The research offers a fully automatic method for tumor segmentation on Magnetic Resonance Images MRI. In this method, at first in the preprocessing level, anisotropic diffusion filter is applied to the image by 8-connected neighborhood for removing noise from it. In the second step, using Support Vector Machine SVM Classifier for tumor detection accurately. After creating the appropriate mask image, based on the symmetry property in axial and coronary magnetic resonance images. The tumor detected and segmented (Dice coefficient > 0.90) in a few seconds. The method applied on several MR images with different types regardless of the degree of complexity in those images.
| Published in | Biomedical Statistics and Informatics (Volume 2, Issue 2) |
| DOI | 10.11648/j.bsi.20170202.16 |
| Page(s) | 73-76 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
MR Images, Support Vector Machine (SVM), Anisotropic Diffusion Filter, Brain Tumor Detection
| [1] | M. C. Clark, L. O. Hall, D. B. Goldgof, R. Velthuizen, F. R. Murtagh, and M. S. Silbiger, "Automatic tumor segmentation using knowledgebased techniques". IEEE Trans. on Medical Imaging, vol. 17, no. 2, pp. 238.251, April 1998. |
| [2] | S. D. Olabarriaga and A. W. M. Smeulders, "Interaction in the segmentation in medical images: A survey. Medical Image Analysis", vol. 5, no. 2, pp. 127.142, June 2001. |
| [3] | M Sharma, S Mukharjee, “Brain Tumor Segmentation Using Genetic Algorithm and Artificial Neural Network Fuzzy Inference System”, Advances in Computing and Information Advances in Intelligent Systems and Computing Volume 177, pp. 329-339, 2013. |
| [4] | M. Rakesh, T. Ravi, “Image Segmentation and Detection of Tumor Objects in MR Brain Images Using FUZZY C-MEANS (FCM) Algorithm”, International Journal of Engineering Research and Applications, Vol. 2, Issue 3, May-Jun 2012, pp. 2088-2094. |
| [5] | J. selvakumar, A. Lakshmi, T. Arivoli, “Brain Tumor Segmentation and Its Area Calculation in Brain MR Images using K-Mean Clustering and Fuzzy C-Mean Algorithm”, IEEE-International Conference On Advances In Engineering, Science And Management, pp. 186-190, 2012. |
| [6] | A Ravi, A Suvarna, A D'Souza, GRM Reddy and Megha, “A Parallel Fuzzy C Means Algorithm for Brain Tumor Segmentation on Multiple MRI Images”, Proceedings of International Conference on Advances in Computing Advances in Intelligent Systems and Computing Volume 174, pp. 787-794, 2012. |
| [7] | J. Zhou1, K. L. Chan1, V. F. H. Chong, S. M. Krishnan, “Extraction of Brain Tumor from MR Images Using One-Class Support Vector Machine”, Proceedings of the 2005 IEEE, Engineering in Medicine and Biology 27th Annual Conference, pp. 6411-6414, 2005. |
| [8] | L Guo, L Zhao, Y Wu, Y Li, G Xu, Q Yan, “Tumor Detection in MR Images Using One-Class Immune Feature Weighted SVMs”, IEEE TRANSACTIONS ON MAGNETICS, VOL. 47, NO. 10, pp. 3849-3852, OCTOBER 2011. |
| [9] | S Chandra, R Bhat, H Singh, “A PSO Based method for Detection of Brain Tumors from MRI”, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), pp 666-671, 2009. |
| [10] | M Sharma, S Mukharjee, “Brain Tumor Segmentation Using Genetic Algorithm and Artificial Neural Network Fuzzy Inference System (ANFIS)”, Advances in Computing and Information, Springer-Verlag Berlin Heidelberg, AISC 177, pp. 329–339, 2013. |
| [11] | MK Kowar, S Yadav, “Brain Tumor Detection and Segmentation Using Histogram Thresholding”, International Journal of Engineering and Advanced Technology, Volume-1, Issue-4, pp 16-20, April 2012. |
| [12] | Mandeep Kaur, Dr. V. K. Banga “Thresholding And Level Set Based Brain Tumor Detection Using Bounding Box As Seed”, International Journal of Engineering Research & Technology, vol. 2, issue 4, pp 2503-2507, April – 2013. |
| [13] | S Tiwari, A Bansal, R Sagar, “Identification of brain tumors in 2D MRI using automatic seeded region growing method”, ISSN: 2249-5517 & E-ISSN: 2249-5525, Volume 2, Issue 1, pp.-41-43, 2012. |
| [14] | N Behzadfar, H Soltanian-Zadeh, “Automatic segmentation of brain tumors in magnetic resonance Images”, Proceedings of the IEEEEMBS International Conference on Biomedical and Health Informatics, pp 329-332, 2012. |
| [15] | Mehdi Jafari and Reza Shafaghi, “A hybrid approach for automatic tumor detection of brain MRI using support vector machine and genetic algorithm”, Global journal of science, engineering and technology, Issue 3, pp 1-8, 2012. |
| [16] | Mehdi Jafari, Javad Mahmoodi, Reza Shafaghi, “ A Neural Network-based Approach for Brain Tissues Classification Using GA”, Global journal of science, engineering and technology, Issue 7, pp 1-7, 20. |
| [17] | Saeid Fazli, Parisa Nadirkhanlou, "A Novel Method for Automatic Segmentation of Brain Tumors in MRI Images", Research Institute of Modern Biological Techniques University of Zanjan, Iran, 2015. |
| [18] | P. Shantha Kumar and P. Ganesh Kumar, "PERFORMANCE ANALYSIS OF BRAIN TUMOR DIAGNOSIS BASED ON SOFT COMPUTING TECHNIQUES", American Journal of Applied Sciences 11 (2): 329-336, 2014. |
| [19] | M. Madheswaran and D. Anto Sahaya Dhas, "Classification of brain MRI images using support vector machine with various Kernels". Biomedical Research, Volume 26 Issue 3, 2015. |
| [20] | Jianguo Zhang, Kai-Kuang Ma, Meng Hwa Er, "TUMOR SEGMENTATION FROM MAGNETIC RESONANCE IMAGING BY LEARNING VIA ONE-CLASS SUPPORT VECTOR MACHINE", School of Electrical & Electronic Engineering Nanyang Technological University, Singapore, 2009. |
| [21] | J. Weickert. Anisotropic diffusion in image processing, ECMI Series, Teubner, Stuttgart, ISBN 3-519-02606-6, 1998. |
| [22] | Caio A. Palma, Fabio A. M. Cappabianco, Jaime S. Ide Paulo A. V. Miranda. "Anisotropic Diffusion Filtering Operation and Limitations-Magnetic Resonance Imaging Evaluation". Preprints of the 19th World Congress. The International Federation of Automatic Control. Cape Town, South Africa. PP. 3887-3892, 2014. |
| [23] | Alexei A. Samsonov and Chris R. Johnson. "Noise-Adaptive Nonlinear Diffusion Filtering of MR Images With Spatially Varying Noise Levels". Magnetic Resonance in Medicine 52: 798–806, PP. 798-806, 2004. |
APA Style
Mariam Saii, Zaid Kraitem. (2017). Automatic Brain Tumor Detection in MRI Using Image Processing Techniques. Biomedical Statistics and Informatics, 2(2), 73-76. https://doi.org/10.11648/j.bsi.20170202.16
ACS Style
Mariam Saii; Zaid Kraitem. Automatic Brain Tumor Detection in MRI Using Image Processing Techniques. Biomed. Stat. Inform. 2017, 2(2), 73-76. doi: 10.11648/j.bsi.20170202.16
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Download@article{10.11648/j.bsi.20170202.16,
author = {Mariam Saii and Zaid Kraitem},
title = {Automatic Brain Tumor Detection in MRI Using Image Processing Techniques},
journal = {Biomedical Statistics and Informatics},
volume = {2},
number = {2},
pages = {73-76},
doi = {10.11648/j.bsi.20170202.16},
url = {https://doi.org/10.11648/j.bsi.20170202.16},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20170202.16},
abstract = {The research offers a fully automatic method for tumor segmentation on Magnetic Resonance Images MRI. In this method, at first in the preprocessing level, anisotropic diffusion filter is applied to the image by 8-connected neighborhood for removing noise from it. In the second step, using Support Vector Machine SVM Classifier for tumor detection accurately. After creating the appropriate mask image, based on the symmetry property in axial and coronary magnetic resonance images. The tumor detected and segmented (Dice coefficient > 0.90) in a few seconds. The method applied on several MR images with different types regardless of the degree of complexity in those images.},
year = {2017}
}
TY - JOUR T1 - Automatic Brain Tumor Detection in MRI Using Image Processing Techniques AU - Mariam Saii AU - Zaid Kraitem Y1 - 2017/03/01 PY - 2017 N1 - https://doi.org/10.11648/j.bsi.20170202.16 DO - 10.11648/j.bsi.20170202.16 T2 - Biomedical Statistics and Informatics JF - Biomedical Statistics and Informatics JO - Biomedical Statistics and Informatics SP - 73 EP - 76 PB - Science Publishing Group SN - 2578-8728 UR - https://doi.org/10.11648/j.bsi.20170202.16 AB - The research offers a fully automatic method for tumor segmentation on Magnetic Resonance Images MRI. In this method, at first in the preprocessing level, anisotropic diffusion filter is applied to the image by 8-connected neighborhood for removing noise from it. In the second step, using Support Vector Machine SVM Classifier for tumor detection accurately. After creating the appropriate mask image, based on the symmetry property in axial and coronary magnetic resonance images. The tumor detected and segmented (Dice coefficient > 0.90) in a few seconds. The method applied on several MR images with different types regardless of the degree of complexity in those images. VL - 2 IS - 2 ER -
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