TY - GEN
T1 - U-Net Autoencoder for Edge-Preserved Denoising of Low Dose Computed Tomography Images
T2 - 13th International Conference on Information Technology in Asia, CITA 2023
AU - Zubair, Muhammad
AU - Md Rais, Helmi B.
AU - Al-Tashi, Qasem
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Computed Tomography (CT) imaging has become a commonly used technique in healthcare to identify irregularities in the human body. However, CT scans involve exposure to electromagnetic radiation, which can pose health risks to patients. To address this, Low-Dose CT has been introduced, but it results in degraded image quality, including increased noise, artifacts, and loss of edge and feature contrast. This can limit the effectiveness of Computer-Aided Diagnosis systems. Denoising and preserving edge sharpness in Low-Dose CT images is a challenging task that conventional denoising techniques may not efficiently solve. Deep learning-based methods have emerged as a potential solution to this problem. This study proposes a new unsupervised LDCT image denoising algorithm called DEPnet (Denoise and Edge Preserve), which utilises a U-Net-based autoencoder with hybrid dilated convolution and batch normalization layers. The proposed algorithm has been evaluated on the KiTS19 Low-Dose CT Grand Challenge dataset and compared with other models such as Q-AE, Msaru-Net, and CT-ReCNN. The results demonstrate that DEPnet effectively reduces noise in LDCT images and preserves fine details, making it a promising solution for denoising Low-Dose CT images.
AB - Computed Tomography (CT) imaging has become a commonly used technique in healthcare to identify irregularities in the human body. However, CT scans involve exposure to electromagnetic radiation, which can pose health risks to patients. To address this, Low-Dose CT has been introduced, but it results in degraded image quality, including increased noise, artifacts, and loss of edge and feature contrast. This can limit the effectiveness of Computer-Aided Diagnosis systems. Denoising and preserving edge sharpness in Low-Dose CT images is a challenging task that conventional denoising techniques may not efficiently solve. Deep learning-based methods have emerged as a potential solution to this problem. This study proposes a new unsupervised LDCT image denoising algorithm called DEPnet (Denoise and Edge Preserve), which utilises a U-Net-based autoencoder with hybrid dilated convolution and batch normalization layers. The proposed algorithm has been evaluated on the KiTS19 Low-Dose CT Grand Challenge dataset and compared with other models such as Q-AE, Msaru-Net, and CT-ReCNN. The results demonstrate that DEPnet effectively reduces noise in LDCT images and preserves fine details, making it a promising solution for denoising Low-Dose CT images.
KW - deep learning
KW - image enhancement
KW - LDCT image denoising
KW - Noise removal techniques
UR - http://www.scopus.com/inward/record.url?scp=85174157396&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85174157396&partnerID=8YFLogxK
U2 - 10.1109/CITA58204.2023.10262803
DO - 10.1109/CITA58204.2023.10262803
M3 - Conference contribution
AN - SCOPUS:85174157396
T3 - 2023 13th International Conference on Information Technology in Asia, CITA 2023
SP - 19
EP - 24
BT - 2023 13th International Conference on Information Technology in Asia, CITA 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 3 August 2023 through 4 August 2023
ER -