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| 1 | Fractional differential approach to detecting textural features of digital image and its fractional differential filter implementation显示文摘This paper mainly discusses fractional differential approach to detecting textural features of digital image and its fractional differential filter. Firstly,both the geometric meaning and the kinetic physical meaning of fractional differential are clearly explained in view of information theory and kinetics,respectively. Secondly,it puts forward and discusses the definitions and theories of fractional stationary point,fractional equilibrium coefficient,fractional stable coefficient,and fractional grayscale co-occurrence matrix. At the same time,it particularly discusses fractional grayscale co-occurrence matrix approach to detecting textural features of digital image. Thirdly,it discusses in detail the structures and parameters of n×n any order fractional differential mask on negative x-coordinate,positive x-coordinate,negative y-coordinate,positive y-coordinate,left downward diagonal,left upward diagonal,right downward diagonal,and right upward diagonal,respectively. Furthermore,it discusses the numerical implementation algorithms of fractional differential mask for digital image. Lastly,based on the above-mentioned discussion,it puts forward and discusses the theory and implementation of fractional differential filter for digital image. Experiments show that the fractional differential-based image operator has excellent feedback for enhancing the textural details of rich-grained digital images. | PU YiFei WANG WeiXing ZHOU JiLiu WANG YiYang JIA HuaDing | 2008 | Science in China(Series F)2008，51，9： | 50 |
| 2 | Fractional partial differential equation denoising models for texture image显示文摘In this paper,a set of fractional partial differential equations based on fractional total variation and fractional steepest descent approach are proposed to address the problem of traditional drawbacks of PM and ROF multi-scale denoising for texture image.By extending Green,Gauss,Stokes and Euler-Lagrange formulas to fractional field,we can find that the integer formulas are just their special case of fractional ones.In order to improve the denoising capability,we proposed 4 fractional partial differential equation based multiscale denoising models,and then discussed their stabilities and convergence rate.Theoretic deduction and experimental evaluation demonstrate the stability and astringency of fractional steepest descent approach,and fractional nonlinearly multi-scale denoising capability and best value of parameters are discussed also.The experiments results prove that the ability for preserving high-frequency edge and complex texture information of the proposed denoising models are obviously superior to traditional integral based algorithms,especially for texture detail rich images. | PU YiFei SIARRY Patrick ZHOU JiLiu LIU YiGuang ZHANG Ni HUANG Guo LIU YiZhi | 2014 | Science China(Information Sciences)2014，57，7： | 13 |
| 3 | Preparation and characteristics of DNA-nanoparticles targeting to hepatocarcinoma cells显示文摘AIM:To prepare thymidine kinase gene (TK gene) nanopartides and to investigate the expression of TK gene.METHODS: Poly(D,L-lactic-co-glycolic acid) (PLGA), a biodegradable and biocompatible polymer, was used to prepare recombinant plasmid p^EGFP-AFP nanoparticles by a double-emulsion evaporation technique. Characteristics of the nanoparticles were investigated in this study, including morphology, entrapment efficiency, and tissue distribution.The expression of TK gene was also investigated by MTT assay, by which the viable cells were determined alter the addition of ganciclovir (GCV).The enhanced green fluorescent protein (EGFP) expression in human hepatocellular carcinoma SMMC-7721 cells and normal parenchymal Chang liver cellswere assessed by flow cytometry.RESULTS: The prepared plasmid-nanoparticles had regular spherical surface and narrow particle size span with a mean diameter of 72±12nm.The mean entrapment efficiency was 91.25%. A total of 80.14% DNA was found to be localized in the livers after 1-h injection with ^32P-DNA-PLGA nanoparticles in mouse caudal vein. The expression of DNA encapsulated in nanopartides was much higher than that in naked DNA, and human hepatocellular carcinoma SMMC-7721 cells were more sensitive to GCV than human normal parenchymal Chang liver cells.CONCLUSION:The enhanced transfection efficiency and stronger ability to protect plasmid DNA from being degraded by nucleases are due to nanoparticles encapsulation. | QinHe JiLiu XunSun Zhi-RongZhang | 2004 | World Journal of Gastroenterology2004，10，5： | 4 |
| 4 | MRI Brain Tumor Segmentation Using 3D U-Net with Dense Encoder Blocks and Residual Decoder Blocks显示文摘The main task of magnetic resonance imaging (MRI) automatic brain tumor segmentation is to automaticallysegment the brain tumor edema, peritumoral edema, endoscopic core, enhancing tumor core and nonenhancingtumor core from 3D MR images. Because the location, size, shape and intensity of brain tumors vary greatly, itis very difficult to segment these brain tumor regions automatically. In this paper, by combining the advantagesof DenseNet and ResNet, we proposed a new 3D U-Net with dense encoder blocks and residual decoder blocks.We used dense blocks in the encoder part and residual blocks in the decoder part. The number of output featuremaps increases with the network layers in contracting path of encoder, which is consistent with the characteristicsof dense blocks. Using dense blocks can decrease the number of network parameters, deepen network layers,strengthen feature propagation, alleviate vanishing-gradient and enlarge receptive fields. The residual blockswere used in the decoder to replace the convolution neural block of original U-Net, which made the networkperformance better. Our proposed approach was trained and validated on the BraTS2019 training and validationdata set. We obtained dice scores of 0.901, 0.815 and 0.766 for whole tumor, tumor core and enhancing tumorcore respectively on the BraTS2019 validation data set. Our method has the better performance than the original3D U-Net. The results of our experiment demonstrate that compared with some state-of-the-art methods, ourapproach is a competitive automatic brain tumor segmentation method. | Juhong Tie Hui Peng Jiliu Zhou | 2021 | Computer Modeling in Engineering & Sciences2021，，8： | 2 |
| 5 | BEX2 regulates mitochondrial apoptosis and G1 cell cycle in breast cancer显示文摘 | AliNaderi JiLiu Ian C.Bennett | 2010 | Int. J. Cancer2010，，7： | 1 |
| 6 | Fractional differential mask: a fractional differential-based approach for muhiscale texture enhancement 显示文摘 | PU YIFEI ZHOU JILIU YUAN XIAO | 2010 | IEEE Transactions on Image Processing2010，19，2： | 1 |
| 7 | A Novel Approach for Multi-scale Texture Segmentation Based on Fractional Differential显示文摘 | Pu Yifei Zhou Jiliu | 2011 | International Journal of Computer Mathematics2011，88，1： | 1 |
| 8 | Edge Detection of Color Image Based on Quaternion Fractional Differential显示文摘 | Gao Chaobang Zhou Jiliu | 2011 | IET Image Processing2011，5，3： | 1 |
| 9 | Fractional Differential Mask:A Fractional Differential-based Approach for Mutiscale Texture Enhancement显示文摘 | Pu Yifei Zhou Jiliu Yuan Xiao | 2010 | IEEE Transactions on Image Processing2010，19，2： | 1 |
| 10 | Fractional differentialmask:a fractional differential-based approach for multiscaletexture enhancement显示文摘 | Pu Yifei Zhou Jiliu Yuan Xiao | 2010 | IEEE Transactions on Image Pro-cessing2010，19，2： | 1 |
| 11 | Identification of the no,-mal and abnormal heart sounds using wavelet- time entropy features based on OMS-WPD 显示文摘 | Wang Yan I i Wenzao Zhou Jiliu | 2014 | Future Generation Computer System2014，37，： | 1 |
| 12 | Fractional Differential Mask:A Fractional Differential-based Approach for Multiscale Texture Enhancement显示文摘 | Pu Yifei Zhou Jiliu Yuan Xiao | | 0，，02： | 1 |
| 13 | Text Detection and Recognition for Natural Scene Images Using Deep Convolutional Neural Networks显示文摘Words are the most indispensable information in human life.It is very important to analyze and understand the meaning of words.Compared with the general visual elements,the text conveys rich and high-level moral information,which enables the computer to better understand the semantic content of the text.With the rapid development of computer technology,great achievements have been made in text information detection and recognition.However,when dealing with text characters in natural scene images,there are still some limitations in the detection and recognition of natural scene images.Because natural scene image has more interference and complexity than text,these factors make the detection and recognition of natural scene image text face many challenges.To solve this problem,a new text detection and recognition method based on depth convolution neural network is proposed for natural scene image in this paper.In text detection,this method obtains high-level visual features from the bottom pixels by ResNet network,and extracts the context features from character sequences by BLSTM layer,then introduce to the idea of faster R-CNN vertical anchor point to find the bounding box of the detected text,which effectively improves the effect of text object detection.In addition,in text recognition task,DenseNet model is used to construct character recognition based on Kares.Finally,the output of Softmax is used to classify each character.Our method can replace the artificially defined features with automatic learning and context-based features.It improves the efficiency and accuracy of recognition,and realizes text detection and recognition of natural scene images.And on the PAC2018 competition platform,the experimental results have achieved good results. | Xianyu Wu Chao Luo Qian Zhang Jiliu Zhou Hao Yang Yulian Li | 2019 | Computers, Materials & Continua2019，，7： | 1 |
| 14 | Image texture details analysis filter on fractional differential theory显示文摘 | Pu Yifei Wang Weixing Zhou Jiliu | 2008 | Science in China2008，51，9： | 1 |
| 15 | Fractional differential mask:A fractional differential-based approach for multiscale texture enhancement显示文摘 | PU YIFEI ZHOU JILIU | 2010 | IEEETransactions on Image Processing2010，19，2： | 1 |
| 16 | Fractional differentia mask:a fractional differential-based approach for multiscale texture enhancement显示文摘 | Pu Yifei Zhou Jiliu Yuan Xiao | | 0，，02： | 1 |
| 17 | Fractional differential mask: a fractional differential based approach for mulfiscale texture enhancement 显示文摘 | Pu Yifei Zhou Jiliu Yuan Xiao | 2010 | IEEE Transaction on Image Pro cessing2010，19，2： | 1 |
| 18 | Fractional differential mask:a fractional differential-based approach for multiscale texture enhancement 显示文摘 | Pu Yifei Zhou Jiliu Yuan Xiao | 2010 | IEEE Trans on Image Processing2010，19，2： | 1 |
| 19 | Inhibitors of protein phosphatase-2A from human brain structures, immunocytological localization and activities towards dephosphorylation of the Alzheimer type hyperphosphorylated tau显示文摘 | Ichiro Tsujio Tanweer Zaidi Jiliu Xu Leszek Kotula Inge Grundke-Iqbal Khalid Iqbal | 2004 | FEBS Letters2004，，2： | 1 |
| 20 | Improved Canonical Correlation Analysis and Its Applications in Image Recognition显示文摘 | Lei Gang Zhou Jiliu | 2010 | Journal of Computational Information Systems2010，6，11： | 1 |
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