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Learning-based parameter prediction for quality control in three-dimensional medical image compression Research Articles

Yuxuan Hou, Zhong Ren, Yubo Tao, Wei Chen,3140104190@zju.edu.cn,renzhong@cad.zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 9,   Pages 1169-1178 doi: 10.1631/FITEE.2000234

Abstract: is of vital importance in compressing three-dimensional (3D) medical imaging data. Optimal compression parameters need to be determined based on the specific quality requirement. In , regarded as the state-of-the-art compression tool, the quantization parameter (QP) plays a dominant role in controlling quality. The direct application of a video-based scheme in predicting the ideal parameters for 3D cannot guarantee satisfactory results. In this paper we propose a parameter prediction scheme to achieve efficient . Its kernel is a support vector regression (SVR) based learning model that is capable of predicting the optimal QP from both video-based and structural image features extracted directly from raw data, avoiding time-consuming processes such as pre-encoding and iteration, which are often needed in existing techniques. Experimental results on several datasets verify that our approach outperforms current video-based methods.

Keywords： 医学图像压缩；高效视频编码（HEVC）；质量控制；基于学习方法    

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Long-term prediction for hierarchical-B-picture-based coding of video with repeated shots None

Xu-guang ZUO, Lu YU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 3,   Pages 459-470 doi: 10.1631/FITEE.1601552

Abstract: The latest video coding standard High Efficiency Video Coding (HEVC) can achieve much higher coding efficiencyAccording to our observations, when these videos are encoded by HEVC with the hierarchical B-picture

Keywords： High Efficiency Video Coding (HEVC)     Long-term temporal correlation     Long-term prediction     Hierarchical    

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Adaptive compression method for underwater images based on perceived quality estimation Regular Papers

Ya-qiong CAI, Hai-xia ZOU, Fei YUAN

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 5,   Pages 716-730 doi: 10.1631/FITEE.1700737

Abstract:

Underwater image compression is an important and essential part of an underwater image transmission system. An assessment and prediction method of effectively compressed image quality can assist the system in adjusting its compression ratio during the image compression process, thereby improving the efficiency of the image transmission system. This study first estimates the perceived quality of underwater image compression based on embedded coding compression and compressive sensing, then builds a model based on the mapping between image activity measurement (IAM) and bits per pixel and structural similarity (BPP-SSIM) curves, next obtains model parameters by linear fitting, and finally predicts the perceived quality of the image compression method based on IAM, compression ratio, and compression strategy. Experimental results show that the model can effectively fit the quality curve of underwater image compression. According to the rules of parameters in this model, the perceived quality of underwater compressed images can be estimated within a small error range. The presented method can effectively estimate the perceived quality of underwater compressed images, balance the relationship between the compression ratio and compression quality, reduce the pressure on the data cache, and thus improve the efficiency of the underwater image communication system.

Keywords： Underwater image compression     Set partitioning in hierarchical trees     Compressive sensing     Compression quality estimation    

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A New Algorithm of Fractal Image Coding

Wang Xiuni,Jiang Wei,Wang Licun

Strategic Study of CAE 2006, Volume 8, Issue 1,   Pages 54-57

Abstract:

Because it takes too much of time in fractal image coding, the paper analyses the factors that affect the speed of fractal image coding , and proposes a novel idea by using the reformed variance (tentatively) to improve image fractal compression performance . A theorem is proved that the IFS cannot change the image blocks' reformed variance. Moreover , it gives a novel fractal image compression method based on the reformed variance. The simulation results illuminate that the new method can run fast, at the same time it can improve the PSNR when compared with other fast algorithms.

Keywords： fractal coding     image compression     variance    

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The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis Review

Yue Hou, Qiuhan Li, Chen Zhang, Guoyang Lu, Zhoujing Ye, Yihan Chen, Linbing Wang, Dandan Cao

Engineering 2021, Volume 7, Issue 6,   Pages 845-856 doi: 10.1016/j.eng.2020.07.030

Abstract:

In modern transportation, pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians. Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users. Therefore, monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance, which in turn ensures public transportation safety. Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions. Advanced technologies can be employed for the collection and analysis of such data, including various intrusive sensing techniques, image processing techniques, and machine learning methods. This review summarizes the state-of-the-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.

Keywords： Pavement monitoring and analysis     The state-of-the-art review     Intrusive sensing     Image processing techniques     Machine learning methods    

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Image quality assessmentmethod based on nonlinear feature extraction in kernel space Article

Yong DING,Nan LI,Yang ZHAO,Kai HUANG

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 10,   Pages 1008-1017 doi: 10.1631/FITEE.1500439

Abstract: To match human perception, extracting perceptual features effectively plays an important role in image quality assessment. In contrast to most existing methods that use linear transformations or models to represent images, we employ a complex mathematical expression of high dimensionality to reveal the statistical characteristics of the images. Furthermore, by introducing kernel methods to transform the linear problem into a nonlinear one, a full-reference image quality assessment method is proposed based on high-dimensional nonlinear feature extraction. Experiments on the LIVE, TID2008, and CSIQ databases demonstrate that nonlinear features offer competitive performance for image inherent quality representation and the proposed method achieves a promising performance that is consistent with human subjective evaluation.

Keywords： Image quality assessment     Full-reference method     Feature extraction     Kernel space     Support vector regression    

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Fuzzy iterative learning control and numerical simulation of tall building seismic response control

Wang Quan,Wang Jianguo,Zhang Mingxiang

Strategic Study of CAE 2011, Volume 13, Issue 4,   Pages 81-86

Abstract:

With research into the fundamental ideas of self tuning control, fuzzy logic and iterative learning control (ILC), this paper provides a new type of fuzzy iterative learning control strategy to reduce the seismic response of tall building. It improves the robustness of the iterative learning control. The model of a seismically excited building in the second generation benchmark vibration control for buildings is studied, using the new control strategy to calculate the seismic response of the building. The result of simulation shows that fuzzy iterative learning control strategy can control the seismic response of the building effectively, and has advantages of simple and practical learning control law, high precision in trajectory and good robustness.

Keywords： tall building     seismic response     iterative learning control     fuzzy control    

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Representation learning via a semi-supervised stacked distance autoencoder for image classification Research Articles

Liang Hou, Xiao-yi Luo, Zi-yang Wang, Jun Liang,jliang@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7,   Pages 963-1118 doi: 10.1631/FITEE.1900116

Abstract: is an important application of deep learning. In a typical classification task, the classification accuracy is strongly related to the features that are extracted via deep learning methods. An is a special type of , often used for dimensionality reduction and feature extraction. The proposed method is based on the traditional , incorporating the “distance” information between samples from different categories. The model is called a semi-supervised distance . Each layer is first pre-trained in an unsupervised manner. In the subsequent supervised training, the optimized parameters are set as the initial values. To obtain more suitable features, we use a stacked model to replace the basic structure with a single hidden layer. A series of experiments are carried out to test the performance of different models on several datasets, including the MNIST dataset, street view house numbers (SVHN) dataset, German traffic sign recognition benchmark (GTSRB), and CIFAR-10 dataset. The proposed semi-supervised distance method is compared with the traditional , sparse , and supervised . Experimental results verify the effectiveness of the proposed model.

Keywords： 自动编码器；图像分类；半监督学习；神经网络    

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Learning to select pseudo labels: a semi-supervised method for named entity recognition Research Articles

Zhen-zhen Li, Da-wei Feng, Dong-sheng Li, Xi-cheng Lu,lizhenzhen14@nudt.edu.cn,davyfeng.c@gmail.com,dsli@nudt.edu.cn,xclu@nudt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 6,   Pages 809-962 doi: 10.1631/FITEE.1800743

Abstract: models have achieved state-of-the-art performance in (NER); the good performance, however, relies heavily on substantial amounts of labeled data. In some specific areas such as medical, financial, and military domains, labeled data is very scarce, while is readily available. Previous studies have used to enrich word representations, but a large amount of entity information in is neglected, which may be beneficial to the NER task. In this study, we propose a for NER tasks, which learns to create high-quality labeled data by applying a pre-trained module to filter out erroneous pseudo labels. Pseudo labels are automatically generated for and used as if they were true labels. Our semi-supervised framework includes three steps: constructing an optimal single neural model for a specific NER task, learning a module that evaluates pseudo labels, and creating new labeled data and improving the NER model iteratively. Experimental results on two English NER tasks and one Chinese clinical NER task demonstrate that our method further improves the performance of the best single neural model. Even when we use only pre-trained static word embeddings and do not rely on any external knowledge, our method achieves comparable performance to those state-of-the-art models on the CoNLL-2003 and OntoNotes 5.0 English NER tasks.

Keywords： 命名实体识别；无标注数据；深度学习；半监督学习方法    

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Research of Lossless Image Compression Base on Level-scalability

Li Luwei,Zhou Shuoyan,Cai Yiyu

Strategic Study of CAE 2005, Volume 7, Issue 10,   Pages 33-37

Abstract:

A level-embedded lossless image compression method for continuous-tone still images is presented. Level (bit-plane) scalability is achieved by separating the image into two layers (the base layer and the residual layer) before compression. Excellent compression performance is obtained by exploiting both spatial and interlevel correlations. A comparison of the proposed scheme with a number of scalable and non-scalable lossless image compression algorithms indicates that the level-embedded compression incurs only a small penalty in compression efficiency over non-scalable lossless compression, while offering the significant benefit of level-scalability.

Keywords： data processing techniques     lossless image compression     context-based model     embedded level    

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Quality Control and Nonclinical Research on CAR-T Cell Products: General Principles and Key Issues Review

Yonghong Li, Yan Huo, Lei Yu, Junzhi Wang

Engineering 2019, Volume 5, Issue 1,   Pages 122-131 doi: 10.1016/j.eng.2018.12.003

Abstract:

Adoptive cell therapy using chimeric antigen receptor T (CAR-T) cells, which is a promising cancer immunotherapy strategy, has been developing very rapidly in recent years. CAR-T cells are genetically modified T cells that can specifically recognize tumor specific antigens on the surface of tumor cells, and then effectively kill tumor cells. At present, exciting results are being achieved in clinical applications of CAR-T cells for patients with hematological malignancies. The research and development of CAR-T cells for various targets and for the treatment of solid tumors have become a hot topic worldwide, so an increasing number of investigational new drug applications (INDAs) and new drug applications (NDAs) of CAR-T cell products are expected to be submitted in future. The quality control and nonclinical research of these products are of great significance in ensuring the safety and effectiveness of these products; however, they also present great challenges and difficulties. This article discusses the general principles of and key issues regarding the quality control and nonclinical research of CAR-T cell products based on their product characteristics and on relevant guidelines for gene and cell therapy products.

Keywords： Chimeric antigen receptor T cells     Quality control     Nonclinical research     Safety     Efficacy     Clinical trials     Cancer immunotherapy    

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Kernel sparse representation for MRI image analysis in automatic brain tumor segmentation None

Ji-jun TONG, Peng ZHANG, Yu-xiang WENG, Dan-hua ZHU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 4,   Pages 471-480 doi: 10.1631/FITEE.1620342

Abstract: The segmentation of brain tumor plays an important role in diagnosis, treatment planning, and surgical simulation. The precise segmentation of brain tumor can help clinicians obtain its location, size, and shape information. We propose a fully automatic brain tumor segmentation method based on kernel sparse coding. It is validated with 3D multiple-modality magnetic resonance imaging (MRI). In this method, MRI images are pre-processed first to reduce the noise, and then kernel dictionary learning is used to extract the nonlinear features to construct five adaptive dictionaries for healthy tissues, necrosis, edema, non-enhancing tumor, and enhancing tumor tissues. Sparse coding is performed on the feature vectors extracted from the original MRI images, which are a patch of × × around the voxel. A kernel-clustering algorithm based on dictionary learning is developed to code the voxels. In the end, morphological filtering is used to fill in the area among multiple connected components to improve the segmentation quality. To assess the segmentation performance, the segmentation results are uploaded to the online evaluation system where the evaluation metrics dice score, positive predictive value (PPV), sensitivity, and kappa are used. The results demonstrate that the proposed method has good performance on the complete tumor region (dice: 0.83; PPV: 0.84; sensitivity: 0.82), while slightly worse performance on the tumor core (dice: 0.69; PPV: 0.76; sensitivity: 0.80) and enhancing tumor (dice: 0.58; PPV: 0.60; sensitivity: 0.65). It is competitive to the other groups in the brain tumor segmentation challenge. Therefore, it is a potential method in differentiation of healthy and pathological tissues.

Keywords： Brain tumor segmentation     Kernel method     Sparse coding     Dictionary learning    

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Practice and Quest of Quality Control in Three Gorges Project

Wang Jiazhu,Xu Changyi

Strategic Study of CAE 2001, Volume 3, Issue 10,   Pages 77-81

Abstract:

The Three Gorges Project (TGP) is a vitally important and backbone project in the development and harnessing of the Yangtze River. Since TOP started eight years ago, a matured and complete quality assurance system has been established with the whole process, full direction and sliced quality adiminstration. The quality of the project has been controlled effectively and the result is quite good and accords with the require-ments of design. Based on a detailed introdution of the TGP quality assurece and quality control, the paper give some suggestions and views about the work in the future.

Keywords： Three Gorges Project (TGP)     quality assurece     quality control    

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Deep Learning in Medical Ultrasound Analysis: A Review Review

Shengfeng Liu, Yi Wang, Xin Yang, Baiying Lei, Li Liu, Shawn Xiang Li, Dong Ni, Tianfu Wang

Engineering 2019, Volume 5, Issue 2,   Pages 261-275 doi: 10.1016/j.eng.2018.11.020

Abstract:

Ultrasound (US) has become one of the most commonly performed imaging modalities in clinical practice. It is a rapidly evolving technology with certain advantages and with unique challenges that include low imaging quality and high variability. From the perspective of image analysis, it is essential to develop advanced automatic US image analysis methods to assist in US diagnosis and/or to make such assessment more objective and accurate. Deep learning has recently emerged as the leading machine learning tool in various research fields, and especially in general imaging analysis and computer vision. Deep learning also shows huge potential for various automatic US image analysis tasks. This review first briefly introduces several popular deep learning architectures, and then summarizes and thoroughly discusses their applications in various specific tasks in US image analysis, such as classification, detection, and segmentation. Finally, the open challenges and potential trends of the future application of deep learning in medical US image analysis are discussed.

Keywords： Deep learning     Medical ultrasound analysis     Classification     Segmentation     Detection    

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Video summarization with a graph convolutional attention network Research Articles

Ping Li, Chao Tang, Xianghua Xu,patriclouis.lee@gmail.com

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 6,   Pages 902-913 doi: 10.1631/FITEE.2000429

Abstract: has established itself as a fundamental technique for generating compact and concise video, which alleviates managing and browsing large-scale video data. Existing methods fail to fully consider the local and global relations among frames of video, leading to a deteriorated summarization performance. To address the above problem, we propose a graph convolutional attention network (GCAN) for . GCAN consists of two parts, embedding learning and , where embedding learning includes the temporal branch and graph branch. In particular, GCAN uses dilated temporal convolution to model local cues and temporal self-attention to exploit global cues for video frames. It learns graph embedding via a multi-layer to reveal the intrinsic structure of frame samples. The part combines the output streams from the temporal branch and graph branch to create the context-aware representation of frames, on which the importance scores are evaluated for selecting representative frames to generate video summary. Experiments are carried out on two benchmark databases, SumMe and TVSum, showing that the proposed GCAN approach enjoys superior performance compared to several state-of-the-art alternatives in three evaluation settings.

Keywords： 时序学习；自注意力机制；图卷积网络；上下文融合；视频摘要    

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Title Author Date Type Operation

Learning-based parameter prediction for quality control in three-dimensional medical image compression

Yuxuan Hou, Zhong Ren, Yubo Tao, Wei Chen,3140104190@zju.edu.cn,renzhong@cad.zju.edu.cn

Journal Article

Long-term prediction for hierarchical-B-picture-based coding of video with repeated shots

Xu-guang ZUO, Lu YU

Journal Article

Adaptive compression method for underwater images based on perceived quality estimation

Ya-qiong CAI, Hai-xia ZOU, Fei YUAN

Journal Article

A New Algorithm of Fractal Image Coding

Wang Xiuni,Jiang Wei,Wang Licun

Journal Article

The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis

Yue Hou, Qiuhan Li, Chen Zhang, Guoyang Lu, Zhoujing Ye, Yihan Chen, Linbing Wang, Dandan Cao

Journal Article

Image quality assessmentmethod based on nonlinear feature extraction in kernel space

Yong DING,Nan LI,Yang ZHAO,Kai HUANG

Journal Article

Fuzzy iterative learning control and numerical simulation of tall building seismic response control

Wang Quan,Wang Jianguo,Zhang Mingxiang

Journal Article

Representation learning via a semi-supervised stacked distance autoencoder for image classification

Liang Hou, Xiao-yi Luo, Zi-yang Wang, Jun Liang,jliang@zju.edu.cn

Journal Article

Learning to select pseudo labels: a semi-supervised method for named entity recognition

Zhen-zhen Li, Da-wei Feng, Dong-sheng Li, Xi-cheng Lu,lizhenzhen14@nudt.edu.cn,davyfeng.c@gmail.com,dsli@nudt.edu.cn,xclu@nudt.edu.cn

Journal Article

Research of Lossless Image Compression Base on Level-scalability

Li Luwei,Zhou Shuoyan,Cai Yiyu

Journal Article

Quality Control and Nonclinical Research on CAR-T Cell Products: General Principles and Key Issues

Yonghong Li, Yan Huo, Lei Yu, Junzhi Wang

Journal Article

Kernel sparse representation for MRI image analysis in automatic brain tumor segmentation

Ji-jun TONG, Peng ZHANG, Yu-xiang WENG, Dan-hua ZHU

Journal Article

Practice and Quest of Quality Control in Three Gorges Project

Wang Jiazhu,Xu Changyi

Journal Article

Deep Learning in Medical Ultrasound Analysis: A Review

Shengfeng Liu, Yi Wang, Xin Yang, Baiying Lei, Li Liu, Shawn Xiang Li, Dong Ni, Tianfu Wang

Journal Article

Video summarization with a graph convolutional attention network

Ping Li, Chao Tang, Xianghua Xu,patriclouis.lee@gmail.com

Journal Article