Abstract
Denoising is a fundamental requirement in the field of medical image processing. It is the process of reducing the additive noise from a noisy medical image, while preserving the information from the clinical data and the physiological signals. Therefore, it is essential to recover an estimated image that conveys almost the same information present in the original image. Wavelet shrink is a standard method of denoising the medical images due to its spectral decomposition and energy compression mechanism. The orthogonal decomposition and energy based compression of the image preserves its spectral components. However, break points are observed due to the presence of purely harmonic patches in the image. To solve this problem, we suggest a spline based fuzzy wavelet shrink (SFWShrink) model for denoising the medical images. The fuzzy wavelet shrink model assigns the wavelet coefficients using a fuzzy rule based membership value. The fuzzy membership values signify the relative importance of a data point with respect to its neighboring data points. This eliminates the noise points in the data space. Next, the spline estimation removes the break points occurring due to the wavelet transform. The benefits of the proposed method are: (i) it is a simple and effective method of denoising; (ii) it preserves the insignificant image details. The proposed model is evaluated with different modalities of synthetic medical images. It is compared with the standard wavelet shrink models, such as VisuShrink, BayesShrink and NeighShrink. The evaluation parameters show the superiority of the suggested technique in comparison to the other approaches.
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This work is supported by PhD scholarship grant under TEQIP-III, VSS University of Technology, Burla.
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Mishro, P.K., Agrawal, S., Panda, R. (2020). Medical Image Denoising Using Spline Based Fuzzy Wavelet Shrink Technique. In: Nain, N., Vipparthi, S., Raman, B. (eds) Computer Vision and Image Processing. CVIP 2019. Communications in Computer and Information Science, vol 1147. Springer, Singapore. https://doi.org/10.1007/978-981-15-4015-8_16
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DOI: https://doi.org/10.1007/978-981-15-4015-8_16
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