Open Access Open Access  Restricted Access Subscription or Fee Access

Automatic Brain Tumor Detection through MRI – A Survey

Dr. T. Logeswari


This review paper, intents to analyze and compare the diverse methods of automatic detection of brain tumor through Magnetic Resonance Image (MRI) used in different stages of Computer Aided Detection System (CAD).Tumor detection and segmentation are two key problems in research undertaken on brain diagnosis. The main techniques for detection and segmentation are clustering based, knowledge-based, Model-based, level-set evolution, or combination of them. In particular, the Preprocessing, Enhancement and Segmentation are studied and compared. Classification procedure used to obtain final results is also discussed. In Preprocessing and Enhancement stage, medical image is converted into standard format and is manipulated for noise reduction by background removal, edge sharpening, filtering process and removal of film artifacts. Segmentation determines the process of dividing an image into disjoint homogenous regions of a medical image. Classification helps to compare the system generated result with the radiologist report are studied and compared.


MRI, Preprocessing & Enhancement, Segmentation, Feature Extraction, Feature Selection, Classification.

Full Text:



Jing-Hao Xue,Su Ruan,Bruno Moretti,Marintte Revenue,Daniel Bloyet,”Knowledge-based segmentation and labeling of brain structures from MRI images”,Elsevier on Pattern Recognition,22,395-405,2001.

Zu Y.Shan,Jing Z.Liu,Guang H.Yue,”Automated human frontal lobe identification in MR images based on fuzzy-logic encoded expert anatomic knowledge”,Elsevier on Magne tic Resonance Imaging, 22,607-617,2004.

Karen Chia Ren Lin,Miin-Shen Yang,Hsiu-Chih liu,Jiing-Feng Lirng,Pei-Ning Wang,”Generalized Kohonen’s competitive Learning Algorithms for ophthalmological MR image segmentation”,Elsevier on Magnetic Resonance Imaging,21,863-870,2003.

C. Tsai, B.S. Manjunath, R. Jagadeesan.”Automated Segmentation of brain MR Images”,Pergamon, Pattern Recognition, Vol 28,No 12,March 1995.

L. Amini, H. SoltanianZadeh, C. Lucas. ”Automated Segmentation of Brain Structure from MRI”,Proc. Intl. Soc. Mag.Reson.11, 2003.

Simon K. Warfield, Matthieu Ferrant, Xavier Gallez_ , Arya Nabav Ferenc A. Joleszand Ron Kikinis, “Real-Time Biomechanical Simulation of Volumetric Brain Deformation for Image Guided Neurosurgery”,IEEE on Biomechanical,2000.

Xiao Xuan, Qingmin Liao” Statistical Structure Analysis in MRI Brain Tumor Segmentation”,IEEE on Image and Graphics,2007

K. M. Iftekharuddin, J. Zheng, M. A. Islam, R. J. Ogg “Brain Tumor Detection in MRI: Technique and Statistical Validation”,Memphies, 1983.

Dimitris N Metaxas, Zhen Qian, Xiaolei Huang and Rui Huang,” Hybrid Deformable Models for Medical Segmentation and Registration”,IEEE,ICARCV,2006

Y.M.Salman, M.A.Assal†, A.M.Badawi , S.M.Alian†, M.El- M.El-Bayome ,“Validation Techniques for Quantitative Brain Tumors Measurements”,IEEE on Engineering in medicine & Biology,china sep1-4,2005.

Mark Schmidt, Ilya Levner, Russell Greiner, Albert Murtha, Aalo Bistritz “Segmenting Brain Tumors using Alignment-Based Features”IEEE on Machine learning and application,2005.

E.I. Papageorgiou , P.P. Spyridonos , D. Th. Glotsos , C.D. Stylios ,P. Ravazoula , G.N. Nikiforidis , P.P. Groumpos,” Brain tumor characterization using the soft computing Technique of fuzzy cognitive maps”, Elsevier on Applied Soft Computing, Vol 8,820-828 ,2007.

K. Thangavel, M. Karnan, R. Sivakumar, A. Kaja Mohiden,”Automatic Detection of Micro classification in Mammograms-A Review”, ICGST, Medical image processing.

D. Glotsosl, P. Spyridonos', P. Petalas', D. Cavourasz,V. Zolota, P. Dadioti, Lekk, G. Nikiforidis' “A hierarchical decision tree classification scheme for brain tumour astrocytoma grading using Support Vector Machines”, Image and Signal Processing and Analysis,2003.

Marcel prastawa, Elizabeth Bullitt,Sean Ho,Guido Gerig,” A Brain tumor segmentation framework based on outlier detection. “, Elsevier, Medical Image Analysis, 8,275-283, 2004.

Azadeh yazdan-shahmorad, Hamid soltanian-zadeh,reza A.Zoroofi,”MRSI –Brain tumor characterization using Wavelet and Wavelet packets Feature spaces and Artificial Neural Networks”,IEEE on EMBS,sep 1-5,2004.

Albert K.W.Law, F.K.Law, Francis H.Y.Chan,”A Fast Deformable Region Model for Brain Tumor Boundary Extraction”, IEEE, oct 23-26, USA, 2002.

H F Gray, “Genetic Programming For The Axxlysis Of Nuclear Magnetic Resonance Spectroscopy Data”, Science Direct, 2007.

Hideki yamamoto,Katsuhiko sugita,”Magnetic Resonance image Enhancement using V-Filter”,IEEE,AES Magazine,june 1990.

Selvanayaki.K and Karnan.M, 2010. “CAD System for Automatic Detection of Brain Tumor through Magnetic Resonance Image – A Review”, International Journal of Engineering Science and Technology, Vol.2 (10).

Jaya, J. and K. Thanushkodi, 2011. Implementation of computer aided diagnosis system based on parallel approach of ant based medical image segmentation. J. Comput. Sci., 7: 291-297.

Murugavalli1 .S, Rajamani.V (2007), “An Improved Implementation of Brain Tumor Detection Using Segmentation Based on Neuro Fuzzy Technique”, Journal of Computer Science 3 (11), Pages: 841-846.

Othman, Z.A., H.M. Rais and A.R. Hamdan, 2008. Embedding Malaysian House Red Ant Behavior into an Ant Colony System. J. Comput. Sci., 4: 934-941.

Bricq.S,Collet.Ch,Armspach.J.P(2008), “Unifying Framework for Multimodal brain MRI Segmentation based on Hidden Markov Chains”, Elsevier on Medical Image Analysis, volume 12, issue 6, Pages: 639-652.

Capeela A.S, Alata O, Fernandez C, Lefevre J.C(2000),”Unsupervised Segmentation for automatic detection of brain tumors in MRI”,Pro.International Conference on Image Processing, Volume 1, Pages:6

Cheng H. D. Wang J, and Shi X(2004), “ Micro calcification Detection Using Fuzzy Logic and Scale Space Approaches. Pattern Recognition, 37:363–375

Chunyan Jiang, Xinhua Zhang, Wanjun Huang, Christoph Meinel(2004), “Segmentation and Quantification of Brain Tumor,” IEEE International conference on Virtual Environment, Human-Computer interfaces and Measurement Systems, USA, Pages: 12-14.

]Dorigo. M and Stuztle T, (2005), “Ant Colony Optimization”, PHI ed.

Elasyad A.M,”Predicting the severity of breast masses with Ensemble of basiyean classifies”, Journal of Computer Science, Voilume 6 page 576-584.

ErikDam, Marcoloog, Marloes Letteboer(2004), “Integrating Automatic and Interactive Brain tumor Segmentation”, Proceedings of the 17th International Conference on Pattern Recognition (ICPR’04), volume 3, Pages: 790-793.

Farahat A.S., El-Dewany E.M., El- Hefnawi F.M., Y.M.Kadah, A.A.Youssef (2006), “Calculations of Heating Patterns of Interstitial Antenna for Brain Tumors Hyperthermia Treatment Planning”, The 23thNational Radio Science Conference, Egypt, Page 178-182.

Jianhau Xuan, Tiilay Adali, Yue Wang.C(1995), “Segmentation of magnetic resonance brain image integrating region growing and edge detection ”, 29th Annual International Conference of the IEEE In Engineering in Medicine and Biology Society, Pages: 1595-1598.

Kai Xie, Jie Yang, Zhang.Z.G, Zhu. Y.M (2005), “Semiautomated brain tumor and edema Segmentation using MRI”, Elsevier, European journal of Radiology 56, Pages: 12-19.

Kannan S.R(2005), “Segmentation of MRI Using New Unsupervised Fuzzy C mean Algorithm”, ICGST-GVIP Journal, Volume 5, Issue 2.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.