Integrated 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is widely performed for staging solitary pulmonary nodules (SPNs). However, the diagnostic efficacy ...of SPNs based on PET/CT is not optimal. Here, we propose a method of detection based on PET/CT that can differentiate malignant and benign SPNs with few false-positives.
Our proposed method combines the features of positron-emission tomography (PET) and computed tomography (CT). A dynamic threshold segmentation method was used to identify lung parenchyma in CT images and suspicious areas in PET images. Then, an improved watershed method was used to mark suspicious areas on the CT image. Next, the support vector machine (SVM) method was used to classify SPNs based on textural features of CT images and metabolic features of PET images to validate the proposed method.
Our proposed method was more efficient than traditional methods and methods based on the CT or PET features alone (sensitivity 95.6%; average of 2.9 false positives per scan).
Astragalus is a medicinal herb used in China for the prevention and treatment of diseases such as diabetes and cancer. As one of the main active ingredients of astragalus, Astragaloside IV (AS-IV) ...has a wide range of pharmacological effects, including anti-inflammation and anti-cancer effects.
Different phosphorylated forms of Smad3 differentially regulate the progression of hepatic carcinoma. The phosphorylation of the COOH-terminal of Smad3 (pSmad3C) and activation of the Nrf2/HO-1 pathway inhibits hepatic carcinoma, while phosphorylation of the linker region of Smad3 (pSmad3L) promotes progression. Thus, pSmad3C/3L and Nrf2/HO-1 pathways are potential targets for drug of anti-cancer development. AS-IV is anti-apoptotic and can inhibit hepatocellular carcinoma cell (HCC) proliferation, invasion, and tumor growth in nude mice. However, it is not clear whether AS-IV has a therapeutic effect on inhibiting the progression of primary liver cancer by regulating the pSmad3C/3L and Nrf2/HO-1 pathway. The purpose of this study is to investigate whether AS-IV inhibits hepatocellular carcinoma by regulating pSmad3C/3L and Nrf2/HO-1 pathway.
primary liver cancer in mice induced by DEN/CCl4/C2H5OH (DCC) and HSC-T6/HepG2 cell models activated by TGF-β1 was investigated for the mechanisms of AS-IV. In vivo assays included liver biopsy, histopathology and post-mortem analysis included immunohistochemistry, immunofluorescent, and Western blotting analysis, and in vitro assays included immunofluorescent, and Western blotting analysis.
AS-IV significantly inhibited the development of primary liver cancer, reflecting improved liver biopsy, histopathology. The incidence and multiplicity of primary liver cancer were markedly decreased by AS-IV treatment at the 20th week. AS-IV had observable effects on the TGF-β1/Smad and Nrf2/HO-1 expression in vivo, especially up-regulated pSmad3C, pNrf2, HO-1, and NQO1, while it down-regulated pSmad2C, pSmad2L, pSmad3L, PAI-1, and α-SMA at the 12th week and the 20th week. Furthermore, in vitro analysis further confirmed that AS-IV regulated the expression of pSmad3C/3L and Nrf2/HO-1 pathway in HSC-T6 and HepG2 cells activated by TGF-β1.
AS-IV administration delays the occurrence of primary liver cancer by continually suppressing the development of fibrosis, the mechanism of the therapeutic effect involving the regulation of the pSmad3C/3L and Nrf2/HO-1 pathways, especially in regulation reversibility and antagonism of pSmad3C and pSmad3L and promoting the phosphorylation of Nrf2.
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•AS-IV could decrease the incidence and multiplicity of hepatocellular carcinoma by suppressing the development of fibrosis.•AS-IV could inhibit hepatocellular carcinoma by regulating the expression of pSmad3C and pSmad3L.•AS-IV could inhibit hepatocellular carcinoma by promoting Nrf2/HO-1 pathway.
The images that are captured in sand storms often suffer from low contrast and serious color cast that are caused by sand dust, and these issues will have significant negative effects on the ...performance of an outdoor computer vision system. To address these problems, a method based on halo-reduced dark channel prior (DCP) dehazing for sand dust image enhancement is proposed in this paper. It includes three components in sequence: color correction in the LAB color space based on gray world theory, dust removal using a halo-reduced DCP dehazing method, and contrast stretching in the LAB color space using a Gamma function improved contrast limited adaptive histogram equalization (CLAHE), in which a guided filter is used to improve the artifacts of the histogram equalization. Experiments on a large number of real sand dust images demonstrate that the proposed method can well remove the overall yellowing tone and dust haze effect and obtain normal visual colors and a detailed clear image.
The soybean aphid Aphis glycines Matsumura (Hemiptera: Aphididae) is a primary pest of soybeans and poses a serious threat to soybean production. Our studies were conducted to understand the effects ...of different concentrations of insecticides (imidacloprid and thiamethoxam) on A. glycines and provided critical information for its effective management. Here, we found that the mean generation time and adult and total pre-nymphiposition periods of the LC50 imidacloprid- and thiamethoxam-treatment groups were significantly longer than those of the control group, although the adult pre-nymphiposition period in LC30 imidacloprid and thiamethoxam treatment groups was significantly shorter than that of the control group. Additionally, the mean fecundity per female adult, net reproductive rate, intrinsic rate of increase, and finite rate of increase of the LC30 imidacloprid-treatment group were significantly lower than those of the control group and higher than those of the LC50 imidacloprid-treatment group (P < 0.05). Moreover, both insecticides exerted stress effects on A. glycines, and specimens treated with the two insecticides at the LC50 showed a significant decrease in their growth rates relative to those treated with the insecticides at LC30. These results provide a reference for exploring the effects of imidacloprid and thiamethoxam on A. glycines population dynamics in the field and offer insight to agricultural producers on the potential of low-lethal concentrations of insecticides to stimulate insect reproduction during insecticide application.
Image haze removal is highly desired for the application of computer vision. This study proposes a novel context-guided generative adversarial network (CGGAN) for single image dehazing. Of which, a ...novel new encoder–decoder is employed as the generator. In addition, it consists of a feature-extraction net, a context-extraction net, and a fusion net in sequence. The feature-extraction net acts as an encoder, and is used for extracting haze features. The content-extraction net is a multi-scale parallel pyramid decoder and is used for extracting the deep features of the encoder and generating coarse dehazing image. The fusion net is a decoder and is used for obtaining the final haze-free image. In order to get better dehazing results, multi-scale information obtained during the decoding process of the context extraction decoder is used for guiding the fusion decoder. By introducing an extra coarse decoder to the original encoder–decoder, the CGGAN can make better use of the deep feature information extracted by the encoder. To ensure that the proposed CGGAN works effectively for different haze scenarios, different loss functions are employed for the two decoders. Experiments results show the advantage and the effectiveness of the proposed CGGAN, evidential improvements over existing state-of-the-art methods are obtained.
Underwater images usually suffer from colour distortion, blur, and low contrast, which hinder the subsequent processing of underwater information. To address these problems, this paper proposes a ...novel approach for single underwater images enhancement by integrating data‐driven deep learning and hand‐crafted image enhancement techniques. First, a statistical analysis is made on the average deviation of each channel of input underwater images to that of its corresponding ground truths, and it is found that both the red channel and the green channel of an underwater image contribute to its colour distortion. Concretely, the red channel of an underwater image is usually seriously attenuated, and the green channel is usually over strengthened. Motivated by such an observation, an attention mechanism guided residual module for underwater image colour correction is proposed, where the colour of the red channel of the underwater image and that of the green channel is compensated in a different way, respectively. Coupled with an attention mechanism, the residual module can adaptively extract and integrate the most discriminative features for colour correction. For scene contrast enhancement and scene deblurring, the traditional image enhancement techniques such as CLAHE (contrast limited adaptive histogram equalization) and Gamma correction are coupled with a multi‐scale convolutional neural network (MSCNN), where CLAHE and Gamma correction are used as complement to deal with the complex and changeable underwater imaging environment. Experiments on synthetic and real underwater images demonstrate that the proposed method performs favourably against the state‐of‐the‐art underwater image enhancement methods.
In rainy conditions, especially at night with low illumination, the visual of images obtained by outdoor computer vision systems is degraded significantly, leading to a significant negative effect on ...the work of the outdoor computer vision system. In this paper, we develop a new rainy image model to describe rain scenes at night with low illumination. From this model, we propose a joint deep neural network-based method for single nighttime rainy image enhancement. First, a decom-net based on Retinex theory is employed for image decomposition, and the purpose of this sub-net is to extract the reflection image and the illumination image from the input image. Then, an enhancement net is proposed for illumination adjustment. The goal of this sub-net is to remove the negative effect (low visual) caused by low illumination. Finally, a symmetric sub-net termed multi-stream network-based contextual autoencoder is developed, where rain features are directly learned from the enhanced nighttime rainy images in a recurrent way. The goal of this sub-net is to effectively remove rain streaks from the illumination-enhanced image. The experimental results show the advantage and effectiveness of the proposed method, and evident improvements over existing state-of-the-art methods are obtained with the proposed method.
This article presents a saliency guided remote sensing image dehazing network model. It consists of the following three blocks: A dense residual based backbone network, a saliency map generator, and ...a deformed atmospheric scattering model (ASM) based haze removal model, of which the dense residual based backbone network is used to capture the texture detail information of a remote sensing image, the saliency map generator is used to generate the saliency map of the related remote sensing image, and the generated saliency map is used to guide the network to capture more texture details through the guided fusion module. Finally, the deformed atmospheric scattering model (ASM) is used to remove haze from remote sensing images. The model here is compared with several state‐of‐art dehazing methods on synthetic data sets and real remote sensing images. Experimental results show that on the synthetic data set, the PSNR value of this model is increased by 4.47 db and the SSIM value is increased by 0.045 compared with the best model. On real remote sensing hazy images, the visual effect of our model is also better than that of existing methods. The authors also perform experiments to demonstrate that remote sensing image dehazing is helpful for remote sensing image detection automatically.
Nighttime low illumination image enhancement is highly desired for outdoor computer vision applications. However, few works have been studied towards this goal. In addition, the low illumination ...enhancement problem becomes very challenging when the depth information of a low illumination image is unknown. To address this problem, in this paper, we propose a dual channel prior-based method for nighttime low illumination image enhancement with a single image, which builds upon two existing image priors: dark channel prior and bright channel prior. We utilize the bright channel prior to get an initial transmission estimate and then use the dark channel as a complementary channel to correct potentially erroneous transmission estimates attained from the bright channel prior. Experimental results show significant credibility of the approach both visually and by quantitative comparison with existing methods.
A novel robust image denoising method based Total Variation (TV) model called Region Fusion based Split Bregman (RFSB) method is proposed in this paper. First, the structural characteristics of the ...edge region and that of the smooth region of the noisy image are analyzed and then separated. Second, split Bregman method is used for calculating two baseline TV-based model, the TV model and TV-penalized least squares functional model, and results of the two models are fused by proposed Region Fusion method. Third, for the fusion denoising result contains a wealth of repetitive redundant information, fast non-local means (FNLM) is introduced for post-processing the denoising result. Our proposed method has three contributions: (i) the use of split Bregman to calculate two baseline TV-based model because of its highly minimization speed; (ii) the proposal of a region fusion method to fuse two baseline models; (iii) the application of the FNLM method as the post-processing. Gray images and color images with different intensities of noise are used to compare our proposed method with other existing classical TV-based method. Experimental results show that Our proposed method has outperformed other TV-based methods in denoising visual effect and objective evaluation for different styles of noised images. The time complexity of our proposed method is about 2
%
and 50
%
lower than that of TV and Zhang’s Total Variation (ZTV). Our proposed method shows lower time complexity and does not distort the image information compared with two deep learning algorithm.
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