. Donoho, Wedgelets: nearly minimax estimation of edges, Ann. Statist. 27(3), (1999), 859–897. 2

      [10] H. Gu, “Image Compression Using the Haar Wavelet Transform”, Master’s thesis, East Tennessee State University, 2000. 3

      [11] Wavelets and Subband Coding by Martin Vetterli&JelenaKovačević. 4

      [12] Ram, I.; Elad, M.; Cohen, I., "Generalized Tree-Based Wavelet Transform," Signal Processing, IEEE Transactions on , vol.59, no.9, pp.4199,4209, Sept. 2011. 5

      [13] Shawe-Taylor, J.; Cristianini, N. (2004). Kernel Methods for Pattern Analysis. Cambridge University Press. 6

      [14] Liu, W.; Principe, J.; Haykin, S. (2010). Kernel Adaptive Filtering: A Comprehensive Introduction. Wiley. 7

      [15] Pearson, K. (1901). "On Lines and Planes of Closest Fit to Systems of Points in Space" (PDF). Philosophical Magazine. 2(11): 559–572. doi:10.1080/14786440109462720.

      [16] Kittler, Josef & Illingworth, John (1985). "On threshold selection using clustering criteria". Systems, Man and Cybernetics, IEEE Transactions on. SMC-15 (5): 652–655. doi:10.1109/tsmc.1985.6313443.

      [17]Vala, HJ & Baxi, Astha (2013). "A review on Otsu image segmentation algorithm". International Journal of Advanced Research in Computer Engineering & Technology (IJARCET). 2 (2): 387.

      [18]Jianzhuang, Liu and Wenqing, Li and Yupeng, Tian (1991). "Automatic thresholding of gray-level pictures using two-dimension Otsu method". Circuits and Systems, 1991. Conference Proceedings, China., 1991 International Conference on: 325–327.

      [19]Zhu, Ningbo and Wang, Gang and Yang, Gaobo and Dai, Weiming (2009). "A fast 2d otsu thresholding algorithm based on improved histogram". Pattern Recognition, 2009. CCPR 2009. Chinese Conference on: 1–5.

      [20] Jolliffe I.T. Principal Component Analysis, Series: Springer Series in Statistics, 2nd ed., Springer, NY, 2002, XXIX, 487 p. 28 illus. ISBN 978-0-387-95442-4.

      [21] Abdi. H., & Williams, L.J. (2010). "Principal component analysis" (PDF). Wiley Interdisciplinary Reviews: Computational Statistics. 2 (4): 433–459. doi:10.1002/wics.101.

      [22] Md Zia Ur Rahman, B.Malakonda Reddy, “Efficient SAR Image Segmentation Techniques using Biasfield Estimation”, Journal of Scientific and Industrial Research, vol. 76, pp. 335-338, 2017.

      [23]M.L.M. Lakshmi, K.Rajkamal, S.V.A.V.Prasad, Md.Zia Ur Rahman, “Amplitude Only Linear Array Synthesis With Desired Nulls Using Evolutionary Computing Technique”, The Applied of Computational Electromagnetics Society Journal, vol.31, no.11, pp. 1357-1361, November, 2016.

      [24]P.V.V. Kishore, A.S.C.S. Sastry, Md. Zia Ur Rahman, “Double Technique for Improving Ultrasound Medical Images”, Journal of Medical Imaging and Health Informatics, vol.6, no.3, pp.667-675, 2016.

      [25]M. Lakshmi, Md Zia Ur Rahman, “Efficient Speckle Noise Reduction Techniques for Synthetic Aperture Radars in Remote Sensing Applications”, International Review of Aerospace Engineering Vol.9, no.10, 2016, pp.114-122.

      [26]M. Lakshmi, Md Zia Ur Rahman, “Analysis of Synthetic Aperture Radar Images using Brute Force Thresholding and Gradient Guide Filters”, Journal of Theoretical and Applied Information Technology,Vol.93, no.1, 2016, pp.152-163.

      [27]B. Mala Konda Reddy, Md. Zia Ur Rahman, “Novel Segmentation Technique for Target Tracking in Synthetic Aperture Radars”, International Journal of Control Theory and Applications,Vol.10, no.35, 2017, pp.335-341.

      [28] K. Murali Krishna, Md. Zia Ur Rahman, “Lung Parenchyma Detection using Levelset Segmentation”, International Journal of Control Theory and Applications, Vol.10, no.35, 2017, pp.207-215.

      [29] Y. Deva Raju, B. Manjula, M. Ajay Kuamr, B.V. Rama Mohana Rao, and Md. Zia Ur Rahman, “A Novel Method for Colour Image Compression using Demosicing,” International Journal of Engineering Sciences Research, ISSN: 2230-8512, Vol. 2(3), Aug. 2011.

      [30] D. Srinivasulu, J. Brahamiah Naik, B.V. Rama Mohana Rao, and Md. Zia Ur Rahman, “Image Blur Estimation in Frequency Domain,”International Research Journal of Signal Processing, E-ISSN: 2230-8512, Vol. 2(3), Aug.2011.


 

View

Download

Article ID: 11720
 
DOI: 10.14419/ijet.v7i2.17.11720




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.