Email this article 
A Technique to Tumor Detection from Brain MRI Images Using FCM and Neuro-Fuzzy Classifier
(*) Corresponding author
DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)
Abstract
Segmentation of medical imagery is a challenging problem due to the complexity of the images, as well as to the absence of models of the anatomy that fully capture the possible deformations in each structure. Image segmentation is an indispensable part of the tumour identification, particularly during analysis of Magnetic Resonance (MR) images. Recently, plenty of techniques are available in the literature for detection of brain tumor using MRI images. Most of the works make use of different machine learning techniques to provide the detection accuracy in a more effective way. In our proposed method, we include following major steps, i) Pre-processing, ii) Segmentation, iii) feature extraction, iv Tumor classification. At first, the input image is given to the pre-processing step to make suitable for further image processing steps. Then, the segmentation will be carried out using the fuzzy c-means clustering so that the feature can be computed from the segments itself. Subsequently, the feature extraction methods such as, shape and texture are used to find the features for classification. Finally, the neuro-fuzzy classifier is used to find whether the input image is tumor image or not. The Comparative analysis is carried out with Radial Basis Function (RBF) neural network, Neuro fuzzy and the Feed Forward Neural Network (FFNN) and the obtained results are analysed in terms of sensitivity, specificity and accuracy.
Copyright © 2013 Praise Worthy Prize - All rights reserved.
Keywords
References
Quart-Ul-Ain, Ghazanfar Latif, Sidra Batool Kazmi, M. Arfan Jaffer, Anwar M. Mirza,"Classification and Segmentation of Brain Tumor using Texture analysis,” International Journal of Innovative Computing, Information and Control, 2009.
Jue Wu and Albert C.S. Chung, “A novel framework for segmentation of deep brain structures based on Markov dependence tree,” Elsevier, Vol.46, pp.1027–1036, 2009.
Rajeev Ratan, Sanjay Sharma and S. K. Sharma,"Multiparameter segmentation and quantization of brain tumor from MRI images,” Indian Journal of Science and Technology, Vol.2, No 2, 2009.
Ahmed Kharrat, Karim Gasmi, Mohamed Ben Messaoud, Nacéra Benamrane and Mohamed, “A Hybrid Approach for Automatic Classification of Brain MRI Using Genetic Algorithm and Support Vector Machine,” Leonardo Journal of Sciences, No.17, pp. 71-82, 2010.
Shafaf Ibrahim, Noor Elaiza Abdul Khalid, and Mazani Manaf, “Seed-Based Region Growing (SBRG) vs Adaptive Network-Based Inference System (ANFIS) vs Fuzzy c-Means (FCM): Brain Abnormalities Segmentation,” International Journal of Electrical and Computer Engineering, Vol.5, No.2, pp.94-104, 2010.
Amir Ehsan Lashkari,"A Neural Network based Method for Brain Abnormality Detection in MR Images Using Gabor Wavelets, “International Journal of Computer Applications (0975 – 8887) Vol.4, No.7, July 2010.
Chuin-Mu Wang, Ruey-Maw Chen,"Vector Seeded Region Growing for Parenchyma Classification in Brain MRI,"International Journal of Advancements in Computing Technology, Vol.3, no.2, March 2011.
Chunming Li, Rui Huang, Zhaohua Ding and J. Chris Gatenby, “A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities with application to MRI,” IEEE Transactions On Image Processing, Vol. 20, No. 7, pp.2007-2016, July 2011.
Manisha Sutar, N. J. Janwe,"A Swarm-based Approach to Medical Image Analysis,” Global Journal of Computer Science and Technology, Vol. 11, March 2011.
Nahla Ibraheem Jabbar, and Monica Mehrotra, "Application of Fuzzy Neural Network for Image Tumor Description", Proceedings of World Academy of Science, Engineering and Technology, Vol: 34, 2008.
Pham, D., Xu, C., Prince, J”Current methods in medical image segmentation,” Annual Review of Biomedical Engineering Vol. 2, pp. 315–337, 2000.
Zijdenbos, A., Forghani, R., Evans, A. “Automatic pipeline analysis of 3D MRI data for clinical trials: application to multiple sclerosis,” IEEE transactions on medical imaging, Vol: 21, No: 10, pp. 1280–1291, 2002.
Van-Leemput, K. “Probabilistic brain atlas encoding using bayesian inference,” Book Medical Image Computing and Computer-Assisted Intervention (MICCAI), vol.1, pp.704–711, 2006.
R. Ganesan, and S. Radhakrisham, “Segmentation of Computed Tomography Brains Images Using Genetic Algorithm,” International Journal of Soft Computing, Vol. 4, No. 4, pp. 157-161, 2009.
J. Roerdink, and A. Meijster, “The Watershed Transform: Definitions, Algorithms and Parallelization Strategies,” Fundamenta Informaticae, pp. 187-228, IOS Press, 2001.
W.Wells, W.Grimson, R. Kikinis, F.A. Jolesz, “Adaptive Segmentation of MRI Data,” IEEE Transaction on Medical Imaging, Vol.15, No. 4, pp. 429-442, August 1992.
Nahla Ibraheem Jabbar and Monica Mehrotra, "Application of Fuzzy Neural Network for Image Tumor Description, “World Academy of Science, Engineering and Technology, Vol. 44, pp. 575-577, 2008.
Nicolaos B. Karayiannis, "A Methodology for Constructing Fuzzy Algorithms for Learning Vector Quantization,” IEEE Transactions on Neural Networks, Vol. 8, No. 3, pp. 505-518, May 1997.
Nicolaos B. Karayiannis and Pin-I Pai, "Segmentation of Magnetic Resonance Images Using Fuzzy Algorithms for Learning Vector Quantization," IEEE Transactions on Medical Imaging, Vol. 18, No. 2, pp. 172-180, February 1999.
Fitsum Admasua, Stephan Al-Zubia, Klaus Toenniesa, Nils Bodammerb and Hermann Hinrichsb, "Segmentation of Multiple Sclerosis Lesions from MR Brain Images Using the Principles of Fuzzy-Connectedness and Artificial Neuron Networks," In Proceedings of International Conference on Image Processing, Barcelona, Spain, Vol. 3, 2003.
N. K. Subbanna, M. Shah, S. J. Francis, S. Narayanan, D. L. Collins, D. L. Arnold and T. Arbel, "MS Lesion Segmentation using Markov Random Fields", In Proceedings of International Conference on Medical Image Computing and Computer Assisted Intervention, London, UK, September 2009.
Sun CT, Jang JSR,”A neuro-fuzzy classifier and its applications,” Proc. of IEEE
Int. Conf. on Fuzzy Systems, San Francisco 1:94–98.Int. Conf. on Fuzzy Systems, pp.94–98,1993.
Wen Zhu, Nancy Zeng, Ning Wang, "Sensitivity, Specificity, Accuracy, Associated Confidence Interval and ROC Analysis with Practical SAS Implementations", Proceedings of the SAS Conference, Baltimore, Maryland, pages: 9, 2010.
Hadi Sadoghi Yazdi and Reza Pourreza, “Unsupervised Adaptive Neural-Fuzzy Inference System for Solving Differential Equations”, Applied Soft Computing, Vol.10, pp. 267–275, 2010.
A. Castro and V. Miranda, “Mapping Neural Networks into Rule Sets and Making Their Hidden Knowledge Explicit Application to Spatial Load Forecasting”, In Proceedings of the 14th Power System Computation Conference, 2002.
Hadi Sadoghi Yazdi, S.E. Hosseini and Mehri Sadoghi Yazdi, “Neuro-Fuzzy Based Constraint Programming”,Applied Mathematical Modelling, Vol. 34, pp. 3547-3559, 2010.
Srinivasan Alavandar and M.J. Nigam, “Inverse Kinematics Solution of 3DOF Planar Robot using ANFIS”, International Journal of Computers, Communications & Control, Vol. 3, pp.150-155, 2008.
J.-S. R. Jang, “ANFIS: Adaptive-Network-based Fuzzy Inference Systems,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, no. 03, pp. 665-685, May 1993.
Ciji Pearl Kurian, V.I.George, Jayadev Bhat and Radhakrishna S Aithal, “ANFIS Model for the Time Series Prediction of Interior Daylight Illuminance”, AIML Journal, Vol. 6, No. 3, pp. 35-40, 2006.
Sushmita Mitra and Yoichi Hayashi, “Neuro-Fuzzy Rule Generation: Survey in Soft Computing Framework”, IEEE Transactions on Neural Networks, Vol. 11, No. 3, May 2000.
C. Kezi Selva Vijila and C.Ebbie Selva Kumar, “Interference Cancellation in EMG signal Using ANFIS”, International Journal of Recent Trends in Engineering, Vol. 2, No. 5, November 2009.
Shufu Xie, Shiguang Shan, Xilin Chenand Jie Chen, “Fusing Local Patterns of Gabor Magnitude and Phase for Face Recognition”, IEEE Transactions on Image Processing, Vol. 19, No. 5, pp.1349-1362 , May 2010.
Y. A. Al sultanny, Region Growing and Segmentation Based on by 2D Wavelet Transform to the Color Images, (2008) International Review on Computers and Software (IRECOS), 3 (3), pp. 315 – 323.
Kangavari, M., Abdi, M.J., Tabatabaee, S.M.S., Improving the goal-shooting skill using a genetic-fuzzy system in the robocup soccer simulation league, (2009) International Review on Computers and Software (IRECOS), 4 (1), pp. 133-141.
Refbacks
- There are currently no refbacks.
Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize
