An Optimal Segmentation Method for Processing Medical Image to Detect the Brain Tumor
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https://doi.org/10.15625/0868-3166/15938Keywords:
Medical image, brain tumor, segmentation, ITKAbstract
In the field of medical physics, detection of brain tumor from computed tomography (CT) or magnetic resonance (MRI) scans is a difficult task due to complexity of the brain hence it is one of the top priority goals of many recent researches. In this article, we describe a new method that combines four different steps including smoothing, Sobel edge detection, connected component, and finally region growing algorithms for locating and extracting the various lesions in the brain. The computational algorithm of the proposed method was implemented using Insight Toolkit (ITK). The analysis results indicate that the proposed method automatically and efficiently detected the tumor region from the CT or MRI image of the brain. It is very clear for physicians to separate the abnormal from the normal surrounding tissue to get a real identification of related areas; improving quality and accuracy of diagnosis, which would help to increase success possibility by early detection of tumor as well as reducing surgical planning time. This is an important step in correctly calculating the dose in radiation therapy later.
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Grau, V., Mewes, A.U., Alcaniz, M., Kikinis, R. & Warfield, S.K. Improved watershed transform for medical image segmentation using prior information. IEEE Trans Med Imaging 23, 447-458 (2004).
Biji C.L., S.D., Panicker A. . Tumor Detection in Brain Magnetic Resonance Images Using Modified Thresholding Techniques. in Advances in Computing and Communications, Vol. 193 300-308 (Springer, Berlin, Heidelberg, 2011).
Nandi, A. Detection of human brain tumour using MRI image segmentation and morphological operators. in 2015 IEEE International Conference on Computer Graphics, Vision and Information Security (CGVIS) 55-60 (Bhubaneswar, 2015).
Mustaqeem, A., Javed, A. & Fatima, T. An Efficient Brain Tumor Detection Algorithm Using Watershed & Thresholding Based Segmentation. International Journal of Image, Graphics and Signal Processing 4, 34-39 (2012).
Ng, H.P., Ong, S.H., Foong, K.W.C., Goh, P.S. & Nowinski, W.L. Medical Image Segmentation Using K-Means Clustering and Improved Watershed Algorithm. in 2006 IEEE Southwest Symposium on Image Analysis and Interpretation 61-65 (Denver, CO, USA, 2006).
M. Masroor Ahmed, D.B.M. Segmentation of Brain MR Images for Tumor Extraction by Combining Kmeans Clustering and Perona-Malik Anisotropic Diffusion Model. in International Journal of Image Processing Vol. 2 27-34 (2008).
Rachel A. Powsner, M.R.P., Edward R. Powsner. Essentials of Nuclear Medicine Physics and Instrumentation, (Wiley-Blackwell, 2013).
Lim, K.O. & Pfefferbaum, A. Segmentation of MR brain images into cerebrospinal fluid spaces, white and gray matter. J Comput Assist Tomogr 13, 588-593 (1989).
Beichel, R.R., et al. Semiautomated segmentation of head and neck cancers in 18F-FDG PET scans: A just-enough-interaction approach. Med Phys 43, 2948-2964 (2016).
Othman, N., Dorizzi, B. & Garcia-Salicetti, S. OSIRIS: An open source iris recognition software. Pattern Recogn Lett 82, 124-131 (2016).
Morris, T. Computer Vision and Image Processing, (Palgrave Macmillan Ltd, United Kingdom, 2004).
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