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A Review of Brain Tumor Segmentation and Detection Techniques through MR Images

by Nikita Singh, Naveen Choudhary
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 103 - Number 7
Year of Publication: 2014
Authors: Nikita Singh, Naveen Choudhary
10.5120/18085-9128

Nikita Singh, Naveen Choudhary . A Review of Brain Tumor Segmentation and Detection Techniques through MR Images. International Journal of Computer Applications. 103, 7 ( October 2014), 12-16. DOI=10.5120/18085-9128

@article{ 10.5120/18085-9128,
author = { Nikita Singh, Naveen Choudhary },
title = { A Review of Brain Tumor Segmentation and Detection Techniques through MR Images },
journal = { International Journal of Computer Applications },
issue_date = { October 2014 },
volume = { 103 },
number = { 7 },
month = { October },
year = { 2014 },
issn = { 0975-8887 },
pages = { 12-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume103/number7/18085-9128/ },
doi = { 10.5120/18085-9128 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:33:53.990182+05:30
%A Nikita Singh
%A Naveen Choudhary
%T A Review of Brain Tumor Segmentation and Detection Techniques through MR Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 103
%N 7
%P 12-16
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The most common imaging technique for brain is MR imaging it is a non-invasive method. Brain tumors are mainly classified as benign or malignant tumors depending on their growth pattern. The manual analysis of brain tumor on MRI is time consuming and subjective Intensity inhomogeneity is very challenging task image segmentation to avoid thus type of problem, in this paper describe the very efficient and accurate segmentation techniques. This paper presents a comprehensive review of the methods and techniques used to detect brain tumor through MRI image segmentation.

References
  1. J. C. Dunn, "A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters" Journal of Cybernetics, Vol. 3, No. 3, 1973, pp. 32-57.
  2. Kaufman, L. and Rousseeuw, P. J. , 1987, Clustering by Means of Medoids, In Y. Dodge, editor, Statistical Data Analysis, based on the L1 Norm, pp. 405- 416, Elsevier/North Holland, Amsterdam.
  3. Stanley Osher and James A. Sethian. "Fronts propagating with curvature dependent speed: Algorithms based on Hamilton-Jacobi formulations" J. Computational Physics, 79(1):1249, 1988
  4. Huang, Z. , "Extensions to the k-means algorithm for clustering large data sets with categorical values" Data Mining and Knowledge Discovery, 2(3), 1998.
  5. Fraley C. and Raftery A. E. , How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis, Technical Report No. 329. Department of Statistics University of Washington, 1998.
  6. Mie Sato*, Sarang Lakare, Ming Wan, Arie Kaufman, Masayuki Nakujima "A Gradient Magnitude Based Region Growing Algorithm For Accurate Segmentation" IEEE 2000.
  7. Hoppner F. , Klawonn F. , Kruse R. , Runkler T. ," Fuzzy Cluster Analysis" Wiley, 2000.
  8. Pham DL, Xu C, Prince JL (2000) "Current methods in medical image segmentation". Annu Rev Biomed Eng 2:315–337
  9. Pohle R, Toennies KD; "Segmentation of medical images using adaptive region growing, proceedings of SPIE -Medical Imaging" Vol. 4322, 2001, pp. 1337-1346.
  10. Han, J. and Kamber, M. "Data Mining: Concepts and Techniques" Morgan Kauf- mann Publishers, 2001.
  11. J. L. Marroquin, B. C. Vemuri*, S. Botello, F. Calderon, and A. Fernandez-Bouzas "An Accurate and Efficient Bayesian Method for Automatic Segmentation of Brain" MRI IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 21, NO. 8, AUGUST 2002
  12. Lei Jiang, Wenhui Yang. "A Modified Fuzzy C-Means Algorithm for Segmentation of Magnetic Resonance Images" Proc. VIIth Digital Image Computing: Techniques and Applications, Sun C. , Talbot H. , Ourselin S. and Adriaansen T. (Eds. ), 10-12 Dec. 2003, Sydney.
  13. Marcel Prastawa , Elizabeth Bullitt , Sean Ho , Guido Gerig. "Medical Image Analysis" 8 (2004) 275–283.
  14. Alan Wee-Chung Liew and Hong Yan. "Current Methods in the Automatic Tissue Segmentation of 3D Magnetic Resonance Brain Images" Current Medical Imaging Reviews, 2006, 2, 000-000
  15. Liew AWC, Yan H (2006) "Current methods in the automatic tissue segmentation of 3D magnetic resonance brain images" Current Med Imaging Rev 2(1):91–103
  16. Jun Kong1, 2, Jianzhong Wang1, 2, Yinghua Lu Jingdan Zhang1, Yongli Li1, Baoxue Zhang2 "A Novel Approach for Segmentation of MRI Brain Images" IEEE MELECON 2006, May 16-19, Benalmádena (Málaga), Spain
  17. Dana Cobzas, Neil Birkbeck, Mark Schmidt "3D Variational Brain Tumor Segmentation using a High Dimensional Feature Set" IEEE 11th international conference on c Computer Vision, 2007. ICCV 2007.
  18. H. Zhang, J. E. Fritts, S. A. Goldman, "Image Segmentation Evaluation: A Survey of unsupervised methods", computer vision and image understanding, pp. 260-280, 2008.
  19. Aliaksei Maistrou "Level Set Methods - Overview" Computer Aided Medical Procedures, TUM May 17, 2008.
  20. Chunming Li, RuiHuang, Zhaohua Ding, Chris Gatenby, Dimitris Metaxas, and John Gore "A Variational Level Set Approach to Segmentation and Bias Correction of Images with Intensity Inhomogeneity" MICCAI 2008, Part II, LNCS 5242, pp. 1083–1091, 2008
  21. Chunming Li, Chenyang Xu, Senior Member, IEEE, Changfeng Gui, and Martin D "Distance Regularized Level Set Evolution and Its Application to Image Segmentation" IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 12, DECEMBER 2010
  22. Laxman Singh, R. B. Dubey, Z. A. Jaffery Zaheeruddin "Segmentation and Characterization of Brain Tumor from MR Images" IEEE 2009 International Conference on Advances in Recent Technologies in Communication and Computing 2009 978-0-7695-3845-7 .
  23. Yangqiu Song, Changshui Zhang, Jianguo Lee, Fei Wang, Shiming Xiang and Dan Zhang "Semi-supervised discriminative classi?cation with application to tumorous tissues segmentation of MR brain images" Pattern Anal Applic (2009) 12:99–115.
  24. Pal N. R, Pal K, Keller J. M. and Bezdek J. C, "A Possibilistic Fuzzy c-Means Clustering Algorithm", IEEE Transactions on Fuzzy Systems, Vol. 13, No. 4, Pp. 517–530, 2005.
  25. J. C. Dunn, A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters, Journal of Cybernetics, Vol. 3, No. 3, 1973, pp. 32-57.
  26. W. X. Kang, Q. Q. Yang, R. R. Liang, "The Comparative Research on Image Segmentation Algorithms", IEEE Conference on ETCS, pp. 703-707, 2009.
  27. Iftekharuddin, Khan M. , Jing Zheng, Mohammad A. Islam, and Robert J. Ogg. "Fractal-based brain tumor detection in multimodal MRI. " Applied Mathematics and Computation 207, no. 1 (2009): 23-41.
  28. Subramanya Bhat, Sanjeev Kunte R "A mixed model based on Watershed and Active contour algorithms for brain tumor segmentation" 2010 International Conference on Advances in Recent Technologies in Communication and Computing 978-0-7695-4201-0 2010 IEEE
  29. Juang, Li-Hong, and Ming-Ni Wu. "MRI brain lesion image detection based on color- converted K-means clustering segmentation. " Measurement 43. 7 (2010): 941-949.
  30. A. Rajendran and R. Dhanasekaran, A hybrid Method Based on Fuzzy Clustering and Active Contour Using GGVF for Brain Tumor Segmentation on MRI Images, European Journal of Scientific Research, Vol. 61, No. 2, 2011, pp. 305-313.
  31. Chunming Li, Rui Huang, Zhaohua Ding, J. Chris Gatenby, Dimitris N. Metaxas, "A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI" IEEE Transactions on image processing, VOL 20, NO. 7, JULY 2011.
  32. Rastgarpour M. , and Shanbehzadeh J. , Application of AI Techniques in Medical Image Segmentation and Novel Categorization of Available Methods and Tools, Proceedings of the International Multi Conference of Engineers and Computer Scientists 2011 Vol. I, IMECS 2011, March 16-18, 2011, Hong Kong.
  33. R. B. Dubey M. Hanmandlu, Shantaram Vasikarla "Evaluation of Three Methods for MRI Brain Tumor Segmentation" 2011 Eighth International Conference on Information Technology: New Generations 978-0-7695-4367-3/ 2011 IEEE.
  34. Ze-Xuan Ji,Quan-SenSun ,De-ShenXia "A framework with modified fast FCM for brain MR images segmentation" Pattern Recognition 44 (2011) 999–1013
  35. Ze-Xuan Ji, Quan-Sen Sun?, De-Shen Xia "A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain MR image" Computerized Medical Imaging and Graphics 35 (2011) 383–397
  36. Ms. Nikita Singh, Mrs Alka Jindal "A Survey of Different types of Characterization Technique in Ultra sonograms of the Thyroid Nodules" Published in international journal for computer science and informatics volume 1 issue 4. 2012, pg no. 112-115, ISSN: 2231-5292(Print).
  37. A. Rajendrana, R. Dhanasekaranb, "Fuzzy Clustering and Deformable Model for Tumor Segmentation on MRI Brain Image: A Combined Approach" International Conference on Communication Technology and System Design 2011 Procedia Engineering 30 ( 2012 ) 327 – 333
  38. Carlos S. Mendoza, Carmen Serrano "Fast parameter-free region growing segmentation with application to surgical planning" Machine Vision and Applications (2012) 23:165–177
  39. Megha. P. Arakeri • G. Ram Mohana Reddy "Computer-aided diagnosis system for tissue characterization of brain tumor on magnetic resonance images" Springer-Verlag London 2013.
  40. P. Dhanalakshmi & T. Kanimozhi "Automatic Segmentation of Brain Tumor using K-Means Clustering and its Area Calculation ISSN (Print) : 2278-8948, Volume-2, Issue-2, 2013
  41. Hsuan- tien in "texture classification using fractal- based features and support vector machines"
  42. Lingfeng Wang n, Chunhong Pan "Robust level set image segmentation via a local correntropy-based K-means clustering" Pattern Recognition 47 (2014) 1917–1925.
Index Terms

Computer Science
Information Sciences

Keywords

MRI brain tumor segmentation techniques feature extraction classification.

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