Severity Analysis of Brain Tumor in MRI Images Using Modified Multi-texton Structure Descriptor and Kernel-SVM

Image segmentation is to recognize structures in the image that are expected to signify scene objects. It is widely used by the radiologists to segment the medical images into meaningful regions. Thus, various segmentation techniques in medical imaging depending on the region of interest had been proposed. In this article, a robust brain tumor classification method is proposed, which focuses on the structural analysis on both tumorous and normal tissues. The proposed system consists of preprocessing, segmentation, feature extraction and classification. In preprocessing steps, anisotropic filter is used to eliminate the noise and enhances the image quality for skull-stripping process. In feature extraction, some specific features are extracted using texture as well from intensity using modified multi-texton structure descriptor. The hybrid kernel is designed in the classification stage and applied to training of support vector machine to perform automatic classification of tumor in magnetic resonance imaging (MRI) images. For comparative analysis, the proposed method is compared with the existing works using k-fold cross-validation method. The accuracy level (93 %) for our proposed approach (αK1, K1 + K2, K1 * K2) proved is good at detecting the tumors in the brain MRI images.

[1]  Mohammed Lahcen Bensaad,et al.  Boosting the Capacity of Diacritics-Based Methods for Information Hiding in Arabic Text , 2013 .

[2]  C. L. Philip Chen,et al.  A Multiple-Kernel Fuzzy C-Means Algorithm for Image Segmentation , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[3]  B. Julesz.  Textons, the elements of texture perception, and their interactions , 1981, Nature.

[4]  Nicolaos B. Karayiannis,et al.  A methodology for constructing fuzzy algorithms for learning vector quantization , 1997, IEEE Trans. Neural Networks.

[5]  Khan M. Iftekharuddin,et al.  Efficacy of Texture, Shape, and Intensity Feature Fusion for Posterior-Fossa Tumor Segmentation in MRI , 2011, IEEE Transactions on Information Technology in Biomedicine.

[6]  Dhanasekaran Raghavan,et al.  Automatic detection of brain tumor in magnetic resonance images using multi‐texton histogram and support vector machine , 2013, Int. J. Imaging Syst. Technol..

[7]  Isabelle Bloch,et al.  3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models , 2009, Fuzzy Sets Syst..

[8]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..

[9]  Richa Mishra,et al.  MRI based brain tumor detection using wavelet packet feature and artificial neural networks , 2010, ICWET.

[10]  Annette Sterr,et al.  MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization , 2005, IEEE Transactions on Information Technology in Biomedicine.

[11]  R. Dhanasekaran,et al.  Brain Tumor Detection using Fuzzy Support Vector Machine Classification based on a Texton Co-occurrence Matrix , 2013 .

[12]  A. C. Phadke,et al.  Feature Extraction and Texture Classification in MRI , 2010 .

[13]  W. Eric L. Grimson,et al.  Adaptive Segmentation of MRI Data , 1995, CVRMed.

[14]  Songthip Ounpraseuth,et al.  Estimating misclassification error: a closer look at cross-validation based methods , 2012, BMC Research Notes.

[15]  K. Thanushkodi,et al.  Tracking Algorithm For De-Noishing of MR Brain Images , 2009 .

[16]  Aliaa A. A. Youssif,et al.  MRI Brain Image Segmentation based on Wavelet and FCM Algorithm , 2012 .

[17]  C. Chellamuthu,et al.  An efficient approach for brain tumour detection based on modified region growing and neural network in MRI images , 2012, 2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET).

[18]  O. Demirkaya.  Anisotropic diffusion filtering of PET attenuation data to improve emission images. , 2002, Physics in medicine and biology.

[19]  K S Cheng,et al.  Segmentation of multispectral magnetic resonance image using penalized fuzzy competitive learning network. , 1996, Computers and biomedical research, an international journal.

[20]  Nahla Ibraheem Jabbar,et al.  Application of Fuzzy Neural Network for Image Tumor Description , 2008 .

[21]  King-Sun Fu,et al.  A survey on image segmentation , 1981, Pattern Recognit..

[22]  Tamer Ölmez,et al.  Tumor detection by using Zernike moments on segmented magnetic resonance brain images , 2010, Expert Syst. Appl..

[23]  J. Umamaheswari,et al.  A Hybrid Approach for DICOM Image Segmenta- tion Using Fuzzy Techniques , 2012 .

[24]  Sudipta Roy,et al.  Detection and Quantification of Brain Tumor from MRI of Brain and it's Symmetric Analysis , 2012 .

[25]  R. Dhanasekaran,et al.  Enhanced Possibilistic Fuzzy C-Means Algorithm for Normal and Pathological Brain Tissue Segmentation on Magnetic Resonance Brain Image , 2013, Arabian Journal for Science and Engineering.

[26]  Alan C. Evans,et al.  Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis , 2002, IEEE Transactions on Medical Imaging.

[27]  B. Venkateswara Reddy,et al.  IDENTIFYING BRAIN TUMOUR FROM MRI IMAGE USING MODIFIED FCM AND SUPPORT VECTOR MACHINE , 2013 .

[28]  Klaus D. Tönnies,et al.  Segmentation of multiple sclerosis lesions from MR brain images using the principles of fuzzy-connectedness and artificial neuron networks , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[29]  Nicolaos B. Karayiannis,et al.  Segmentation of magnetic resonance images using fuzzy algorithms for learning vector quantization , 1999, IEEE Transactions on Medical Imaging.