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Faridafshin, Mohammadjavad
- Automated Brain Tumor Segmentation in MR Images Using a Hidden Markov Classifier Framework Trained by Svd-Derived Features
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Authors
Fazel Mirzaei
1,
Mohammad Reza Parishan
1,
Mohammadjavad Faridafshin
1,
Reza Faghihi
2,
Sedigheh Sina
2
Affiliations
1 Department of Medical Radiation Engineering, Shiraz University, IR
2 Radiation Research Center, Shiraz University, Shiraz, IR
1 Department of Medical Radiation Engineering, Shiraz University, IR
2 Radiation Research Center, Shiraz University, Shiraz, IR
Source
ICTACT Journal on Image and Video Processing, Vol 9, No 1 (2018), Pagination: 1844-1848Abstract
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
- 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.
- A New Approach for Contrast Enhancement in SPECT Imaging based on Gradient Approximation and Histogram Equalization (GAHE)
Abstract Views :227 |
PDF Views:0
Authors
Fazel Mirzaei
1,
Mohammadjavad Faridafshin
1,
Vahed Moharramzadeh
1,
Mohammadreza Parishan
1,
Reza Faghihi
2
Affiliations
1 Department of Medical Radiation Engineering, Shiraz University, IR
2 Radiation Research Center, Shiraz University, IR
1 Department of Medical Radiation Engineering, Shiraz University, IR
2 Radiation Research Center, Shiraz University, IR
Source
ICTACT Journal on Image and Video Processing, Vol 10, No 4 (2020), Pagination: 2186-2189Abstract
Nuclear medicine is playing a major role in medical diagnosis. But, due to limitations in both injected radionuclide to the patient’s body and low count rates of the detector, output images are of very low contrast. Several methods have been proposed to improve the contrast of medical images. In this study, a new method is presented for SPECT images. The proposed method is based on the combination of Gradient Approximation (GA) and Histogram Equalization (HE) algorithms to improve the image contrast. Poisson editing concept is deployed to allow the images to be edited and processed in the gradient domain before the reconstruction phase. GA is initially applied on the images to overcome the limitations of HE method. Using the GA concept, image gradients are manipulated first and then the images are reconstructed. These reconstructed images are fed as input for the HE block. Finally, results are presented both qualitatively and quantitatively.Keywords
SPECT, Contrast Enhancement, Histogram Equalization, Gradient Approximation.References
- National Research Council (US) and Institute of Medicine (US) Committee on the Mathematics and Physics of Emerging Dynamic Biomedical Imaging, “Single Photon Emission Computed Tomography”, Mathematics and Physics of Emerging Biomedical Imaging, National Academies Press, 1996.
- M. Agarwal and R. Mahajan, “Medical Image Contrast Enhancement using Range Limited Weighted Histogram Equalization”, Procedia Computer Science, Vol. 125, pp. 149-156, 2018.
- H. Kaur and J. Rani, “MRI Brain Image Enhancement using Histogram Equalization Techniques”, Proceedings of IEEE International Conference on Wireless Communications, Signal Processing and Networking, pp. 770-773, 2016.
- F. Mirzaei and R. Faghihi, “Quantification of Contrast Agent Materials using A New Image Domain Multi Material Decomposition Algorithm based on Dual Energy CT”, British Institute of Radiology, Vol. 1, No. 1, p. 20180008, 2019.
- F. Mirzaei, M. Faridafshin, A. Movafeghi and R. Faghihi, “Automated Defect Detection of Weldments and Castings using Canny, Sobel and Gaussian filter Edge Detectors: A Comparison Study”, Proceedings of IEEE International Conference on Non-Destructive Testing, pp. 101-108, 2017.
- F. Mirzaei, M.R. Parishan, M. Faridafshin, R. Faghihi and S. Sina, “Automated Brain Tumor Segmentation in MR Images using A Hidden Markov Classifier Framework Trained by SVD-Derived Features”, ICTACT Journal on Image and Video Processing, Vol. 9, No. 1, pp. 1844-1848, 2018.
- R. Maini and H. Aggarwal, “A Comprehensive Review of Image Enhancement Techniques”, Journal of Computing, Vol. 2, No. 3, pp. 8-13, 2010.
- S.S. Bedi and R. Khandelwal, “Various Image Enhancement Techniques A Critical Review”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, No. 3, pp. 1605-1609, 2013.
- A.B. Patrick Perez, Michel Gangnet and Andrew Blake, “Poisson Image Editing”, ACM Transactions on Graphics, Vol. 22, No. 3, pp. 313-318, 2003.
- P.J. Hampton and P. Agathoklis, “Comparison of HAAR Wavelet-Based and Poisson-Based Numerical Integration Techniques”, Proceedings of IEEE International Symposium on Circuits Systems, Nano Circuits Fabrication Systems, pp. 1623-1626, 2010.
- I.S. Sevcenco, P.J. Hampton and P. Agathoklis, “A Wavelet Based Method for Image Reconstruction from Gradient Data with Applications”, Multidimensional Systems and Signal Processing, Vol. 26, No. 3, pp. 717-737, 2015.
- P. Bhat, B. Curless, M. Cohen and C.L. Zitnick, “Fourier Analysis of the 2D Screened Poisson Equation for Gradient Domain Problems”, Lecture Notes in Computer Science, Springer, Vol. 5303, No. 2, pp. 114-128, 2008.
- L. Huang, W. Zhao, Z. Sun and J. Wang, “An Advanced Gradient Histogram and its Application for Contrast and Gradient Enhancement”, Journal of Visual Communication and Image Representation, Vol. 31, pp. 86-100, 2015.
- J.M. Morel, A.B. Petro and C. Sbert, “Fourier Implementation of Poisson Image Editing”, Pattern Recognition Letters, Vol. 33, No. 3, pp. 342-348, 2012.
- T. Shibata, M. Tanaka and M. Okutomi, “Gradient-Domain Image Reconstruction Framework with Intensity-Range and Base-Structure Constraints”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2745-2753, 2016, pp. 2745–2753.
- A. Kumar Pandey, P.D. Sharma, P. Dheer, G.K. Parida, H. Goyal, C. Patel, C. Bal and R. Kumar, “Investigating the Role of Global Histogram Equalization Technique for 99m Technetium‑Methylene diphosphonate Bone Scan Image Enhancement”, Indian Journal of Nuclear Medicine, Vol. 33, No. 1, pp. 1-5, 2018.
- Y.M.Y. Abdallah and E. Wagiallah, “Enhancement of Nuclear Medicine Images using Filtering Technique”, International Journal of Science and Research, Vol. 3, No. 8, pp. 916-921, 2014.
- D. Asamoah, E. Ofori, S. Opoku and J. Danso, “Measuring the Performance of Image Contrast Enhancement Technique”, International Journal of Computer Applications, Vol. 181, No. 22, pp. 6-13, 2018.