Abstract
Computer tomography (CT) is mostly necessary for identifying the core of any medical condition or ailment. The total number of X-rays affects how well the CT scans turn out. When the X-ray dose is higher, the precision of the CT pictures is comparatively higher, but the patient's health may suffer as a result. More drawn CT pictures are noisy as a result of the significant cause of statistical uncertainty in all physical measures. If the noise in low draught CT pictures can be reduced or eliminated, then it should be able to boost their effectiveness without raising the draught. Consequently, this research uses an adaptive Particle swam Optimization Algorithm to extract the fittest threshold value, contourlet and NLM filter are also used in our algorithm. The denoising technique is employed to safeguard the edges and get rid of noise. The proposed methodology's results are analysed and contrasted using certain established methods. According to the differentiated outcome analysis, the Proposed Methodology’s execution is finer and more acceptable to the existing procedures in terms of optical standard PSNR, SSIM, and Entropy Difference (ED).
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Sehgal, R., Kaushik, V.D. (2023). An Adaptive Whale Optimization Algorithm-Based CT Image Denoising in Wavelet Domain. In: Swaroop, A., Kansal, V., Fortino, G., Hassanien, A.E. (eds) Proceedings of Fourth Doctoral Symposium on Computational Intelligence . DoSCI 2023. Lecture Notes in Networks and Systems, vol 726. Springer, Singapore. https://doi.org/10.1007/978-981-99-3716-5_30
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DOI: https://doi.org/10.1007/978-981-99-3716-5_30
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