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Dental image enhanc...
Dental image enhancement network for early diagnosis of oral dental disease
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- Khan, Rizwan (författare)
- Department of Computer Science and Mathematics, Zhejiang Normal University, Jinhua, 321004, Zhejiang, China, Zhejiang
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- Akbar, Saeed (författare)
- School of Computer Science, Huazhong University of Science and Technology, Wuhan, China
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- Khan, Ali (författare)
- Department of Computer Science and Mathematics, Zhejiang Normal University, Jinhua, 321004, Zhejiang, China, Zhejiang
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- Marwan, Muhammad (författare)
- Department of Computer Science and Mathematics, Zhejiang Normal University, Jinhua, 321004, Zhejiang, China, Zhejiang
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- Qaisar, Zahid Hussain (författare)
- School of Computer Science, Huazhong University of Science and Technology, Wuhan, China
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- Mehmood, Atif (författare)
- KTH,Medicinsk avbildning,Department of Computer Science, National University of Modern Language, NUML, Islamabad, Pakistan
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- Shahid, Farah (författare)
- Department of Computer Science, University of Agriculture, Sub-Campus (Burewala-Vehari), Faisalabad, Punjab, Pakistan, Sub-Campus (Burewala-Vehari), Punjab
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- Munir, Khushboo (författare)
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
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- Zheng, Zhonglong (författare)
- Department of Computer Science and Mathematics, Zhejiang Normal University, Jinhua, 321004, Zhejiang, China, Zhejiang; Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, 321004, Zhejiang, China, Zhejiang
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(creator_code:org_t)
- 2023-03-31
- 2023
- Engelska.
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 13:1
- Relaterad länk:
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Intelligent robotics and expert system applications in dentistry suffer from identification and detection problems due to the non-uniform brightness and low contrast in the captured images. Moreover, during the diagnostic process, exposure of sensitive facial parts to ionizing radiations (e.g., X-Rays) has several disadvantages and provides a limited angle for the view of vision. Capturing high-quality medical images with advanced digital devices is challenging, and processing these images distorts the contrast and visual quality. It curtails the performance of potential intelligent and expert systems and disincentives the early diagnosis of oral and dental diseases. The traditional enhancement methods are designed for specific conditions, and network-based methods rely on large-scale datasets with limited adaptability towards varying conditions. This paper proposed a novel and adaptive dental image enhancement strategy based on a small dataset and proposed a paired branch Denticle-Edification network (Ded-Net). The input dental images are decomposed into reflection and illumination in a multilayer Denticle network (De-Net). The subsequent enhancement operations are performed to remove the hidden degradation of reflection and illumination. The adaptive illumination consistency is maintained through the Edification network (Ed-Net). The network is regularized following the decomposition congruity of the input data and provides user-specific freedom of adaptability towards desired contrast levels. The experimental results demonstrate that the proposed method improves visibility and contrast and preserves the edges and boundaries of the low-contrast input images. It proves that the proposed method is suitable for intelligent and expert system applications for future dental imaging.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
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- ref (ämneskategori)
- art (ämneskategori)
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