Abstract
Medical image processing plays a significant role in disease diagnosis and provides the details about tissues and organs. Image denoising techniques are used to restore the original information of the image and remove unwanted distortion. In this paper, denoising the clinical database image using discrete wavelet transform (DWT) is done and the performance is measured by considering the following quality metric parameters, namely peak signal-to-noise ratio, structural content, image fidelity, normalized correlation coefficient, structural similarity index, and universal quality index. We evaluate the performance of various wavelet filter coefficients for image denoising and which wavelet coefficient would be most reliable one on clinical dataset, and their efficiency is reported.
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Ganesan, K., Rajaguru, H. (2018). Performance Analysis of Wavelet Function Using Denoising for Clinical Database. In: Bhuvaneswari, M., Saxena, J. (eds) Intelligent and Efficient Electrical Systems. Lecture Notes in Electrical Engineering, vol 446. Springer, Singapore. https://doi.org/10.1007/978-981-10-4852-4_23
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DOI: https://doi.org/10.1007/978-981-10-4852-4_23
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