An Overview on Automated Brain Tumor Segmentation Techniques

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-40 Number-1
Year of Publication : 2016
Authors : Arati Kothari, Dr. B. Indira
DOI :  10.14445/22312803/IJCTT-V40P108

MLA

Arati Kothari, Dr. B. Indira "An Overview on Automated Brain Tumor Segmentation Techniques". International Journal of Computer Trends and Technology (IJCTT) V40(1):45-48, October 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Segmentation of brain tumor is a very important and crucial step in the initial detection of tumor in the Medical Image Analysis. Though various methods are present for brain tumor segmentation, but detection of tumor still is a challenging task since for researchers as tumor possesses complex characteristics in appearance and boundaries. Brain tumor segmentation must be done with precision in the clinical practices. The objective of this review paper is to presents a comprehensive overview for MRI brain tumor segmentation methods. In this paper, various segmentation techniques have been discussed. Comparative analysis among these various segmentation conventions has been discussed in brief.

References
1) T. Tanatipanond and N. Covavisaruch.A Multiscale Approach to Deformable Contour for Brain MR Images by Genetic Algorithm. The Third Annual National Symposium on Computational Science and Engineering.1999; pp. 306- 315.
2) Zavaljevski A, Dhawan A, Gaskil M, Ball W,Johnson D. Multi-level adaptive segmentation of multi-parameter MR brain images. Computerized Medical Imaging and Graphics2000;24:87 98
3) Yongyue Zhang, Michael Brady, Stephen 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, Jan 2001.
4) Moon N, Bullitt E, Leemput K, Gerig G. Model based brain and tumor segmentation.Int. Conf. on Pattern Recognition 2002;528-531.
5) M. Sezgin, B. Sankur . Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13 (1) (2004) 146-165.
6) Valdes-Cristerna R, Medina-Banuelos V, Yanez-Suarez O. Coupling of radial basis network and active contour model for multispectral brain MRI segmentation. IEEE Trans. Biomed. Eng. 2004;51:459-70.
7) Pierre-yves bondjau,Gregoire Malandain. Atlas-based automatic segmentation of MR images: Validation study on the brainstem in radiotherapy context. International Journal of Radiation Oncology . 2005;Volume 61, Issue 1 , Pages 289-298.
8) Wen-Liang Hung, Miin-Shen Yang and De-Hua Chen.Parameter selection for suppressed fuzzy c-means with an application to MRI segmentation. Pattern Recognition Letters. 2006; Vol.27, No.5, pp.424-438.l
9) Murugavalli and Rajamani.A High Speed parallel Fuzzy CMean Algorithm for brain tumor segmentation. ICGST International Journal on Bioinformatics and Medical Engineering2006;Vol.6, No.1,pp.29-34.
10) Martin-Landrove M, Villalta R. Brain tumor image segmentation using neural networks. Proc. of International Society of Magnetic Resonance in Medicine 2006; 14:1610.
11) Huang G, Zhu Q, Siew C. Real-time learning capability of neural networks. IEEE Trans. on Neural Networks 2006; 17:863-78.
12) Ning Li; Miaomiao Liu; Youfu Li.Image Segmentation Algorithm using Watershed Transform and Level Set Method. International Conference on Acoustics, Speech and Signal Processing. 2007; April 2007: I-613 - I-616.
13) Pan, Zhigeng; Lu, Jianfeng.A Bayes-Based Region-Growing Algorithm for Medical Image Segmentation. Computing in Science & Engineering. 2007;Volume 9, Issue 4,Page(s):32 – 38.
14) Murugavalli1, V. Rajamani. An Improved Implementation of Brain Tumor Detection Using Segmentation Based on Neuro Fuzzy Technique.Journal of Computer Science. 2007;3 (11): 841-846.
15) Khotanlou H, Colliot O, Bloch I. Automatic brain tumor segmentation using symmetry analysis and deformable models. Int. Conf. on Advances in Pattern Recognition 2007;198-202.
16) Ming-Ni Wu, Chia-Chen Lin and Chin-Chen Chang.Brain Tumor detection using Color-Based K-means Clustering Segmentation . Proc of International conference on IIHMSP 2008.
17) Yeh J, Fu C. A hierarchial genetic algorithm for segmentation of multi-spectral human brain MRI. Expert Systems with Applications 2008;34:1285-95.
18) Xinyu Du, Yongjie Li, Dezhong Yao.A Support Vector Machine Based Algorithm for Magnetic Resonance Image Segmentation.International Conference on Natural Computation. 2008; vol. 3, pp. 49-53.
19) Jabbar N, Mehrotra M. Application of fuzzy neural network for image tumor description.Proc. of World Academy of Science, Engineering and Technology.2008;34:575-77.
20) Laxman singh,R.B.Dubey,Z.AJaffery.Segmentation and characterization of Brain tumor from MR images. International conference on Advances in Recent Technologies in communication and Computing 2009.
21) Ruoyu Du and Hyo Jong Lee.A modified-FCM segmentation algorithm for brain MR images. In proceedings of ACM International Conference on Hybrid Information Technology.2009; pp.25-27.
22) P. Vasuda et. al. Improved Fuzzy C-Means Algorithm for MR Brain Image Segmentation. International Journal on Computer Science and Engineering. 2010; Vol. 02, No. 05, 1713-1715.
23) Dr. H. B. Kekre et. Al, Dr.Tanuja Sarode.Detection Of Tumor in MRI using Vector Quantization. International Journal of Engineering Science and Technology.2010;Vol. 2(8), pp.3753-3757.
24) Xiao K, Ho S, Bargiela A. Automatic brain MRI segmentation scheme based on feature weighting factors selection on fuzzy C means clustering algorithms with Gaussian smoothing. International Journal of Computational Intelligence in Bioinformatics and Systems Biology 2010;1:316-3.
25) An Effective Approach for Segmentation of MRI Images: Combining Spatial Information with Fuzzy C-Means Clustering European Journal of Scientific Research ISSN 1450-216X Vol.41 No.3 (2010), pp.437-451.
26) T.Logeswari and M.Karnan,An Improved Implementation of Brain Tumor Detection Using Segmentation Based on Hierarchical Self Organizing Map International Journal of Computer Theory and Engineering, Vol. 2, No. 4, August, 20101.

Keywords
Brain tumor, Image Segmentation, Medical Image Analysis, MRI.