Abstract
CT presents images of cross-sectional slices of the body. The quality of CT images varies depending on penetrating X-rays in a different anatomically structures. Noise in CT is a multi-source problem and arises from the fundamentally statistical nature of photon production. This chapter presents an adaptive approach for noise reduction in sequences of CT images, based on the Wavelet Packet Decomposition and adaptive threshold of wavelet coefficients in the high frequency sub-bands of the shrinkage decomposition. Implementation results are given to demonstrate the visual quality and to analyze some objective estimation parameters such as PSNR, SNR, NRR, and Effectiveness of filtration in the perspective of clinical diagnosis.
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Acknowledgments
This chapter was supported by the Joint Research Project Bulgaria-Romania (2010–2012): “Electronic Health Records for the Next Generation Medical Decision Support in Romanian and Bulgarian National Healthcare Systems”, DNTS 02/19.
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Georgieva, V., Kountchev, R., Draganov, I. (2014). An Adaptive Approach for Noise Reduction in Sequences of CT Images. In: Iantovics, B., Kountchev, R. (eds) Advanced Intelligent Computational Technologies and Decision Support Systems. Studies in Computational Intelligence, vol 486. Springer, Cham. https://doi.org/10.1007/978-3-319-00467-9_4
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DOI: https://doi.org/10.1007/978-3-319-00467-9_4
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