Transactions on Machine Intelligence

Transactions on Machine Intelligence

Noise Reduction in CT Scan Images Using Wavelet Transform and Fuzzy Logic

Document Type : Original Article

Authors
D. Kargaran 1
P. Moallem 2
M. Hashemi 3
1 Department of Electrical Engineering, Payam Higher Education Institute, Golpayegan, Iran
2 Assistant Professor, Department of Electrical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran
3 Lecturer, Department of Electrical Engineering, Payam Higher Education Institute, Golpayegan, Iran
10.47176/TMI.2021.118
Abstract
Images are often contaminated with noise due to sensor errors or during transmission. Noise in an image reduces the effectiveness of subsequent image processing operations, such as edge detection, segmentation, and object recognition. The use of an appropriate filter to reduce or eliminate noise in medical images shortens imaging time and consequently minimizes the exposure of patients to X-ray radiation. In the medical field, this improvement can enhance a physician’s analysis of captured images, thereby reducing medical errors. In conventional noise removal methods using wavelet coefficient thresholding, the threshold value is uniformly applied to all image coefficients, leading to abrupt changes in values close to the threshold and causing image distortions. To address this issue, fuzzy logic is employed to achieve more effective noise reduction, overcoming the shortcomings of traditional thresholding methods and ensuring that the resulting image is not only quantitatively enhanced but also subjectively satisfactory. Due to its ability to model uncertain and approximate values, fuzzy logic can be effectively utilized for image denoising. By accepting approximation in data processing, fuzzy logic often provides more suitable behavior in many cases. This study aims to introduce an innovative approach for noise removal from CT scan images with the highest precision and sensitivity, considering evaluation criteria such as Peak Signal-to-Noise Ratio (PSNR) and Signal-to-Noise Ratio (SNR), among others. The proposed method integrates wavelet transform and fuzzy logic to establish a novel approach to noise reduction, overcoming existing challenges in this domain through fuzzy logic rules.
Keywords
CT Scan Image Noise Removal
Wavelet Transform
Wavelet Thresholding
Fuzzy Logic
Signal-To-Noise Ratio
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Transactions on Machine Intelligence
Volume 4, Issue 3
Summer 2021
Pages 118-127

  • Receive Date 12 June 2021
  • Revise Date 12 July 2021
  • Accept Date 11 September 2021