An Advanced Approach for Extraction of Brain Tumor from Digital MR Images Using Morphology Gradient & ART
(*) Corresponding author
In this work, An Advanced Approach has been proposed for the detection and extraction of brain tumor from magnetic resonance images (MRIs). Magnetic resonance images are visualized and examined using the high order of Angular Radial Transform (ART) and morphological gradient. The Angular Radial Transform (ART) is one of the region-based shape descriptors have many desirable properties such as rotation invariance, robustness to noise, and scale changes. This approach is based on the three steps. Before the segmentation process, the pre-processing of the original image is done by median filter to remove the noise. Then, the morphological gradient is computed and added to the filtered image, and the local statistics values obtained from the high order angular radial transform which are used to calculate the appropriate threshold value. After thresholding, the features of 36 ART of high order are used to extract tumor. These features are then used as input to a support vector machine (SVM) to classify images between normal and abnormal. The experimental results show that the proposed approach is successful to analyze the tumor of the image on testing with different MRIs.
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Inbamalar, T.M., Sivakumar, R., An efficient approach for cancer prediction using genomic signal processing, (2014) International Review on Computers and Software (IRECOS), 9 (3), pp. 585-591.
M.Kanimozhi, C.H. HimaBindu, “Brain MR Image Segmentation Using Self Organizing map”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, No 10,pp 3968-3973, 2013.
Vallabhaneni, R.B., Rajesh, V., Brain tumor segmentation with wavelet watershed and detection using MULTI-SVM classifier, (2014) International Review on Computers and Software (IRECOS), 9 (11), pp. 1807-1815.
F. C. Monteiro and A. Campilho, “Watershed framework to region-based image segmentation,” in Proc. International Conference on Pattern Recognition, ICPR 19th, pp. 1-4, 2008.
S. Murugavalli and V. Rajamani, “A high speed parallel fuzzy C-means algorithm for tumor segmentation,” ICGST International Journal on Bioinformatics and Medical Engineering, vol. 6, no. 1, pp. 29-34, 2006.
S.M. Bhandarkar and P. Nammalwar, “Segmentation of Multispectral MR images Using a Hierarchial Self-Organizing Map Computer-Based Medical System CBMS 2001,” Proc. 14th IEEE Symposium, vol 26, no. 27, 2001, pp. 294-299.
M. C. Clark, L. O. Hall, D. B. Goldgof, R. Velthuizen, F. R. Murtagh, and M. S. Silbiger, “Automatic tumor segmentation using knowledge-based techniques,” IEEE Transactions on Medical Imaging, vol. 17, no. 2, pp. 187-201, 1998.
Iscan, Z., Dokur, Z., & Ölmez, T. (2010). Tumor detection by using Zernike moments on segmented magnetic resonance brain images. Expert Systems with Applications, 37(3), 2540-2549.
S. Ghosal and R. Mehrotra, “Robust Optical Flow Estimation Using Semiinvariant Local Features,” Pattern Recognition, vol. 30(2), 1997, pp. 229-237.
M.-S. Choi and W. -Y. Kim, "The description and retrieval of a sequence of moving objects using a shape variation map," Pattern Recognition Letters, Vol. 25, pp.1369–1375, Sep. 2004.
M.W. Omar, K.Omar, M.F. Nasrudin, Logo recognition system using angular radial transform descriptors, J. Comput. Sci. 7 (2011) 1416–1422.
S.H. Lee, S.Sharma, L.Sang, J.-I. Park, Y.G. Park, An intelligent video security system using object tracking and shape recognition, in: ACIVS, in: LNCS, vol.6915, Springer-Verlag, Berlin, Heidelberg, 2011, pp.471–482.
M. Hearst, "Support vector machines", IEEE Intelligence Systems, pp. 18 - 28, July/August, 1998.
The Moving Picture Experts Group (MPEG), http://www.chiariglione.org/mpeg, 2009.12.01.
Amanatiadis, A., Kaburlasos, V. G., Gasteratos, A., & Papadakis, S. E. (2011). Evaluation of shape descriptors for shape-based image retrieval. Image Processing, 5, 493–499.
[Pooja, C. S. (2012). An effective image retrieval system using region and contour based features. In IJCA proceedings on international conference on recent advances and future trends in information technology (pp. 7–12).
C.Y. Wee, R. Paramesran, On the computational aspects of Zernike moments, Image Vis. Comput. 25 (2007) 967–980.
C. Singh, R. Upneja, Error analysis in the computation of orthogonal rotation invariant moments, J. Math. Imaging Vis. 49 (2014) 251–271.
El- Shayed A El Dahshan, Tamel Hosny, Abdel- badech M. Salem, “Hybrid intelligent techniques for MRI brain images classification”, Elsevier Journal of Digital Signal Processing, Vol. 20 , No 2 ,pp 433-441,2010.
B. Scholkopf, S. Kah-Kay, C. J. Burges, F. Girosi, P. Niyogi, T. Poggio, and V.Vapnik, “Comparing support vector machines with Gaussian kernels to radial basis function classifiers,” IEEE trans Signal Processing, vol. 45, pp. 2758-2765, 1997.
Evangelia I. Zacharaki and Sumei Wang (2009), “MRI-Based Classification of Brain Tumor Type and Grade using SVMRFE,” IEEE Transactions on Medical Imaging, Vol.6 No.10.
J.P.Lewis, Tutorial on SVM, CGIT Lab, USC, 2004.
Burges B.~Scholkopf, editor, “Advances in Kernel Methods--Support Vector Learning”. MIT press, 1998.
Zahran, B.M., Classification of brain tumor using neural network, (2014) International Review on Computers and Software (IRECOS), 9 (4), pp. 673-678.
L. Singh, R. B. Dubey, and Z. A. Jaffery, “Segmentation and characterization of brain tumor from MR images,” in Proceedings of International Conference on Advances in Recent Technologies in Communication and Computing, Kottayam, India, pp. 815-819, 2009.
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