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A Segmentation based Automated System for Brain Tumor Detection
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 153 - Number 10 |
| Year of Publication: 2016 |
| Authors: Md. Sujan, Nashid Alam, Syed Abdullah Noman, M. Jahirul Islam |
10.5120/ijca2016912177
|
Md. Sujan, Nashid Alam, Syed Abdullah Noman, M. Jahirul Islam . A Segmentation based Automated System for Brain Tumor Detection. International Journal of Computer Applications. 153, 10 ( Nov 2016), 41-49. DOI=10.5120/ijca2016912177
Abstract
The functionality of brain can be disrupted by brain tumor, which is an abnormal growth of tissue in brain or central spine. Due to undefined size, shape and location, detection of brain tumor from MRI (Magnetic Resonance Imaging) is a challenging and difficult task. Previous tumor segmentation methods were generally based on intensity enhancement techniques on T1-weighted image, which was appeared with gadolinium contrast agent on strictly uniform intensity patterns. This paper presents a new method based on Thresholding along with morphological image analysis techniques to detect brain tumor from MRI image. The image was first converted to grayscale and then noises were removed by applying different filtering techniques. The grayscale image was then converted to binary image adding 0.3 with the Otsu's threshold value to perfectly segment the tumor region. Afterwards, morphological operations were performed to detect the tumor that contains the brightest part of the MRI. The method suggested for detection was tested over 72 FLAIR images of 72 patients taken from BRATS Brain Tumor database, out of which the proposed algorithm was able to detect tumor from 61 images successfully. Experimental result showed an accuracy rate of 84.72% in detecting 61 patients Brain Tumor which is very much promising compares to other existing method.
References
- Olaf Dietrich “Brain MRI image types”, Ludwig-Maximilian-University of Munich.
- S. N. Sulaiman, S. M. Che Ishak, I. Isa, N. Hamzah : “De-noising of Noisy MRI Brain Image Using The Switching-based Clustering Algorithm”, IEEE International Conference on Control System, Computing and Engineering(ICCSCE), 28 - 30 November 2014, Penang, Malaysia.
- N. Sachin:“brain tumor detection based on bilateral symmetry information”, International Journal of Engineering Research and Applications( IJERA),ISSN: 2248-9622.
- http://www.med.harvad.edu/AANLIB/home.html,accessed on 8 August 2013.
- M. K. Kowari,S. Yadav.2012: “Brain Tumor Detection and Segmentation using Histogram Thresholding”, International Journal of Engineering and Advanced Technology(IJEAT) ISSN: 2249-898, Volume-1, Issue-4, Journal, India.
- M. M. Ahmed, D. Bin Mohamad: “Segmentation of Brain MR Images for Tumor Extraction by Combining K-means Clustering and Perona-Malik Anisotropic Diffusion model.International Journal of Image Processing, Volume(2): Issue(1).
- Nagalkaar V.J, Asole S.S.2012: “Brain Tumor Detection using Digital Image Processing based on Soft Computing,” Journal of Signal and Image Processing, Volume 3, Issue 3, Issn: 0976-8882.
- P. Vasuda, S. Satheesh.2010: “Improved Fuzzy C-Means Algorithm for MR Brain Image Segmentation”, International Journal on Computer Science and Engineering (IJCSE), vol. 02, no.05, pp 1713-1715.
- Prastawa M, Bullitt E, Ho S, Gerig G.2004: “A brain tumor segmentation framework based on outlier detection”, Medical Image Analysis.
- Zhou J, Chan K. L, Chongand VFH, Krishnan SM.2005: “Extraction of brain tumor from MR images using one-class support vector machine ”, Conf Proc IEEE Eng Med Bio Soc. 2005;6:6411-4.
- Y. Sun, B. Bhanu, S. Bhanu.2009: “Automatic Symmetry-integrated Brain Injury Detection in MRI Sequences”, Conference, Proc of the International Conference on Image Processing, ICIP 2009, 7-10 November 2009, Cairo, Egypt.
- S. Zin Oo, Aung Soe Khaing “brain tumor detection and segmentation using watershed segmentation and morphological operation”, IJRET; ISSN: 2319-1163.
- http://challenge.kitware.com/midas/folder/102,accessed on 10 November 2015.
- B. Menze, A. Jakab, S. Bauer, M. Reyes, M. Prastawa, and K. Van Leemput, “MICCAI 2012 Challenge on Multimodal Brain Tumor Segmentation”.
- http://www.mathworks.com/matlabcentral/fileexchange/29067-look3d--a-3-d-image-viewer,accessed on 15 November 2015.
- Otsu, N., "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, 1979, pp. 62-66.
- Van Solihin and C. G. Leedham “The Multi-stage Approach to Grey-Scale Image Thresholding for Specific Applications”.
- The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS); IEEE Transaction on Medical Imaging, Vol. 34, No. 10, October 2015.
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