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Price: EUR 160.00Authors: Tai, Duong Thanh | Nhu, Nguyen Tan | Tuan, Pham Anh | Suleiman, Abdelmoneim | Omer, Hiba | Alirezaei, Zahra | Bradley, David | Chow, James C.L.
Article Type: Research Article
Abstract: BACKGROUND: Accurate diagnosis and subsequent delineated treatment planning require the experience of clinicians in the handling of their case numbers. However, applying deep learning in image processing is useful in creating tools that promise faster high-quality diagnoses, but the accuracy and precision of 3-D image processing from 2-D data may be limited by factors such as superposition of organs, distortion and magnification, and detection of new pathologies. The purpose of this research is to use radiomics and deep learning to develop a tool for lung cancer diagnosis. METHODS: This study applies radiomics and deep learning in the diagnosis …of lung cancer to help clinicians accurately analyze the images and thereby provide the appropriate treatment planning. 86 patients were recruited from Bach Mai Hospital, and 1012 patients were collected from an open-source database. First, deep learning has been applied in the process of segmentation by U-NET and cancer classification via the use of the DenseNet model. Second, the radiomics were applied for measuring and calculating diameter, surface area, and volume. Finally, the hardware also was designed by connecting between Arduino Nano and MFRC522 module for reading data from the tag. In addition, the displayed interface was created on a web platform using Python through Streamlit. RESULTS: The applied segmentation model yielded a validation loss of 0.498, a train loss of 0.27, a cancer classification validation loss of 0.78, and a training accuracy of 0.98. The outcomes of the diagnostic capabilities of lung cancer (recognition and classification of lung cancer from chest CT scans) were quite successful. CONCLUSIONS: The model provided means for storing and updating patients’ data directly on the interface which allowed the results to be readily available for the health care providers. The developed system will improve clinical communication and information exchange. Moreover, it can manage efforts by generating correlated and coherent summaries of cancer diagnoses. Show more
Keywords: Lung cancer, deep learning-based diagnosis, radiomics, computer-aided diagnosis
DOI: 10.3233/XST-230255
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Lu, Chang | Han, Zhenye | Zou, Jing
Article Type: Research Article
Abstract: BACKGROUND: Projection Domain Decomposition (PDD) is a dual energy reconstruction method which implements the decomposition process before image reconstruction. The advantage of PDD is that it can alleviate beam hardening artifacts and metal artifacts effectively as energy spectra estimation is considered in PDD. However, noise amplification occurs during the decomposition process, which significantly impacts the accuracy of effective atomic number and electron density. Therefore, effective noise reduction techniques are required in PDD. OBJECTIVE: This study aims to develop a new algorithm capable of minimizing noise while simultaneously preserving edges and fine details. METHODS: …In this study, a denoising algorithm based on low rank and similarity-based regularization (LRSBR) is presented. This algorithm incorporates the low-rank characteristic of tensors into similarity-based regularization (SBR) framework. This method effectively addresses the issue of instability in edge pixels within the SBR algorithm and enhances the structural consistency of dual-energy images. RESULTS: A series of simulation and practical experiments were conducted on a dual-layer dual-energy CT system. Experiments demonstrate that the proposed method outperforms exiting noise removal methods in Peak Signal-to-noise Ratio (PSNR), Root Mean Square Error (RMSE), and Structural Similarity (SSIM). Meanwhile, there has been a notable enhancement in the visual quality of CT images. CONCLUSIONS: The proposed algorithm has a significantly improved noise reduction compared to other competing approach in dual-energy CT. Meanwhile, the LRSBR method exhibits outstanding performance in preserving edges and fine structures, making it practical for PDD applications. Show more
Keywords: Dual-energy computed tomography, projection domain decomposition, Low rank, similarity-based regularization
DOI: 10.3233/XST-230248
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Shi, Liu | Wei, Cunfeng | Jia, Tong | Zhao, Yunsong | Liu, Baodong
Article Type: Research Article
Abstract: BACKGROUND: The rapid development of industrialization in printed circuit board (PCB) warrants more complexity and integrity, which entails an essential procedure of PCB inspection. X-ray computed laminography (CL) enables inspection of arbitrary regions for large-sized flat objects with high resolution. PCB inspection based on CL imaging is worthy of exploration. OBJECTIVE: This work aims to extract PCB circuit layer information based on CL imaging through image segmentation technique. METHODS: In this work, an effective and applicable segmentation model for PCB CL images is established for the first time. The model comprises two components, …with one integrating edge diffusion and l 0 smoothing to filter CL images with aliasing artifacts, and the other being the fuzzy energy-based active contour model driven by local pre-fitting energy to segment the filtered images. RESULT: The proposed model is able to suppress aliasing artifacts in the PCB CL images and has good performance on images of different circuit layers. CONCLUSIONS: Results of the simulation experiment reveal that the method is capable of accurate segmentation under ideal scanning condition. Testing of different PCBs and comparison of different segmentation methods authenticate the applicability and superiority of the model. Show more
Keywords: PCB, CL, image segmentation, active contour model
DOI: 10.3233/XST-240006
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Zhao, Wei | Liu, Yi | Linghu, Xinyao | Zhang, Pengcheng | Yan, Hongxu | Ding, Xiaxu | Wang, Xiang | Gui, Zhiguo | Chen, Yan
Article Type: Research Article
Abstract: BACKGROUND: Recently, X-rays have been widely used to detect complex structural workpieces. Due to the uneven thickness of the workpiece and the high dynamic range of the X-ray image itself, the detailed internal structure of the workpiece cannot be clearly displayed. OBJECTIVE: To solve this problem, we propose an image enhancement algorithm based on a multi-scale local edge-preserving filter. METHODS: Firstly, the global brightness of the image is enhanced through logarithmic transformation. Then, to enhance the local contrast, we propose utilizing the gradient decay function based on fuzzy entropy to process the gradient and then incorporate …the gradient into the energy function of the local edge-preserving filter (LEP) as a constraint term. Finally, multiple base layers and detail layers are obtained through filtering multi-scale decomposition. All detail layers are enhanced and fused using S-curve mapping to improve contrast further. RESULTS: This method is competitive in both quantitative indices and visual perception quality. CONCLUSIONS: The experimental results demonstrate that the proposed method significantly enhances various complex workpieces and is highly efficient. Show more
Keywords: X-ray images, local edge-preserving filter, local fuzzy entropy, gradient domain compression, S-curve mapping
DOI: 10.3233/XST-240045
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Cui, Wei | Lv, Haipeng | Wang, Jiping | Zheng, Yanyan | Wu, Zhongyi | Zhao, Hui | Zheng, Jian | Li, Ming
Article Type: Research Article
Abstract: BACKGROUND: Photon-counting computed tomography (Photon counting CT) utilizes photon-counting detectors to precisely count incident photons and measure their energy. These detectors, compared to traditional energy integration detectors, provide better image contrast and material differentiation. However, Photon counting CT tends to show more noticeable ring artifacts due to limited photon counts and detector response variations, unlike conventional spiral CT. OBJECTIVE: To comprehensively address this issue, we propose a novel feature shared multi-decoder network (FSMDN) that utilizes complementary learning to suppress ring artifacts in Photon counting CT images. METHODS: Specifically, we employ a feature-sharing encoder …to extract context and ring artifact features, facilitating effective feature sharing. These shared features are also independently processed by separate decoders dedicated to the context and ring artifact channels, working in parallel. Through complementary learning, this approach achieves superior performance in terms of artifact suppression while preserving tissue details. RESULTS: We conducted numerous experiments on Photon counting CT images with three-intensity ring artifacts. Both qualitative and quantitative results demonstrate that our network model performs exceptionally well in correcting ring artifacts at different levels while exhibiting superior stability and robustness compared to the comparison methods. CONCLUSIONS: In this paper, we have introduced a novel deep learning network designed to mitigate ring artifacts in Photon counting CT images. The results illustrate the viability and efficacy of our proposed network model as a new deep learning-based method for suppressing ring artifacts. Show more
Keywords: Photon counting CT, ring artifact suppression, feature shared multi-decoder network, complementary learning
DOI: 10.3233/XST-230396
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
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