Search

Owing to the various object scales and high similarity with the surrounding organs (e.g., kidney, stomach, and spleen), it is difficult to accurately segment the liver region from the abdominal computed tomography images. In this study, we propose a multi-scale context integration network called MCI-Net for liver image segmentation. Specifically, we first design a simplified residual module to prevent network degradation. Given the scale variability of objects, we propose a multi-scale context extraction module by combining four cascaded branches of hybrid dilated convolutions to capture broader and deeper features. In addition, we introduce an external attention mechanism based on two external, learnable and shared memory units, which helps to perceive the most discriminative information and suppress redundant features. Finally, we provide a boundary correction block to further improve the localization ability of boundary information. Extensive experiments on two liver CT image benchmark datasets qualitatively and quantitatively illustrate that our method is effective in improving liver segmentation accuracy and outperforms several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Arising from low contrast, high similarity, and different scales between diverse tissues in 2D medical images, it is challenging to accurately segment the regions of interest. To address these issues, we propose a context hierarchical integrated network named CHI-Net for medical image segmentation, which can accurately segment salient regions from medical images in a purely task-driven manner. The proposed CHI-Net consists of two key modules, i.e., a dense dilated convolution (DDC) and a stacked residual pooling (SRP). Specifically, The DDC module can capture substantial complementary features hierarchically by combining four cascaded branches of hybrid dilated convolutions, which is conducive to extracting the features of diverse scales. The SRP module integrates encoder detail features by multiple effective field-of-views, which aims at generating more discriminative features. Extensive experimental results on five benchmark datasets with different objects illustrate that the proposed CHI-Net is superior to the state-of-the-art object segmentation methods. [ABSTRACT FROM AUTHOR]

The utilization of processed waste materials in road construction is of utmost significance from the environmental and economic viewpoints. In this paper, waste cooking oil (WCO) and ground tire rubber (GTR) were applied to modify the asphalt binder. Based on the optimal content of both modifiers, sixteen asphalt samples with different combinations of the two wastes were designed. Relevant tests including basic properties along with rheological behavior and chemical characterization were carried out on the modified asphalt. The results indicated that the modified asphalt with 20 wt % GTR and 2.5 wt %-5 wt. % WCO exhibited satisfactory high temperature resistance and low temperature rheological properties. The Fourier transform infrared spectroscopic investigation revealed that the presence of GTR increased the concentration of styrene and butadiene while the addition of WCO introduced fatty acids and esters to modified asphalt. Overall, the road performance studies demonstrate that modified asphalt mixture with 5 wt % WCO and 20 wt % GTR displayed a markedly improved anti-rutting properties, water stability and superior low-temperature performance compared to normal asphalt mixture. • The molecular structures of WTO/GTR-modified asphalt were analyzed. • Asphalt modified with an appropriate amount of GTR and WCO exhibited optimum properties. • GTR increased the concentration of functional groups in modified asphalt binder. • WCO introduced fatty acids and esters to modified asphalt binder. • WTO/GTR-modified asphalt mixtures showed a promising application in road industry. [ABSTRACT FROM AUTHOR]

Due to the gray values of liver and surrounding tissues and organs are resemblance in abdominal computed tomography(CT) images, it is difficult to accurately determine the boundary of liver images. To address the aforementioned issues, we propose a segmentation method based on dynamic adaptive residual network (DAR-net). Firstly, inspired by the U-Net network, a dynamic adaptive pooling strategy based on interpolation optimization is proposed to process all features in the pooling domain. Subsequently, the skip connections and a residual structure are introduced into the network to improve the generalization ability and convergence speed of DAR-net. Finally, removing irrelevant pixels and condition random fields are used to optimize the boundaries and textures of liver to avoid the inaccuracy of liver boundaries. Experiments on a standard 3DIRCADB dataset demonstrate that the DAR-net can obtain the average Dice score is 96.13%, which increases by 13.02% compared to the prediction result without any processing. [ABSTRACT FROM AUTHOR]

Self-supervised visual representation learning (SSL) attempts to extract significant features from unlabeled datasets, alleviating the necessity for labor-intensive and time-consuming manual labeling processes. However, existing contrastive learning-based methods typically suffer from the underutilization of datasets, consume significant computational resources, and employ longer training epochs or large batch sizes. In this study, we propose a novel method aimed at optimizing self-supervised learning that integrates the advantages of sparse-dense sampling and collaborative optimization, thereby significantly improving the performance of downstream tasks. Specifically, sparse-dense sampling primarily focuses on high-level semantic features, while leveraging the spatial structure relationship provided by the unlabeled dataset to ensure the incorporation of low-level texture features to improve data utilization. Besides, collaborative optimization, including contrastive and location tasks, further enhances the model's ability to perceive features of different dimensions, thereby improving its utilization of features in the embedding space. Furthermore, the combination of sparse-dense sampling and collaborative optimization strategies can reduce computational consumption while improving performance. Extensive experiments demonstrate that the proposed method effectively reduces the computational requirements while delivering favorable results. The codes and model weights will be available at https://github.com/AI-TYQ/S4. [ABSTRACT FROM AUTHOR]

To address the color cast and low contrast of underwater images, we propose a color correction and adaptive contrast enhancement algorithm for underwater image enhancement. In our work, we first design the dedicated fractions to compensate the lower color channels, which are calculated by considering the ratio of the differences between the upper and lower color channels to the lower color channels. Subsequently, an adaptive contrast enhancement algorithm is applied to each color channel is proposed to produce the background-stretched and foreground-stretched images, the combination of these two images significantly improves the contrast of the output image. Finally, a unsharp masking technique is applied to sharpen the overall image. Experiments on the standard underwater image enhancement benchmark dataset have proven that our algorithm can output high-quality images, while the performer of our algorithm is at least 5% better than the second-best in the average of all metric scores. [ABSTRACT FROM AUTHOR]

• CN pcf promotes the majority of coarse aggregates to form a well-embedded skeleton. • Optimum contents of dominant structure and disturbing aggregates were determined. • Correlation of CN pcf with contact zones (CZ) can describe internal structural features. • Mann-Whitney U test verified stability of the dominant skeleton with the appropriate level of CN pcf for resisting permanent rutting. Optimizing asphalt mix design at the indoor stage is of significant importance for enhancing the rutting resistance of asphalt mixture, which is affected by its structural characteristics. In this work, the coordination number of particle contact force (CN pcf) was proposed as an indicator to represent contact characteristics of skeleton structure aggregates in asphalt mixture. Nine asphalt mixtures with different gradations were designed, and the relationship of CN pcf with the number of aggregate contact zones (CZ) was established by combining rutting tests and digital image processing technique (DIP). The Mann-Whitney U test was implemented to analyze the distribution properties of inter-particle contacts before and after the rutting test. In addition, the resistance to the further expansion of rutting was analyzed. The results revealed a significant positive correlation (PCCs = 0.843, R 2 = 0.711) between CN pcf and CZ. The content of coarse aggregates in the dominant structure did not exhibit monotonic related to anti-rutting performance of the asphalt mixture. Therefore, an optimum aggregate content of 57% was utilized. The Mann-Whitney U test revealed that the mesoscale skeleton structure of the asphalt mixes before and after rutting exhibited excellent stability. This study further indicated the applicability of combining CN pcf to adjust the mix design to enhance the rutting resistance of asphalt mixture and to prevent rutting expansion in flexible pavement. [ABSTRACT FROM AUTHOR]

Underwater images suffer from multiple quality degradation issues due to the complex underwater environment. Unfortunately, currently available underwater image enhancement methods do not enhance all degradation types of underwater images, so it is important to study underwater image enhancement methods dedicated to specific degradation categories for underwater vision tasks. To tackle the above issues, we propose a Multi-channel Attention Network (MCANet) with Multi-color Space Encoder, which is instructive for designing dedicated classes of underwater image enhancement methods. Specifically, first, we use a multi-color space encoding method to fully integrate the advantages of features in different color spaces. Then, we obtain global and local deeper features of images in multiple dimensions through a multi-channel attention path aggregation strategy. Finally, we form the MCANet network architecture by embedding and stacking multi-channel attention modules to continuously strengthen the perception of image features. The experimental results show that the classification accuracy of MCANet reaches 98.737%. [ABSTRACT FROM AUTHOR]

To improve remote sensing image transmission efficiency, we propose a cloud detection method using a multi-feature embedded learning support vector machine (SVM) to address cloud coverage occupying channel transmission bandwidth. Specifically, we first consider the imaging and physical properties of the clouds to construct a multi-feature space of cloud and non-cloud samples, which mainly includes five valuable features of grayscale, geometry, contrast, correlation, and angular second moment. Subsequently, we regard cloud detection (CDRSI) of remote sensing images as a binary classification problem, and construct a classifier by using multi-feature embedded learning SVM. Finally, the CDRSI is implemented by image block operations. Additionally, we build a large-scale real-world Remote Sensing Image Cloud Detection Benchmark (RSICDB) including 1520 images, where 790 non-cloud images and 430 cloud images are used as training datasets, 150 of which as test samples with the corresponding 150 mask results. Experimental results demonstrate that the proposed method can detect clouds with higher accuracy and robustness than compared methods. [ABSTRACT FROM AUTHOR]