eprintid: 19112
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/91/12
datestamp: 2024-06-04 14:11:33
lastmod: 2024-06-04 14:11:34
status_changed: 2024-06-04 14:04:53
type: conference_item
metadata_visibility: show
creators_name: Zubair, M.
creators_name: Md Rais, H.B.
creators_name: Al-Tashi, Q.
title: U-Net Autoencoder for Edge-Preserved Denoising of Low Dose Computed Tomography Images: A Novel Technique
ispublished: pub
keywords: Computer aided diagnosis; Computerized tomography; Deep learning; Electromagnetic waves; Image denoising; Image enhancement; Learning systems, Auto encoders; Computed tomography images; De-noising; Deep learning; Dose computed tomographies; Edge-preserved; LDCT image denoising; Low dose; Noise removal technique; Noises removal, Health risks
note: cited By 2; Conference of 13th International Conference on Information Technology in Asia, CITA 2023 ; Conference Date: 3 August 2023 Through 4 August 2023; Conference Code:193023
abstract: 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. © 2023 IEEE.
date: 2023
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174157396&doi=10.1109%2fCITA58204.2023.10262803&partnerID=40&md5=0a37ca4b32bfd4b5f5ae7772201703e3
id_number: 10.1109/CITA58204.2023.10262803
full_text_status: none
publication: 2023 13th International Conference on Information Technology in Asia, CITA 2023
pagerange: 19-24
refereed: TRUE
isbn: 9798350326208
citation:   Zubair, M. and Md Rais, H.B. and Al-Tashi, Q.  (2023) U-Net Autoencoder for Edge-Preserved Denoising of Low Dose Computed Tomography Images: A Novel Technique.  In: UNSPECIFIED.