An Algorithm of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:msub><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math>-Norm and<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:msub><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:math>-Norm Regularization Algorithm for CT Image Reconstruction from Limited Projection

Li, Xiezhang; Feng, Guocan; Zhu, Jiehua

doi:10.1155/2020/8873865

Cited by 4 publications

(5 citation statements)

References 23 publications

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“…In the GA calculation, in order to make the algorithm retain the individual's good genes, the adaptive GA can be used to adjust the traditional GA adaptive degree problem. Let the crossover operator be P c , if they need to be adjusted automatically with the fitness, there is formula (5):…”

Section: Ct Image Segmentation Methods Based On Image Segmentationmentioning

confidence: 99%

“…Li et al has found that norm regularization has attracted attention in computerized CT scan image reconstruction. CT image gradients have provided a measure of image gradient sparsity for reconstructing images from finite projection data in CT and have effectively addressed optimization problems [ 5 ]. Scholars have seen that CT images are not only used in the medical field, but also used in various fields to a certain extent, which also shows that CT images have made great progress.…”

Section: Related Workmentioning

confidence: 99%

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Implementation of CT Image Segmentation Based on an Image Segmentation Algorithm

Shen

2022

Applied Bionics and Biomechanics

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With the increasingly important role of image segmentation in the field of computed tomography (CT) image segmentation, the requirements for image segmentation technology in related industries are constantly improving. When the hardware resources can fully meet the needs of the fast and high-precision image segmentation program system, the main means of how to improve the image segmentation effect is to improve the related algorithms. Therefore, this study has proposed a combination of genetic algorithm (GA) and Great Law (OTSU) algorithm to form an image segmentation algorithm-immune genetic algorithm (IGA) algorithm. The algorithm has improved the segmentation accuracy and efficiency of the original algorithm, which is beneficial to the more accurate results of CT image segmentation. The experimental results in this study have shown that the operating efficiency of the OTSU segmentation algorithm is up to 75%. The operating efficiency of the GA algorithm is up to 78%. The operating efficiency of the IGA algorithm is up to 92%. In terms of operating efficiency, the OTSU segmentation algorithm has more advantages. In terms of segmentation accuracy, the highest accuracy rate of OTSU segmentation algorithm is 45%. The accuracy of the GA algorithm is 80%. The highest accuracy of the IGA algorithm is 97%. The IGA algorithm is more powerful in terms of operating efficiency and accuracy. Therefore, the application of the IGA algorithm to CT image segmentation is beneficial to doctors to better judge the lesions and improve the diagnosis rate.

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Section: Ct Image Segmentation Methods Based On Image Segmentationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Implementation of CT Image Segmentation Based on an Image Segmentation Algorithm

Shen

2022

Applied Bionics and Biomechanics

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“…If Np, the quantitative parameters V i, i=1,Np , are present in the experimental design, the norm L p is defined by Relation (5) [63,81,82]:…”

Section: P Methodsmentioning

confidence: 99%

Evaluation of In Vitro Wound-Healing Potential, Antioxidant Capacity, and Antimicrobial Activity of Stellaria media (L.) Vill

et al. 2021

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The healing of skin wounds remains an important concern in medicine, especially in chronic wounds caused by various diseases such as diabetes. Using herbs or herbal products to heal skin wounds is a therapeutic challenge for traditional medicine. In this context, the main aim of our work was to highlight the in vitro healing potential of Stellaria media (L.) Vill. (SM) extract using the scratch assay on normal human dermal fibroblasts (NHDF). The ability to stimulate cell migration and proliferation under the influence of different concentrations of SM extract (range between 12.5 and 200 µg/mL) was determined compared to the control (untreated in vitro-simulated wound) and positive control (allantoin 50 µg/mL). Our results showed that the concentration of 100 µg/mL SM extract applied on the simulated wound recorded the strongest and fastest (24 h) migration (with wound closure) and proliferation of NHDF compared with the control. In addition, the SM extract was characterized in terms of bioactive compounds (total phenols and flavonoids content), antioxidant capacity (FRAP (The Ferric-Reducing Antioxidant Power) assay and electrochemical method), and antimicrobial activity. The results show that the SM extract contains a considerable amount of polyphenols (17.19 ± 1.32 mg GAE/g dw and 7.28 ± 1.18 mg QE/g dw for total phenol and flavonoid content, respectively) with antioxidant capacity. Antimicrobial activity against Gram-positive bacteria (S. aureus) is higher than E. coli at a dose of 15 µg/mL. This study showed that Stellaria media is a source of polyphenols compounds with antioxidant capacity, and for the first time, its wound healing potential was emphasized.

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“…Li, X. used the transfer learning method to extract CT image features, recognize epidemic, modify the typical inception network, and use the pre training weight to fine tune the m-inception model. The experimental results show that the deep learning method is very valuable for extracting radiation image features 9 . Li, Z. based on the v-net model, using the bottleneck structure, obtained a lightweight 3D convolutional neural network VB net model, which automatically and quantitatively analyzed the volume and density of the infected area in the CT image of epidemic 10 .…”

Section: Related Workmentioning

confidence: 99%

Image classification and discrimination of COVID pneumonia based on convolutional neural network

Qin,

Liu,

Che

et al. 2023

Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023)

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To address the negative quality of RT-PCR and the time taken to obtain results, this paper reports on the classification and discrimination of COVID pneumonia images by developing a neural network adaptively. Given the limited number of published COVID-19 CT data currently available, we suggest using the proposed negative process to refine the data to obtain more CT data to reduce matching risk; Experimental results show that compared to network models such as AlexNet and GoogleNet, the proposed BUF-Net network model has the best performance with an accuracy of 93%. Visualization of systemic findings using Grad-CAM techniques may further elucidate the critical role of CT imaging in the diagnosis of COVID-19.Conclusion: The use of deep learning tools in clinical practice can help radiologists make better diagnoses.

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An Algorithm ofl1-Norm andl0-Norm Regularization Algorithm for CT Image Reconstruction from Limited Projection

Cited by 4 publications

References 23 publications

Implementation of CT Image Segmentation Based on an Image Segmentation Algorithm

Implementation of CT Image Segmentation Based on an Image Segmentation Algorithm

Evaluation of In Vitro Wound-Healing Potential, Antioxidant Capacity, and Antimicrobial Activity of Stellaria media (L.) Vill

Image classification and discrimination of COVID pneumonia based on convolutional neural network

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