Abstract
The most demanded tool to identify diagnosis in medical science is computed Tomography (CT). Radiation dose acts as the major factor in the degradation of CT image quality, in terms of noise. Hence in curvelet domain, an inter-scale and intra-scale thresholding-based noisy CT image quality improvement is proposed. In the proposed scheme, inter- and intra-scale dependencies are applied parallel in high-frequency coefficients. These filtered high-frequency coefficients are analyzed by obtaining correlation values. Using correlation analysis, an aggregation is performed between both filtered high-frequency coefficients. Denoised image has been retrieved using inverse curvelet transform. Comparison of the proposed method with existing methods has been performed. The result analysis of the proposed method shows that its performance is better than existing ones concerning visual quality, peak signal-to-noise ratio (PSNR), and image quality index (IQI).
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Diwakar, M., Verma, A., Lamba, S., Gupta, H. (2019). Inter- and Intra-scale Dependencies-Based CT Image Denoising in Curvelet Domain. In: Ray, K., Sharma, T., Rawat, S., Saini, R., Bandyopadhyay, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 742. Springer, Singapore. https://doi.org/10.1007/978-981-13-0589-4_32
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DOI: https://doi.org/10.1007/978-981-13-0589-4_32
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