Abstract
Recent advancement in biomedical image processing area has been augmented by big data research and machine learning techniques. It is observed that image segmentation is contributing significantly in this domain. In this study, an efficient method for segmenting magnetic resonance (MR) images is proposed. The strategy for the method developed here is as follows. First, the MR images are preprocessed through a vector median filter to mitigate the noise inherent in images. Next, Otsu thresholding is implemented for initial image segmentation which detects the homogenous regions of the MR image. Finally, a modified suppression factor based suppressed fuzzy c-means is implemented for segmentation. For computational evaluation, the metrics such as signal-to-noise ratio (SNR), mean square error (MSE), and the peak signal-to-noise ratio (PSNR) are considered in this study. The proposed method shows better results over other algorithms by considering the above metrics.
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Acknowledgement
We acknowledge the authors of the paper [22], for their valuable suggestions while performing this study. Also, the idea helped us to obtain better results by implementing our modified suppression factor and it can be carried out to a bigger solution in biomedical image processing research.
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Ahmed, F., Chowdhury, T.U.A., Furhad, M.H. (2020). A Fuzzy-Based Study for Biomedical Imaging Applications. In: Uddin, M., Bansal, J. (eds) Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-7564-4_6
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DOI: https://doi.org/10.1007/978-981-13-7564-4_6
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