TECHNIQUES FOR MRI BRAIN TUMOR DETECTION: A SURVEY
Keywords:
Brain Tumor, Magnetic Resonance Image (MRI), SegmentationAbstract
Brain magnetic resonance (MR) image segmentation is a very important and challenging task needed for the diagnosis of brain tumors and other neurological diseases. The brain tumor is an abnormal growth of cells in the brain that multiplies in an abnormal and uncontrollable way. The detection of brain tumor is a challenging task due to complexity in the structure of brain. Accurate segmentation of brain tissues is very important and critical for the correct diagnosis of tumors. In this paper various techniques used to detect the tumor in human brain using MRI images has been surveyed.
References
Geoff Dougherty, “Digital Image Processing for Medical Applications” (ISBN: 9780521860857, May 2009).
M. Kaus, S. K. Warfield, F. A. Jolesz, and R. Kikinis, “Adaptive Template Moderated Brain Tumor Segmentation in MRI,” in Proc. Bildverarbeitung f¨ur die Medizin, 1999, pp. 102–106.
Demirhan. A, Toru. M, Guler. I, “Segmentation of Tumor and Edema Along With Healthy Tissues of Brain Using Wavelets and Neural Networks", IEEE Journal of Biomedical and Health Informatics, Vol. 19, No. 4, July 2015. pp. 1451 – 1458.
H.H. Chang, D. J. Valentino, G. R. Duckwiler, and A. W. Toga, “Segmentation of Brain MR Images Using A Charged Fluid Model,” IEEE Trans. Biomed. Eng., Vol. 54, No. 10, pp. 1798–1813, Oct. 2007.
Easha Noureen and Dr. Kamrul Hassan, Md.,“ Brain Tumor Detection Using Histogram Thresholding to Get the Threshold point” IOSR Journal of Electrical and Electronics Engineering, Vol. 9,pp.14-19, 2014.
Heena Hooda, Om Prakash Verma and Tripti Singhal ,“Brain tumor segmentation: “A Performance Analysis using K-Means, Fuzzy C-Means and Region Growing Algorithm” , IEEE International Conference on advanced Communication Control and Computing Technologies (ICACCCT), 2014, pp.1621 – 1626.
Padma Nanda Gopal and R. Sukanesh, "Wavelet Statistical Feature Based Segmentation and Classification of Brain Computed Tomography Images” IET Image Process Vol.7 pp 25-32 2013.
Yudong Zhang, Zhengchao Dong, Lenan Wu “A Hybrid Method for MRI Brain Image Classification”, ELSEVIER Expert System with Application 38 (2011) 10049-100.
Sumitra, Saxena, "Brain Tumor Classification Using Back Propagation Neural Network”, I.J.Image, Graphics and Signal Processing, 2013, 2, 45-50.
Mehdi Jafari and Reza Shafaghi,”A Hybrid Approach for Automatic Tumor Detection of Brain MRI Using Support Vector Machine and Genetic Algorithm” Global Journal of Science, Engineering and Technology (ISSN: 2332-2441), 2012.
El- Sayed Ahmed El-Dahshan, Tamer Hosny, Abdel-Badeesh, “Hybrid Intelligent Technique for MRI Brain Image Classification”, ELSEVIER Digital Signal Processing 20(2010) 433-441.
Parisot, Sarah, Hugues Duffau, Stéphane Chemouny and Nikos Paragios."Graph Based Detection, Segmentation and Characterization of Brain Tumors." In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference, pp. 988-995.
J. Selvakumar, A. Lakshmi, T. Arivoli "Brain Tumor Segmentation and its Area Calculation in Brain MR Images Using K-Mean Clustering and Fuzzy C Mean Algorithm", IEEE Intl. Conference on Advances In Engineering, Science And Management (ICAESM -2012), March 30, 31, 2012.