Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(556.74KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
Brain Tumor Segmentation and Volume Estimation from T1-Contrasted and T2 MRIs
Amrit Hanuman, Ken Sooknanan
Pages - 48 - 62     |    Revised - 31-05-2018     |    Published - 30-06-2018
Volume - 12   Issue - 2    |    Publication Date - June 2018  Table of Contents
MORE INFORMATION
KEYWORDS
Brain Tumor, Magnetic Resonance Imaging (MRI), Segmentation, Volume Estimation.
ABSTRACT
Amid the variations of the cancer disease, brain tumors account for the majority deaths among young people. To diagnose and treat this deadly disease effectively, analysis of hundreds of medical images such as Magnetic Resonance Imaging (MRI) scans is usually performed. However, the analyses of these scans are still mainly performed manually, making the procedure not only very tedious and time-consuming for doctors, but also error prone and non-repeatable. Attempts have been made to automate this procedure by performing image processing techniques such as thresholding, region-growing, unsupervised learning (e.g. k-means, fuzzy c-means clustering), and supervised learning (e.g. support vector machines). Some require human interaction. The techniques may be applied on one or more MRI sequence scans. Unfortunately, these automated attempts still result in a high level of error, and more computationally complex algorithms do not guarantee an increase in accuracy. This paper presents a novel, fully automatic brain tumor segmentation and volume estimation method using simple techniques on T1-contrasted and T2 MRIs. This new approach implemented five main steps: preprocessing using anisotropic diffusion, segmentation of tumor regions using k-means clustering, region combination using logical and Morphological operations, error checking using temporal smoothing, and volumetric measurement. When compared with five state-of-the-art algorithms, the proposed algorithm outperformed those in past works. Advances were seen by its noise reduction, increase in accuracy and closeness to actual tumor volume.
1 Google Scholar 
2 BibSonomy 
3 Doc Player 
4 Scribd 
5 SlideShare 
1 V. S. Dessai, M. P. Arakeri and G. R. M. Reddy, "A parallel segmentation of brain tumor from magnetic resonance images," in ICCCNT, 2012, pp. 1-6.
2 Bhattacharjee and M. Chakraborty, "Brain tumor detection from MR images: Image processing, slicing and PCA based reconstruction," in EAIT, 2012, pp. 97-101.
3 T. S. D. Murthy and G. Sadashivappa, "Brain tumor segmentation using thresholding, morphological operations and extraction of features of tumor," in ICAECC, 2014, pp. 1-6.
4 M. U. Akram and A. Usman, "Computer aided system for brain tumor detection and segmentation," in ICCNIT, 2011, pp. 299-302.
5 K. Thapaliya and G. R. Kwon, "Extraction of brain tumor based on morphological operations," in ICCM, 2012, pp. 515-520.
6 World Health Organization. "Cancer." Internet: www.who.int/mediacentre/factsheets/fs297-/en/, Feb., 05, 2016 [Jan., 25, 2018].
7 A. A. Siddiqi, A. Khawaja and M. Tariq, "3D volume representation of brain tumor using Image Processing," in ICSIPA, 2011, pp. 75-75.
8 G. M. N. R. Gajanayake, R. D. Yapa, and B. Hewawithana, "Comparison of standard image segmentation methods for segmentation of brain tumors from 2D MR images." in ICIIS, 2009, pp. 301-305.
9 J. Vijay and J. Subhashini, "An efficient brain tumor detection methodology using K-means clustering algorithm," in ICCSP, 2013, pp. 653-657.
10 X. Li, S. Lebonvallet, T. Qiu and S. Ruan, "An improved level set method for automatically volume measure: application in tumor tracking from MRI images," in EMBS, 2007, pp. 808-811.
11 M. P. Beham and A. B. Gurulakshmi, "Morphological image processing approach on the detection of tumor and cancer cells," in ICDCS, 2012, pp. 350-354.
12 The OsiriX Development Team. "OsiriX Imaging Software 2010." Internet: http://www.osirix-viewer.com/, Jan. 1, 2010 [Jan., 25, 2018].
13 J. Liu, M. Li, J. Wang, F. Wu, T. Liu and Y. Pan. (2014, Dec.). "A survey of MRI-based brain tumor segmentation methods." Tsinghua Science and Technology. [Online]. 19 (6), pp. 578-595. Available: 10.1109/TST.2014.6961028
14 N. Otsu. (1979, Jan.). "A threshold selection method from gray-level histograms." IEEE transactions on systems, man, and cybernetics. [Online]. 9(1), pp. 62-66. Available: http://web-ext.u-aizu.ac.jp/course/bmclass/documents/otsu1979.kpdf
15 M. M. Ahmed and D. B. Mohamad. (2008, Feb.). "Segmentation of brain MR images for tumor extraction by combining kmeans clustering and perona-malik anisotropic diffusion model." International Journal of Image Processing. [Online]. 2 (1), pp. 27-34. Available: http://www.cscjournals.org/manuscript/Journals/IJIP/Volume2/Issue1/IJIP-8.pdf
16 P. Perona and J. Malik. (1990, Jul.). "Scale-space and edge detection using anisotropic diffusion." IEEE Transactions on Pattern Analysis and Machine Intelligence. [Online]. 12 (7), pp. 629-639. Available: http://authors.library.caltech.edu/6498/1/PERieeetpami90.pdf
17 C. Tsiotsios and M. Petrou. (2013, May). "On the choice of the parameters for anisotropic diffusion in image processing." Pattern recognition. [Online]. 46 (5), pp. 1369-1381. Available: https://pdfs.semanticscholar.org/6d18/674b370247dc7ffc9ec6bf3cd808bc794eb2.pdf
18 B. H. Menze, A. Jakab, S. Bauer, K. Farahani, J. Kirby, Y. Burren, N. Porz, J. Slotboom, R. Wiest and L. Lanczi. (2015, Oct.). "The multimodal brain tumor image segmentation benchmark (BRATS)." IEEE transactions on medical imaging. [Online]. 34(10), pp. 1993-2024. Available: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6975210
19 M. Gupta, B.P. Rao, V. Rajagopalan, A. Das, and C. Kesavadas, "Volumetric segmentation of brain tumor based on intensity features of multimodality magnetic resonance imaging," in IC4, 2015, pp. 1-6.
20 M. M. Hossam, A. E. Hassanien, and M. Shoman, "3D brain tumor segmentation scheme using K-mean clustering and connected component labeling algorithms," in ISDA, 2010, pp. 320-324.
21 M. A. Qadar and Y. Zhaowen. (2014, Nov.). "Brain Tumor Segmentation: A Comparative Analysis." International Journal of Computer Science Issues. [On-line]. 11(6), pp. 1694-0784. Available: https://arxiv.org/abs/1503.02466
22 M. Angulakshmi and G. G. L. Priya. (2017, Mar.). "Automated brain tumour segmentation techniques- A review." International Journal of Imaging Systems and Technology. [On-line]. 27(1), pp. 66-77. doi:10.1002/ima.22211
23 American Brain Tumor Association. "Malignant brain tumors most common cause of cancer deaths in adolescents and young adults: First comprehensive study of 15-39 year-old population." Internet: www.sciencedaily.com/releases/2016/02/160224132910.htm, Feb., 01, 2016 [Jan., 25, 2018].
Mr. Amrit Hanuman
Manufacturing and Design Engineering, The University of Trinidad and Tobago - Trinidad and Tobago
Dr. Ken Sooknanan
Information and Communications Technology The University of Trinidad and Tobago - Trinidad and Tobago
ken.sooknanan@utt.edu.tt