Abstract
Diagnosing the brain tumor from Magnetic Resonance Imaging (MRI) in Computer-Aided Diagnosis (CAD) applications is one of the challenging task in medical image processing. Traditionally many segmentation methods are used to address this issue. This paper introduces a segmentation method along with image fusion. Here a Discrete Wavelet Transform (DWT) method is chosen, for image fusion followed by segmentation using Support Vector Machine (SVM) for detecting the abnormality region. The types of MRI images considered here include T1-weighted (T1-w), T2-weighted (T2-w) and FLAIR images. The various fusion combinations are T1-w and T2-w, T1-w and FLAIR, T2-w and FLAIR. Experimental results suggest that on an average, fusion-based segmented result is superior to non-fusion-based segmented result.
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Acknowledgements
The website link for BRATS image database is https://www.smir.ch/BRATS/Start2013. This data set was supported for my doctoral degree purpose only. We have no conflict of interest with regard to the work presented. Ethical approval to conduct this study was obtained for my research work. Informed consent was obtained from all individual participants in the study.
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Deepa, B., Sumithra, M.G., Chandran, V., Gnanaprakash, V. (2019). Fusion-Based Segmentation Technique for Improving the Diagnosis of MRI Brain Tumor in CAD Applications. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_31
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DOI: https://doi.org/10.1007/978-3-030-00665-5_31
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