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Extracting information for medical purposes from magnetic resonance imaging is critically important for diagnostic and treatment plans. In this paper, a simple algorithm for tumor segmentation of Magnetic resonance imaging (MRI) is introduced. The novelty incorporates, preserving fine details of the input image while detecting the boundary accurately. Tumor segmentation is carried out by set of pre processing steps followed by morphological operations. Rough contour of the tumor is localized to reduce the search space for the boundary. Line drawing algorithm in cooperated with pixel selection criteria is used to detect the accurate boundary. The algorithm is evaluated in terms of the performance and accuracy with radiologist labelled ground truth MRI scans. Simulation results show that the proposed algorithm provides better identification with above 95% of accuracy, for clearly distinguishable tumors in relation to conventional contour detection methods.
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Othman, Mohd Fauzi, and Mohd Ariffanan Mohd Basri. "Probabilistic neural network for brain tumor classification." 2011 Second International Conference on Intelligent Systems, Modelling and Simulation. IEEE, 2011.
Aslam, Asra, Ekram Khan, and MM Sufyan Beg. "Improved Edge Detection Algorithm for Brain Tumor Segmentation." Procedia Computer Science 58 (2015): 430-437.
M. R. Udhaya, â€œBoundary Exposure using Intensity and Texture Gradient Features,â€ IOSR Journal of Computer Engineering (IOSRJCE), vol. 8, no. 1, pp. 28â€“33, 2012.
J. Anand and K. Sivachandar, â€œAn Edge Vector and Edge Map Based Boundary Detection in Medical Images,â€ vol. 1, no. 4, pp. 1050â€“1055, 2013.
G. Latif, S. B. Kazmi, M. A. Jaffar, and A. M. Mirza, â€œClassification and Segmentation of Brain Tumor Using Texture Analysis,â€ Recent Adv. Artif. Intell. Knowl. Eng. Data Bases, pp. 147â€“155, 2010.
S. Z. Oo and A. S. Khaing, â€œBrain Tumor Detection and Segmentation Using Watershed Segmentation and Morphological Operation,â€ IJRET Int. J. Res. Eng. Technol., vol. 3, pp. 367â€“374, 2014.
G. E. Sujji, Y. V. S. Lakshmi, and G. W. Jiji, â€œMRI Brain Image Segmentation based on Thresholding,â€ Int. J. Adv. Comput. Res., vol. 3, no. 8, pp. 1â€“5, 2013.
S. Mohane and M. Borse, â€œComparitive Study of Brain Tumor Detection Using,â€ pp. 422â€“428, 2015.
K. A. K. I. S. N. Alyaa H. Ali, â€œSegmentation of brain tumour using Enhanced Thresholding Algorithm and Calculatethe area of the tumour\n,â€ IOSR J. Res. Method Educ., vol. 4, no. 1, pp. 58â€“62, 2014.
S. Egmentation, D. L. Pham, C. Xu, and J. L. Prince, â€œCurrent Methods in Medical Image Segmentation,â€ 2000.
P. Tambe, â€œComparative Study of Segmentation Techniques for Brain Tumor Detection,â€ vol. 5, no. 2, pp. 269â€“271, 2016.
Gonzalez, Rafael C., and Richard E. Woods. "Digital image processing."Nueva Jersey (2008).
Y.-C. Sung, K.-S. Han, C.-J. Song, S.-M. Noh, and J.W. Park, â€œThreshold estimation for region segmentation on mr image of brain having the partial volume artifactâ€, in Signal Processing Proceedings, 2000. WCCC-ICSP 2000. 5th International Conference on, IEEE, 2000, vol. 2, pp. 1000-1009.
A. Stadlbauer, E. Moser, S. Gruber, R. Buslei, C. Nimsky, R. Fahlbusch, and O. Ganslandt, â€œImproved delineation of brain tumors: An automated method for segmentation based on pathologic changes of 1H-MRSI metabolites in gliomasâ€, vol. 23, no. 2, pp. 454-461, 2004.
M. R. Kaus, S. K. Warfield, A. Nabavi, P. M. Black, F. A. Jolesz, and R. Kikinis,â€Automated segmentation of mr images of brain tumorsâ€, vol. 218, no. 2, pp. 586-591, 2001.
Y. M. Salman, â€œModified technique for volumetric brain tumor measurements, Journal of Biomedical Science and Engineeringâ€ , vol. 2, p. 16, 2009.
R. Roslan, N. Jamil, and R. Mahmud, Skull stripping magnetic resonance images brain images: Region growing versus mathematical morphology, International Journal of Computer Information Systems and Industrial Management Applications, vol. 3, pp. 150-158, 2011
J. E. Cates, R. T. Whitaker, and G. M. Jones, Case study: An evaluation of user-assisted hierarchical watershed segmentation, Medical Image Analysis, vol. 9, no. 6, pp. 566-578, 2005.
J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, 1981.
N. Moon, E. Bullitt, K. Van Leemput, and G. Gerig, Automatic brain and tumor segmentation, in Medical Image Computing and Computer-Assisted Interventionâ€”MICCAI 2002. Springer, 2002, pp. 372-379.