A Review on Typical and Modern Brain MRI Image Segmentation Methods and Challenges
D.Sreedevi, Prof.K.Samatha, Prof.M.P.Rao, ,
Affiliations 1:Research Scholar, Department of Physics, Andhra University, India, 2:Professor, Department of Physics, Andhra University, India, 3:Professor, Department of Systems Design, Andhra University, India
Background: Brain image segmentation is one of the essential tasks in medical image analysis. Digital Brain MR Images usually contain Noise, inhomogeneity, and sometimes deviation due to the capturing device's configuration. Therefore, accurate segmentation of brain MRI images is deployed to measure and visualize the brain's anatomical structures, analyze brain changes, delineate pathological regions, and for surgical planning and image-guided interventions. During the past few decades, various segmentation techniques of different accuracy and degree of complexity have been developed and reported in the literature. In this paper, several popular methods are used for brain MRI segmentation and focus on their capabilities, advantages, and pitfalls. Likewise, we also discuss modern image segmentation techniques by Deep Learning Technology and deliberate the metrics to evaluate the brain tumor segmentation and dataset availability performance. Eventually, we suggest future research challenges among brain tumor multimodal imaging techniques.
D.Sreedevi, Prof.K.Samatha, Prof.M.P.Rao."A Review on Typical and Modern Brain MRI Image Segmentation Methods and Challenges". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084, Vol.6, Issue 05,pp.322-329, May - 2019, URL :https://ijcert.org/ems/ijcert_papers/V6I503.pdf,
Keywords : Magnetic Resonance Imaging (MRI), Deep Learning, Segmentation techniques, Convolutional Neural Network (CNN.
 Source retrieved from https://www.envrad.com/difference-between-x-ray-ct-scan-and-mri/ on 20 march 2019.
 Hasan, A., Meziane, F., Aspin, R., & Jalab, H. (2016). Segmentation of Brain Tumors in MRI Images Using Three-Dimensional Active Contour without Edge. Symmetry, 8(11), 132.doi:10.3390/sym8110132
 N. Gordillo, E. Montseny, and P. Sobrevilla, "State of the art survey on MRI brain tumor," In IEEE of Segmentation, 2013.
 N. Otsu, "A Threshold Selection Method from Graylevel Histogram, IEEE Transaction on System", Man. and Cybernetics, vol.9. no.1 .pp. 62-66, 1979.
 A. Aslam, E. Khan, and M.M.S. Beg, Improved edge detection algorithm for brain tumour segmentation, Elsevier, 58 (2015), pp. 430–437.
 Gonzalez, R. C.,Woods, R. E., 2008. Digital image processing. Upper Saddle River, New Jersey.
 Easha Noureen, Dr. Md. Kamrul Hassan, "Brain Tumor Detection Using Histogram Thresholding to Get the Threshold point", IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), Volume 9, Issue 5, PP 14-19, Sep – Oct. 2014.
 Radha, R., Lakshman, B., 2013. Retinal image analysis using morphological process and clustering technique. International Journal of Signal and Image Processing, 4(6), 55-32.
 K.S.A. Viji and J. Jayakumari, "Modified texture based region growing segmentation of M.R. brain images," In Proceedings of the IEEE conference on information and communication technologies, 2013.
 Bilmes, J.A., 1998. A gentle tutorial of the em algorithm and its application to parameter estimation for gaussian mixture and hidden Markov models. International Computer Science Institute.
 R. Chandra and K.R.H. Rao, "Tumor detection in the brain using genetic algorithm G," In 7th international conference on communication, computing and virtualization, 2016.
 M. Shasidhar, V. S. Raja and B. V. Kumar, "MRI Brain Image Segmentation Using Modified Fuzzy C-Means Clustering Algorithm," 2011 International Conference on Communication Systems and Network Technologies, Katra, Jammu, 2011, pp. 473-478, doi: 10.1109/CSNT.2011.102.
 Shweta A. Ingle, Snehal M. Gajbhiye, "Review on Automatic Brain Tumor Detection Technique", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=ART20171047, Volume 6 Issue 2, February 2017, 1553 – 1557
 Yousefi S, Azmi R, Zahedi M. Brain tissue segmentation in M.R. images based on a hybrid of MRF and social algorithms. Medical Image Analysis. 2012;16:840–848
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