Abstract
Meningioma is the one of the most common type of brain tumor, it as arises from the meninges and encloses the spine and the brain inside the skull. It accounts for 30% of all types of brain tumor. Meningioma’s can occur in many parts of the brain and accordingly it is named. In this paper, a mixture model based classification of meningioma brain tumor using MRI image is developed. The proposed method consists of four stages. In the first stage, with respect to the cells’ boundary, it is necessary to further processing, which ensures the boundary of some cells is a discrete region. Mathematical Morphology brings a fancy result during the discrete processing. Accurate cancer cell nucleus segmentation is necessary for automated cytological image analysis. Thresholding is a crucial step in segmentation..An adaptive binarization technique is an important step for medical image analysis.Finally, a novel hybrid Fuzzy SVM is designed in the classification stage meningioma brain tumor. The tumor classification results of proposed feature extraction with SVM is 74.24%, MM with FSVM is 82.67% and MM with RBF is 62.71% and our proposed method MM with Hybrid SVM is 91.64%.
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Ramsay, C. R., Matowe, L., Grilli, R., Grimshaw, J. M., and Thomas, R. E., Interrupted time series designs in health technology assessment: lessons from two systematic reviews of behavior change strategies. Int. J. Technol. Assess. Health Care 19(4):613–623, 2003.
Zikic, B., Glocker, E. K., Criminisi, A., Demiralp, C., Shotton, J., Thomas, O. M., Das, T., Jena, R., and Price, S. J., Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel. Journal of Medical Image Computing and Computer Assisted Intervention 7512:369–376, 2012.
Dhanasekaran, R., and Jayachandran, A., Severity analysis of brain tumor in MRI images uses modified multi-texton structure descriptor and kernel-SVM. Arab. J. Sci. Eng. 39(10):7073–7086, 2014.
Dubey, M. H., Gupta, S. K., and Gupta, S. K., Semi-automatic Segmentation of MRI Brain Tumor. Journal of Graphics, Vision and Image Processing. 9:33–40, 2009.
Kromer, C., Xu, J., Ostrom, Q. T. et al., Estimating the annual frequency of synchronous brain metastasis in the United States 2010-2013: a population-based study. J. Neuro-Oncol. 134(1):55–64, 2017.
Posner, J. B., and Chernik, N. L., Intracranial metastases from systemic cancer. Adv. Neurol. 19:579–592, 1978.
Vishvaksenan, K. S., Mithra, K., Kalidoss, R., and Karthipan, R., Experimental study on Elliot wave theory for Handoff Prediction. Fluctuation and Noise Letters 15(4):1–11, 2016.
Taheri, S., Ong, S. H., and Chong, V. F. H., Level-set segmentation of brain tumors using a threshold-based speed function. J. Image Vision Comput. 28:26–37, 2010.
DeAngelis, L. M., and Posner, J. B., Neurologic Complications of Cancer. 2nd edition. New York: Oxford University Press, 2009.
Silberstein, S. D., Practice parameter: evidence-based guidelines for migraine headache (an evidence-based review): report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology 55(6):754–762, 2000.
Krumholz, A., Wiebe, S., Gronseth, G. et al., Practice Parameter: evaluat- ing an apparent unprovoked first seizure in adults (an evidence-based review): report of the Quality Standards Subcommittee of the Ameri- can Academy of Neurology and the American Epilepsy Society. Neurology 69(21):1996–2007, 2007.
Jayachandran, A., and Dhanasekaran, R., Brain tumor severity analysis using modified multi-texton histogram and hybrid kernel SVM. Int. J. Imaging Syst. Technol. 24(1):72–82, 2014.
Glantz, M. J., Cole, B. F., Glantz, L. K. et al., Cerebrospinal fluid cytology in patients with cancer: minimizing false-negative results. Cancer 82(4):733–739, 1998.
Del Principe, M. I., Buccisano, F., Cefalo, M. et al., High sensitivity of flow cytometry improves detection of occult leptomeningeal disease in acute lymphoblastic leukemia and lymphoblastic lymphoma. Ann. Hematol. 93(9):1509–1513, 2014.
Abdel-Maksoud, E., Elmogy, M., and Al-Awadi, R., Brain tumor segmentation based on a hybrid clustering technique. Egypt. Informatics J. 16:71–81, 2015.
Jayachandran, A., and Dhanasekaran, R., Automatic detection of brain tumor in magnetic resonance images using multi-texton histogram and support vector machine. Int. J. Imaging Syst. Technol. 23:97–103, 2013.
Jayachandran, A., and Dhanasekaran, R., Abnormality segmentation and Classification of multi model brain tumor in MR images using Fuzzy based hybrid kernel SVM. International Journal of Fuzzy Systems 17(3):434–443, 2015.
Patchell, R. A., Tibbs, P. A., Walsh, J. W. et al., A randomized trial of surgery in the treatment of single metastases to the brain. N. Engl. J. Med. 322(8):494–500, 1990.
Hariharan, G., and Jayachandran, A., Color, textures and shape descriptor based cervical cancer classification system of pap smear images. J. Comput. Theor. Nanosci. 14(7):3609–3614, 2017.
Vishvaksenan, K. S., Kalaiarasan, R., Kalidoss, R., and Karthipan, R., Real time experimental study and analysis of Elliott wave theory in signal strength prediction. Proceedings of National Academy of Sciences, Springer 88(1):107–119, 2018.
Luts, J., Laudadio, T., Idema, A. J., Simonetti, A. W., Heerschap, A., Vandermeulen, D., Suykens, J. A. K., and Huffel, S. V., Nosologic imaging: segmentation and classification using MRI and MRSI. Journal of NMR in Biomedicine 22:374–390, 2009.
Borgelt, B., Gelber, R., Kramer, S. et al., The palliation of brain metasta- ses: final results of the first two studies by the Radiation Therapy Oncology Group. Int J RadiatOncolBiol Phys. 6(1):1–9, 1980.
Jayachandran, A., and Dhanasekaran, R., Multi Class Brain Tumor Classification Of MRI Images using Hybrid Structure Descriptor and Fuzzy Logic Based RBF Kernel SVM. Iranian Journal of Fuzzy system 14(3):41–54, 2017.
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Arokia Jesu Prabhu, L., Jayachandran, A. Mixture Model Segmentation System for Parasagittal Meningioma brain Tumor Classification based on Hybrid Feature Vector. J Med Syst 42, 251 (2018). https://doi.org/10.1007/s10916-018-1094-3
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DOI: https://doi.org/10.1007/s10916-018-1094-3