ICTACT Journal on Image and Video Processing

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Automated Brain Tumor Segmentation in MR Images Using a Hidden Markov Classifier Framework Trained by Svd-Derived Features


Affiliations
1 Department of Medical Radiation Engineering, Shiraz University, Iran, Islamic Republic of
2 Radiation Research Center, Shiraz University, Shiraz, Iran, Islamic Republic of
     

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Interpreting brain MR images are becoming automated, to such extent that in some cases “all” the diagnostic procedure is done by computers. Therefore, diagnosing the patients is done by a comparably higher accuracy. Computer models that have been trained by a priori knowledge act as the decision makers. They make decisions about each new image, based on the training data fed to them previously. In case of cancerous images, the model picks that image up, and isolates the malignant tissue in the image as neatly as possible. In this paper we have developed an unsupervised learning system for automatic tumor segmentation and detection that can be applied to low contrast images.

Keywords

Image Segmentation, Hidden Markov Model, Singular Value decomposition, Wavelet Analysis.
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  • N. Gordillo, E. Montseny and P. Sobrevilla, “State of the Art Survey on MRI Brain Tumor Segmentation”, Magnetic Resonance Imaging, Vol. 31, No. 8, pp. 1426-1438, 2013.

  • B. Likar, P. Markelj and D. Tomaz, “A review of 3D / 2D Registration methods for Image-Guided Interventions”, Medical Image Analysis, Vol. 16, No. 3, pp. 642-661, 2012.

  • E.H. Guerrout, R. Mahiou and S. Ait-Aoudia, “Medical Image Segmentation using Hidden Markov Random Field A Distributed Approach”, Proceedings of 3rd International Conference on Digital Information Processing and Communications, pp. 423-430, 2013.

  • S. Faisan, L. Thoraval, J. Armspach and F. Heitz, “Unsupervised Learning and Mapping of Active Brain Functional MRI Signals Based on Hidden Semi-Markov Event Sequence Models”, IEEE Transactions on Medical Imaging, Vol. 24, No. 2, pp. 263-276, 2005.

  • C.A.S. Sergio Pereira, Adrino Pinto and Victor Aves, “Brain Tumor Segmentation using Convolutional Neural Networks in MRI Images”, IEEE Transactions on Medical Imaging, Vol. 35, No. 5, pp. 1240-1251, 2016.

  • M.K.N. Nabizadeh and M. Dorodchi, “Automatic Tumor Lesion Detection and Segmentation using Modified Winnow Algorithm”, Proceedings of IEEE 12th International Symposium on Biomedical Imaging, pp. 212218, 2015.

  • I.G. Ivan Cabria, “Automated Localization of Brain Tumors in MRI using Potential-K-Means Clustering Algorithm”, Proceedings of 12th Conference on Computer and Robot Vision, pp. 1-4, 2015.

  • H. Choi, R. G. Baraniuk and S. Member, “Multiscale Image Segmentation using Wavelet-Domain Hidden Markov Models”, IEEE Transactions on Image Processing, Vol. 10, No. 9, pp. 1309-1321, 2001.

  • S.L. Jui et al., “Brain MR Image Tumor Segmentation with 3-Dimensional Intracranial Structure Deformation Features”, IEEE Intelligent Systems, Vol. 31, No. 2, pp. 1-9, 2015.

  • I. Ali and C. Direko, “Review of MRI-based Brain Tumor Image Segmentation using Deep Learning Methods”, Proceedings of 12th International Conference on Application of Fuzzy Systems and Soft Computing, pp. 317324, 2016.

  • Z. Hong, “Algebraic Feature Extraction of Image for Recognition”, Pattern Recognition, Vol. 24, No. 3, pp. 211219, 1991.

  • Andrew Blake, Pushmeet Kohli and Carsten Rother, “Markov Random Fields for Vision and Image Processing”, MIT Press, 2011.

  • H. Miar-Naimi and P. Davari, “A New Fast and Efficient HMM-Based Face Recognition System using a 7-State HMM Along with SVD Coefficients”, Iranian Journal of Electrical and Electronic Engineering, Vol. 4, No. 1, pp. 4657, 2008.

  • T.L. Griffiths and A. Yuille, “Technical Introduction: A Primer on Probabilistic Inference”, Master Thesis, Department of Statistics, University of California, pp. 1-57, 2008.

  • T. Chen and T.S. Huang, “Region Based Hidden Markov Random Field Model for Brain MR Image Segmentation”, International Journal of Computer and Information Engineering, Vol. 1, No. 4, pp. 1129-1132, 2007.

  • J.L. Marroquin, E.A. Santana and S. Botello, “Hidden Markov Measure Field Models for Image Segmentation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 11, pp. 1380-1387, 2003.

  • R. Azmi and N. Norozi, “A New Markov Random Field Segmentation Method for Breast Lesion Segmentation in MR Images”, Journal of Medical Signals and Sensors, Vol.1, No. 3, pp. 156-164, 2011.

  • Y. Zhang, M. Brady and S. Smith, “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm”, IEEE Transactions on Medical Imaging, Vol. 20, No. 1, pp.45-57, 2001.

  • E. Levitan, M. Chan and G.T. Herman, “Image-Modeling Gibbs Priors”, Graphical Models and Image Processing, Vol. 57, No. 2, pp. 117-130, 1995.

  • H. Derin and H. Elliot, “Modeling and Segmentation of Noisy and Textured Images using Gibbs Random Fields”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 9, No. 1, pp. 39-55, 1987.

  • T. Zhang, Y. Xia and D. Dagan, “Hidden Markov Random Field Model based Brain MR Image Segmentation using Clonal Selection Algorithm and Markov Chain Monte Carlo Method”, Biomedical Signal Processing and Control, Vol.12, pp. 10-18, 2014.

  • R.S.A. Padma Nanthagopal, “Wavelet Statistical Texture Features-based Segmentation and Classification of Brain Computed Tomography Images”, IET Image Processing, Vol. 7, No. 1, pp. 25-32, 2013.

  • H.S. Abdulbaqi and A.F. Omar, “Detecting Brain Tumor in Magnetic Resonance Images using Hidden Markov Random Fields and Threshold Techniques”, Proceedings of IEEE Student Conference on Research and Development, pp. 1617, 2014.


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  • Automated Brain Tumor Segmentation in MR Images Using a Hidden Markov Classifier Framework Trained by Svd-Derived Features

Abstract Views: 282  |  PDF Views: 0

Authors

Fazel Mirzaei
Department of Medical Radiation Engineering, Shiraz University, Iran, Islamic Republic of
Mohammad Reza Parishan
Department of Medical Radiation Engineering, Shiraz University, Iran, Islamic Republic of
Mohammadjavad Faridafshin
Department of Medical Radiation Engineering, Shiraz University, Iran, Islamic Republic of
Reza Faghihi
Radiation Research Center, Shiraz University, Shiraz, Iran, Islamic Republic of
Sedigheh Sina
Radiation Research Center, Shiraz University, Shiraz, Iran, Islamic Republic of

Abstract


Interpreting brain MR images are becoming automated, to such extent that in some cases “all” the diagnostic procedure is done by computers. Therefore, diagnosing the patients is done by a comparably higher accuracy. Computer models that have been trained by a priori knowledge act as the decision makers. They make decisions about each new image, based on the training data fed to them previously. In case of cancerous images, the model picks that image up, and isolates the malignant tissue in the image as neatly as possible. In this paper we have developed an unsupervised learning system for automatic tumor segmentation and detection that can be applied to low contrast images.

Keywords


Image Segmentation, Hidden Markov Model, Singular Value decomposition, Wavelet Analysis.

References