New Method for Automatic Detection of Brain Tumor in Multimodal Brain Magnetic Resonance Images
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Abstract
Brain tumor is a one of the severe life altering disease and analysis of brain imaging is a most important task of visualizing the brain inner anatomical structures, analyzing brain tumor and surgical planning. Magnetic Resonance Imaging is used to diagnose a variety of diseases in the brain and it is found to be much superior to other techniques especially for brain tissues. The main advantage is that
the soft tissue differentiation is extremely high for MRI. Image processing plays vital role in medical image analysis and Image segmentation is a most conman technique for analysis of MR imaging in many clinical applications. The parallel segmentation methods and techniques are expressed for the automatic detection of tumor in multimodal brain MR Image by existing state-of-art methods. However the specific results are not being projected and established in the similar researches. Hence, this proposed work tackles about automatic segmentation and detection of tumor in multimodal brain MR images. The main aim of the proposed work to achieve high segmentation accuracy and detection of tumor in the multimodal brain MR images and it was demonstrated in multimodal brain MR Images, viz. FLAIR MRI, T1 MRI, MRI and T2 MRI. The relative performance of the Proposed Method is demonstrated over existing methods using real brain MRI and open brain MRI data sets.
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References
Van N Porz, "Multi-modalodal glioblastoma segmentation: Man versus machine," PLOS ONE, 9, e96873, 2014.
Bauer, S., Wiest, R., Nolte, L.-P., & Reyes, M. (2013). A survey of MRI-based medical image analysis for brain tumor studies. Phys. Med. Biol., 58(13), R97-R129.
Weizman, L. (2012). Automatic segmentation, internal classification, and follow-up of optic pathway gliomas in MRI. Med. Image Anal., 16(1), 177-188.
Ahmed, S., Iftekharuddin, K. M., & Vossough, A. (2011). Efficacy of texture, shape, and intensity feature fusion for posterior-fossa tumor segmentation in MRI. IEEE Trans. Inf. Technol. Biomed., 15(2), 206-213.
Liu, J., Li, M., Wang, J., Wu, F., Liu, T., & Pan, Y. (2014). A Survey of MRI-Based Brain Tumor Segmentation Methods. TSINGHUA SCIENCE AND TECHNOLOGY, 19(6), December.
Roerdink, J. B. T. M., & Meijster, A. (2000). The watershed transform: Definitions, algorithms, and parallelization strategies. Fundamenta Informaticae, 41, 187–228.
Li, G. (2011). Improved watershed segmentation with optimal scale based on ordered dither halftone and mutual information. In Computer Science and Information Technology (ICCSIT), 2010, 3rd IEEE International Conference (pp. 296-300).
Benson, C. C., Deepa, V., Lajish, V. L., & Kumar Rajamani. (2016). Brain Tumor Segmentation from MR Brain Images using Improved Fuzzy c-Means Clustering and Watershed Algorithm. In Intl. Conference on Advances in Computing, Communications and Informatics (ICACCI), Sept. 21-24, 2016, Jaipur, India.
Szilágyi, L., Lefkovits, L., & Benyó, B. (2015). Automatic Brain Tumor Segmentation in Multispectral MRI Volumes Using a Fuzzy c-Means Cascade Algorithm. In 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).
Lin, G.-C., Wang, W.-J., Kang, C.-C., & Wang, C.-M. (2012). Multispectral MR images segmentation based on fuzzy knowledge and modified seeded region growing. Magnetic Resonance Imaging, 30(2), 230-246.
NageswaraReddy, P., Mohan Rao, C. P. V. N. J., Satyanarayana, Ch. (2016). Optimal Segmentation Framework for Detection of Brain Anomalies. I.J. Engineering and Manufacturing, 6, 26-37.