Deep Perceptual Enhancement for Medical Image Analysis
- Authors
- Sharif, S.M.A.; Naqvi, R.A.; Biswas, M.; Loh, Woong-Kee
- Issue Date
- Oct-2022
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Convolution; deep learning; Image analysis; Image enhancement; image processing; Kernel; Medical diagnostic imaging; medical image analysis; Medical image enhancement; perceptual enhancement; Task analysis; Visualization
- Citation
- IEEE Journal of Biomedical and Health Informatics, v.26, no.10, pp.4826 - 4836
- Journal Title
- IEEE Journal of Biomedical and Health Informatics
- Volume
- 26
- Number
- 10
- Start Page
- 4826
- End Page
- 4836
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/85703
- DOI
- 10.1109/JBHI.2022.3168604
- ISSN
- 2168-2194
- Abstract
- Due to numerous hardware shortcomings, medical image acquisition devices are susceptible to producing low-quality (i.e., low contrast, inappropriate brightness, noisy, etc.) images. Regrettably, perceptually degraded images directly impact the diagnosis process and make the decision-making manoeuvre of medical practitioners notably complicated. This study proposes to enhance such low-quality images by incorporating end-to- end learning strategies for accelerating medical image analysis tasks. To the best concern, this is the first work in medical imaging which comprehensively tackles perceptual enhancement, including contrast correction, luminance correction, denoising, etc., with a fully convolutional deep network. The proposed network leverages residual blocks and a residual gating mechanism for diminishing visual artefacts and is guided by a multi-term objective function to perceive the perceptually plausible enhanced images. The practicability of the deep medical image enhancement method has been extensively investigated with sophisticated experiments. The experimental outcomes illustrate that the proposed method could outperform the existing enhancement methods for different medical image modalities by 5.00 to 7.00 dB in peak signal-to-noise ratio (PSNR) metrics and 4.00 to 6.00 in DeltaE metrics. Additionally, the proposed method can drastically improve the medical image analysis tasks performance and reveal the potentiality of such an enhancement method in real-world applications. IEEE
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - IT융합대학 > 소프트웨어학과 > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.