T_(1)-mapping鉴别腮腺常见肿瘤的初探

目的:探讨T_(1)-mapping和分段读出DWI(RESOLVE-DWI)在腮腺常见肿瘤鉴别诊断中的价值。方法:回顾性分析2018年6月-2022年8月经病理证实的169例腮腺常见肿瘤患者的T_(1)-mapping及分段读出DWI资料。其中,多形性腺瘤85例,Warthin瘤32例,恶性肿瘤52例。测量并比较三组肿瘤的T_(1)及ADC值,运用受试者工作特征(ROC)曲线评价T_(1)、ADC及二者联合(T_(1)+ADC)对腮腺肿瘤的鉴别诊断价值。采用Delong检验比较各参数的AUC的差异。结果:三组肿瘤的T_(1)值和ADC值的差异均有统计学意义(P<0.001);组间两两比较,差异均有统计学意义(P<0.001);多形性腺瘤的T_(1)值及ADC值最高;恶性肿瘤次之,Warthin瘤最低。T_(1)和ADC鉴别多形性腺瘤与Warthin瘤、多形性腺瘤与恶性肿瘤、Warthin瘤与恶性肿瘤的AUC分别为0.914和0.997、0.664和0.869、0.835和0.695,Delong检验显示2个参数AUC的差异均无统计学意义(P>0.05)。T_(1)+ADC鉴别多形性腺瘤与Warthin瘤、多形性腺瘤与恶性肿瘤的AUC均显著高于T_(1)(1.000 vs.0.914、0.873 vs.0.664,P均<0.001);T_(1)+ADC鉴别Warthin瘤与恶性肿瘤的AUC显著高于T_(1)(0.895 vs.0.835,P<0.001)和ADC(0.895 vs.0.695,P<0.001)。结论:T_(1)-mapping和RESOLVE-DWI有助于鉴别诊断腮腺常见肿瘤,二者联合诊断可提高鉴别诊断效能。

传统武术套路训练对大学生武术运动员膝关节软骨影响的T2-mapping功能磁共振成像研究

目的:本研究旨在通过T2-mapping功能磁共振影像学技术对大学生武术运动员膝关节软骨进行定量评估,探究传统武术套路训练对大学生武术运动员膝关节软骨的影响。方法:选取9名年龄介于18~25岁之间的大学生武术运动员作为实验组,同时选取18名普通健康的大学生作为对照组。在相同条件下,对两组分别进行右膝关节的T2-mapping磁共振检查,并将膝关节软骨分为股骨内侧髁、胫骨内侧髁、髌软骨、股骨外侧髁及胫骨外侧髁5个感兴趣区,比较实验组与对照组之间T2值的差异,分析各组膝关节软骨不同感兴趣区T2值的变化。结果:(1)不论性别,实验组和对照组各个膝关节面软骨感兴趣区的T2值均未见显著性差异(P>0.05);(2)男性对照组、女性对照组以及女性实验组的膝关节各感兴趣区T2值两两之间均无统计学差异(P>0.05);(3)男性实验组膝关节股骨外侧髁与胫骨内侧髁、髌软骨的T2值差异具有统计学意义(P<0.05)。结论:T2-mapping技术可通过无创手段定量评估传统武术套路训练对大学生膝关节软骨的影响。在长期传统武术套路训练后,男性大学生武术运动员的膝关节出现了异质性改变,因此在训练过程中应该注意调整方案,以实现针对性保护。

An Improved High Precision 3D Semantic Mapping of Indoor Scenes from RGB-D Images

This paper proposes an improved high-precision 3D semantic mapping method for indoor scenes using RGB-D images.The current semantic mapping algorithms suffer from low semantic annotation accuracy and insufficient real-time performance.To address these issues,we first adopt the Elastic Fusion algorithm to select key frames from indoor environment image sequences captured by the Kinect sensor and construct the indoor environment space model.Then,an indoor RGB-D image semantic segmentation network is proposed,which uses multi-scale feature fusion to quickly and accurately obtain object labeling information at the pixel level of the spatial point cloud model.Finally,Bayesian updating is used to conduct incremental semantic label fusion on the established spatial point cloud model.We also employ dense conditional random fields(CRF)to optimize the 3D semantic map model,resulting in a high-precision spatial semantic map of indoor scenes.Experimental results show that the proposed semantic mapping system can process image sequences collected by RGB-D sensors in real-time and output accurate semantic segmentation results of indoor scene images and the current local spatial semantic map.Finally,it constructs a globally consistent high-precision indoor scenes 3D semantic map.

磁共振T2-mapping评估Graves眼病活动性的价值

目的探讨磁共振T_(2)-mapping在Graves眼病(GO)活动性评估方面的价值。方法收集2019年7月-2021年1月于重庆医科大学附属第一医院内分泌科就诊的GO患者64例,使用计算机对其进行简单随机分组,其中49例作为观察研究对象,另外15例进行诊断试验评价。按照临床活动性评分(CAS)将49例GO患者分为活动组(CAS≥3分,48只患眼)与非活动组(CAS<3分,50只患眼)。同期招募31名健康志愿者作为正常对照组,共62只眼。所有人均行3.0T眼眶磁共振T_(2)-mapping成像。在T_(2)-mapping冠状位图像上测量上直肌、下直肌、内直肌、外直肌眼球后5个层面的T_(2)弛豫时间(T_(2)RT),每块眼外肌分别取眼球后5个层面的T_(2)RT最大值来代表该眼外肌的T_(2)RT,最后取4块眼外肌T_(2)RT中的最大值,以眼外肌最大T_(2)RT表示。比较活动组、非活动组与正常对照组间上述5个指标(上直肌T_(2)RT、下直肌T_(2)RT、内直肌T_(2)RT、外直肌T_(2)RT、眼外肌最大T_(2)RT)的差异;采用受试者工作特征(ROC)曲线分析上述5个指标对GO活动性评估的诊断价值,得出诊断阈值,然后利用另外15例GO患者进行诊断试验评价,确定诊断效能较高的指标及其诊断活动性的阈值。结果活动组所有眼外肌的T_(2)RT均明显高于非活动组及正常对照组,差异有统计学意义(P<0.001)。ROC曲线分析显示,上直肌、下直肌、内直肌、外直肌、眼外肌的最大T_(2)RT判断活动性的截断值分别为80.200 ms、97.045 ms、94.355 ms、85.750 ms、101.385 ms。利用另外15例GO患者进行诊断试验评价,综合评估敏感度、特异度、阳性预测值、阴性预测值较高的指标为下直肌T_(2)RT、眼外肌最大T_(2)RT,其判断活动性的截断值分别为97.045 ms、101.385 ms;敏感度分别为91.7%、93.8%;特异度均为80.0%。结论磁共振T_(2)-mapping序列对评估GO的活动性具有良好价值。临床上可选择测量下直肌T_(2)RT或眼外肌最大T_(2)RT协助评估GO的活动性。

M-Dixon quant和T2-mapping对杜氏肌营养不良症患儿进展前期及进展期运动功能的评估

目的探讨M-Dixon quant和T2-mapping技术评估杜氏肌营养不良症(DMD)患儿进展前期及进展期运动功能的价值,为精确评估DMD病情和监测随访提供合适的影像学工具。方法前瞻性收集40例经基因检测或组织病理活检确诊的DMD男性患儿,进展前期(<7岁)患儿20例,进展期(≥7岁)患儿20例。所有患儿均进行下肢M-Dixon quant和T2-mapping检查,观察进展前期及进展期患儿大腿肌肉的MRI表现,选取右侧大腿14块肌肉并测量其脂肪分数(FF)值和T2值。根据运动功能量表(MFM)评分对患儿进行运动功能评估并计算MFM总评分。采用Pearson相关分析2组患儿各肌肉FF值、T2值与MFM总评分的相关性(rFF,rT2)。结果进展前期DMD患儿大腿肌肉MRI表现为轻微脂肪浸润,随疾病进展表现为脂肪浸润程度加重。进展前期DMD患儿臀大肌、半膜肌、半腱肌FF值与MFM总评分均呈负相关(均P<0.05);除大收肌外,其余13块肌肉T2值与MFM总评分均呈负相关(均P<0.05),以股中间肌与MFM总评分的相关性最强。进展期DMD患儿14块肌肉FF值、T2值与MFM总评分均呈负相关(rFF为-0.877~-0.697,rT2为-0.968~-0.774,均P<0.05),以股外侧肌相关性最强。进展期DMD患儿每块肌肉的rT2均高于rFF。结论DMD患儿大腿肌肉受累有一定的进展规律。无论是进展前期还是进展期,相对于M-Dixon quant技术,T2-mapping技术能够更准确地反映疾病严重程度,疾病进展前期患儿的股中间肌和进展期患儿的股外侧肌T2值可作为评估DMD病情的良好指标。

Pervasive Attentive Neural Network for Intelligent Image Classification Based on N-CDE’s

The utilization of visual attention enhances the performance of image classification tasks.Previous attentionbased models have demonstrated notable performance,but many of these models exhibit reduced accuracy when confronted with inter-class and intra-class similarities and differences.Neural-Controlled Differential Equations(N-CDE’s)and Neural Ordinary Differential Equations(NODE’s)are extensively utilized within this context.NCDE’s possesses the capacity to effectively illustrate both inter-class and intra-class similarities and differences with enhanced clarity.To this end,an attentive neural network has been proposed to generate attention maps,which uses two different types of N-CDE’s,one for adopting hidden layers and the other to generate attention values.Two distinct attention techniques are implemented including time-wise attention,also referred to as bottom N-CDE’s;and element-wise attention,called topN-CDE’s.Additionally,a trainingmethodology is proposed to guarantee that the training problem is sufficiently presented.Two classification tasks including fine-grained visual classification andmulti-label classification,are utilized to evaluate the proposedmodel.The proposedmethodology is employed on five publicly available datasets,including CUB-200-2011,ImageNet-1K,PASCAL VOC 2007,PASCAL VOC 2012,and MS COCO.The obtained visualizations have demonstrated that N-CDE’s are better appropriate for attention-based activities in comparison to conventional NODE’s.

T1 mapping联合胆道-椎旁肌信号强度比预测肝功能的价值

目的探讨钆塞酸二钠增强MRI T1 mapping联合胆道-椎旁肌信号强度比评估肝功能的价值。资料与方法前瞻性收集北京友谊医院2020年8月—2022年2月72例行钆塞酸二钠增强MRI的患者,将患者按照肝功能Child-Pugh分级分为肝功能正常组22例、A级组35例、B+C级组15例。患者均于增强前及增强后20 min扫描T1 mapping序列,测量肝实质的增强前及增强后(T1post)T1弛豫时间,并计算肝脏T1弛豫时间减少率(ΔT1)。在肝胆期图像上测量胆道信号及椎旁肌信号,计算胆道-椎旁肌信号强度比(SIR)。评价医师间测量结果的一致性,观察各指标与肝功能分级的相关性,比较不同组间各指标的差异和诊断效能。结果T1post与肝功能分级呈正相关(r=0.683,P<0.001),随肝功能受损程度加重逐渐增加。ΔT1、SIR、SIR×ΔT1与肝功能分级呈负相关(r=-0.764、-0.729、-0.826,P均<0.001),随肝功能受损程度加重逐渐减小。T1post、ΔT1、SIR、SIR×ΔT1在各肝功能分级组间差异均有统计学意义(F=27.491、55.782、48.410、46.809,P均<0.05);两两比较中,各指标差异均有统计学意义(P<0.05)。各指标中的诊断效能最优者为SIR×ΔT1,其鉴别肝功能正常组与肝硬化组、A级组与B+C级组的曲线下面积分别为0.915、0.984。结论T1post、ΔT1、SIR、SIR×ΔT1与肝功能Child-Pugh分级均有较强的相关性。SIR×ΔT1在各组间鉴别能力更好,可作为定量评估肝功能的影像指标。

Analysis of morphological characteristics of gravels based on digital image processing technology and self-organizing map

A comprehensive understanding of spatial distribution and clustering patterns of gravels is of great significance for ecological restoration and monitoring.However,traditional methods for studying gravels are low-efficiency and have many errors.This study researched the spatial distribution and cluster characteristics of gravels based on digital image processing technology combined with a self-organizing map(SOM)and multivariate statistical methods in the grassland of northern Tibetan Plateau.Moreover,the correlation of morphological parameters of gravels between different cluster groups and the environmental factors affecting gravel distribution were analyzed.The results showed that the morphological characteristics of gravels in northern region(cluster C)and southern region(cluster B)of the Tibetan Plateau were similar,with a low gravel coverage,small gravel diameter,and elongated shape.These regions were mainly distributed in high mountainous areas with large topographic relief.The central region(cluster A)has high coverage of gravels with a larger diameter,mainly distributed in high-altitude plains with smaller undulation.Principal component analysis(PCA)results showed that the gravel distribution of cluster A may be mainly affected by vegetation,while those in clusters B and C could be mainly affected by topography,climate,and soil.The study confirmed that the combination of digital image processing technology and SOM could effectively analyzed the spatial distribution characteristics of gravels,providing a new mode for gravel research.

Robust Multi-Watermarking Algorithm for Medical Images Based on GoogLeNet and Henon Map

The field of medical images has been rapidly evolving since the advent of the digital medical information era.However,medical data is susceptible to leaks and hacks during transmission.This paper proposed a robust multi-watermarking algorithm for medical images based on GoogLeNet transfer learning to protect the privacy of patient data during transmission and storage,as well as to increase the resistance to geometric attacks and the capacity of embedded watermarks of watermarking algorithms.First,a pre-trained GoogLeNet network is used in this paper,based on which the parameters of several previous layers of the network are fixed and the network is fine-tuned for the constructed medical dataset,so that the pre-trained network can further learn the deep convolutional features in the medical dataset,and then the trained network is used to extract the stable feature vectors of medical images.Then,a two-dimensional Henon chaos encryption technique,which is more sensitive to initial values,is used to encrypt multiple different types of watermarked private information.Finally,the feature vector of the image is logically operated with the encrypted multiple watermark information,and the obtained key is stored in a third party,thus achieving zero watermark embedding and blind extraction.The experimental results confirmthe robustness of the algorithm from the perspective ofmultiple types of watermarks,while also demonstrating the successful embedding ofmultiple watermarks for medical images,and show that the algorithm is more resistant to geometric attacks than some conventional watermarking algorithms.

Cryptanalysis of 2D-SCMCI Hyperchaotic Map Based Image Encryption Algorithm

Chaos-based cryptosystems are considered a secure mode of communication due to their reliability.Chaotic maps are associated with the other domains to construct robust encryption algorithms.There exist numerous encryption schemes in the literature based on chaotic maps.This work aims to propose an attack on a recently proposed hyper-chaotic map-based cryptosystem.The core notion of the original algorithm was based on permutation and diffusion.A bitlevel permutation approach was used to do the permutation row-and column-wise.The diffusion was executed in the forward and backward directions.The statistical strength of the cryptosystem has been demonstrated by extensive testing conducted by the author of the cryptosystem.This cryptanalysis article investigates the robustness of this cryptosystem against a chosen-plaintext attack.The secret keys of the cryptosystem were retrieved by the proposed attack with 258 chosen-plain images.The results in this manuscript suggest that,in addition to standard statistical evaluations,thorough cryptanalysis of each newly suggested cryptosystem is necessary before it can be used in practical application.Moreover,the data retrieved is also passed through some statistical analysis to compare the quality of the original and retrieved data.The results of the performance analysis indicate the exact recovery of the original data.To make the cryptosystem useful for applications requiring secure data exchange,a few further improvement recommendations are also suggested.

Dimensionality Reduction Using Optimized Self-Organized Map Technique for Hyperspectral Image Classification

The high dimensionalhyperspectral image classification is a challenging task due to the spectral feature vectors.The high correlation between these features and the noises greatly affects the classification performances.To overcome this,dimensionality reduction techniques are widely used.Traditional image processing applications recently propose numerous deep learning models.However,in hyperspectral image classification,the features of deep learning models are less explored.Thus,for efficient hyperspectral image classification,a depth-wise convolutional neural network is presented in this research work.To handle the dimensionality issue in the classification process,an optimized self-organized map model is employed using a water strider optimization algorithm.The network parameters of the self-organized map are optimized by the water strider optimization which reduces the dimensionality issues and enhances the classification performances.Standard datasets such as Indian Pines and the University of Pavia(UP)are considered for experimental analysis.Existing dimensionality reduction methods like Enhanced Hybrid-Graph Discriminant Learning(EHGDL),local geometric structure Fisher analysis(LGSFA),Discriminant Hyper-Laplacian projection(DHLP),Group-based tensor model(GBTM),and Lower rank tensor approximation(LRTA)methods are compared with proposed optimized SOM model.Results confirm the superior performance of the proposed model of 98.22%accuracy for the Indian pines dataset and 98.21%accuracy for the University of Pavia dataset over the existing maximum likelihood classifier,and Support vector machine(SVM).

Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model

Preserving biodiversity and maintaining ecological balance is essential in current environmental conditions.It is challenging to determine vegetation using traditional map classification approaches.The primary issue in detecting vegetation pattern is that it appears with complex spatial structures and similar spectral properties.It is more demandable to determine the multiple spectral ana-lyses for improving the accuracy of vegetation mapping through remotely sensed images.The proposed framework is developed with the idea of ensembling three effective strategies to produce a robust architecture for vegetation mapping.The architecture comprises three approaches,feature-based approach,region-based approach,and texture-based approach for classifying the vegetation area.The novel Deep Meta fusion model(DMFM)is created with a unique fusion frame-work of residual stacking of convolution layers with Unique covariate features(UCF),Intensity features(IF),and Colour features(CF).The overhead issues in GPU utilization during Convolution neural network(CNN)models are reduced here with a lightweight architecture.The system considers detailing feature areas to improve classification accuracy and reduce processing time.The proposed DMFM model achieved 99%accuracy,with a maximum processing time of 130 s.The training,testing,and validation losses are degraded to a significant level that shows the performance quality with the DMFM model.The system acts as a standard analysis platform for dynamic datasets since all three different fea-tures,such as Unique covariate features(UCF),Intensity features(IF),and Colour features(CF),are considered very well.

Asymmetric image encryption algorithm based on a new three-dimensional improved logistic chaotic map

Based on some analyses of existing chaotic image encryption frameworks and a new designed three-dimensional improved logistic chaotic map(3D-ILM),an asymmetric image encryption algorithm using public-key Rivest–Shamir–Adleman(RSA)is presented in this paper.In the first stage,a new 3D-ILM is proposed to enhance the chaotic behavior considering analysis of time sequence,Lyapunov exponent,and Shannon entropy.In the second stage,combined with the public key RSA algorithm,a new key acquisition mathematical model(MKA)is constructed to obtain the initial keys for the 3D-ILM.Consequently,the key stream can be produced depending on the plain image for a higher security.Moreover,a novel process model(NPM)for the input of the 3D-ILM is built,which is built to improve the distribution uniformity of the chaotic sequence.In the third stage,to encrypt the plain image,a pre-process by exclusive OR(XOR)operation with a random matrix is applied.Then,the pre-processed image is performed by a permutation for rows,a downward modulo function for adjacent pixels,a permutation for columns,a forward direction XOR addition-modulo diffusion,and a backward direction XOR addition-modulo diffusion to achieve the final cipher image.Moreover,experiments show that the the proposed algorithm has a better performance.Especially,the number of pixels change rate(NPCR)is close to ideal case 99.6094%,with the unified average changing intensity(UACI)close to 33.4634%,and the information entropy(IE)close to 8.

T_(2)-mapping鉴别诊断腮腺肿瘤的初步探索

目的:评价T_(2)-mapping成像在腮腺肿瘤鉴别诊断中的价值。方法:回顾性分析2018年6月-2021年7月经病理证实的168例腮腺肿瘤患者的T_(2)-mapping资料,其中良性肿瘤118例,恶性肿瘤50例,并进一步分为多形性腺瘤(n=73)、Warthin瘤(n=29)、基底细胞腺瘤(n=11)和恶性肿瘤4组。测量肿瘤及对侧正常腮腺组织的T_(2)值,计算相对T_(2)(rT_(2))值,分析腮腺肿瘤T_(2)值、rT_(2)值的差异,运用受试者操作特征(ROC)曲线评价T_(2)值、rT_(2)值的诊断价值。结果:腮腺良性肿瘤的T_(2)值、rT_(2)值显著高于恶性肿瘤(P均<0.001),多形性腺瘤的T_(2)值及rT_(2)值最高;Warthin瘤的T_(2)值及rT_(2)值最低。T_(2)值、rT_(2)值鉴别诊断腮腺良性与恶性肿瘤的曲线下面积(AUC)分别为0.715和0.692,T_(2)值及rT_(2)值鉴别诊断多形性腺瘤与Warthin瘤的AUC分别为0.891和0.862。T_(2)值及rT_(2)值鉴别诊断多形性腺瘤与基底细胞腺瘤的AUC分别为0.686和0.664。T_(2)值及rT_(2)值鉴别诊断多形性腺瘤与恶性肿瘤的AUC分别为0.836和0.799。T_(2)值鉴别诊断Warthin瘤与基底细胞腺瘤的AUC为0.865。结论:T_(2)-mapping有助于鉴别诊断腮腺肿瘤。

Fractal Image Compression Using Self-Organizing Mapping

One of the main disadvantages of fractal image data compression is a loss time in the process of image compression (encoding) and conversion into a system of iterated functions (IFS). In this paper, the idea of the inverse problem of fixed point is introduced. This inverse problem is based on collage theorem which is the cornerstone of the mathematical idea of fractal image compression. Then this idea is applied by iterated function system, iterative system functions and grayscale iterated function system down to general transformation. Mathematical formulation form is also provided on the digital image space, which deals with the computer. Next, this process has been revised to reduce the time required for image compression by excluding some parts of the image that have a specific milestone. The neural network algorithms have been applied on the process of compression (encryption). The experimental results are presented and the performance of the proposed algorithm is discussed. Finally, the comparison between filtered ranges method and self-organizing method is introduced.

An Object-based Approach for Two-level Gully Feature Mapping Using High-resolution DEM and Imagery： A Case Study on Hilly Loess Plateau Region, China

Gully feature mapping is an indispensable prerequisite for the motioning and control of gully erosion which is a widespread natural hazard. The increasing availability of high-resolution Digital Elevation Model(DEM) and remote sensing imagery, combined with developed object-based methods enables automatic gully feature mapping. But still few studies have specifically focused on gully feature mapping on different scales. In this study, an object-based approach to two-level gully feature mapping, including gully-affected areas and bank gullies, was developed and tested on 1-m DEM and Worldview-3 imagery of a catchment in the Chinese Loess Plateau. The methodology includes a sequence of data preparation, image segmentation, metric calculation, and random forest based classification. The results of the two-level mapping were based on a random forest model after investigating the effects of feature selection and class-imbalance problem. Results show that the segmentation strategy adopted in this paper which considers the topographic information and optimal parameter combination can improve the segmentation results. The distribution of the gully-affected area is closely related to topographic information, however, the spectral features are more dominant for bank gully mapping. The highest overall accuracy of the gully-affected area mapping was 93.06% with four topographic features. The highest overall accuracy of bank gully mapping is 78.5% when all features are adopted. The proposed approach is a creditable option for hierarchical mapping of gully feature information, which is suitable for the application in hily Loess Plateau region.

MARKER-BASED MAPPING BETWEEN MEDICAL IMAGE AND ROBOT SPACE IN ROBOT-ASSISTED SURGERY

This paper focuses on the problems of matching a virtual and a real environments by means of hardware and software tools. The real space is represented by a patient’s bone where a set of cuts by means of robot system is to be made. The virtual space is a 3D model of the bone reconstructed from a set of CT slices. Robot system is then not only to machine bones but also to perform the fundamental step of registration between the two spaces. An external force sensor is used to adjust robot stiffness in order to perform the tactile searching necessary for the registration. A simple but reliable software algorithm is used to control the robot for matching between medical image and robot space in robot-assisted surgery. The results show the system proposed is precise enough for application, and tests been made also clarify the way to improve it.

Image Motion Compensation of Off-Axis TMA Three-Line ArrayAerospace Mapping Cameras

To enhance the image motion compensation accuracy of off-axis three-mirror anastigmatic( TMA)three-line array aerospace mapping cameras,a new method of image motion velocity field modeling is proposed in this paper. Firstly,based on the imaging principle of mapping cameras,an analytical expression of image motion velocity of off-axis TMA three-line array aerospace mapping cameras is deduced from different coordinate systems we established and the attitude dynamics principle. Then,the case of a three-line array mapping camera is studied,in which the simulation of the focal plane image motion velocity fields of the forward-view camera,the nadir-view camera and the backward-view camera are carried out,and the optimization schemes for image motion velocity matching and drift angle matching are formulated according the simulation results. Finally,this method is verified with a dynamic imaging experimental system. The results are indicative of that when image motion compensation for nadir-view camera is conducted using the proposed image motion velocity field model,the line pair of target images at Nyquist frequency is clear and distinguishable. Under the constraint that modulation transfer function( MTF) reduces by 5%,when the horizontal frequencies of the forward-view camera and the backward-view camera are adjusted uniformly according to the proposed image motion velocity matching scheme,the time delay integration( TDI) stages reach 6 at most. When the TDI stages are more than 6,the three groups of camera will independently undergo horizontal frequency adjustment. However, when the proposed drift angle matching scheme is adopted for uniform drift angle adjustment,the number of TDI stages will not exceed 81. The experimental results have demonstrated the validity and accuracy of the proposed image motion velocity field model and matching optimization scheme,providing reliable basis for on-orbit image motion compensation of aerospace mapping cameras.

Diffusion-weighted imaging and variable flip angle T1 mapping: a supplement for image-guided biopsy in follow-up analysis of liver fibrosis

Purpose To evaluate the performance of diffusion-weighted imaging(DWI) and variable flip angle(VFA) T1 mapping as a supplement to image-guided biopsy in follow-up analysis of liver fibrosis. Materials and Methods This prospective study was approved by the institution's committee on human research, and written informed consent was provided from the enrolled patients. We investigated five MRI parameters of DWI and VFA T1 mapping, collected from 11 patients who underwent serial ultrasound image-guided biopsy with follow-up MRI within 1.5 years after treatment for liver fibrosis/cirrhosis. For each patient, four consecutive MRI examinations were conducted, including baseline MRI before treatment and three follow-up MRI examinations after treatment at each 0.5-year interval. ADC values at four b values and T1 relaxation times were correlated to pathology-confirmed liver fibrosis stages, which were subsequently divided into two groups, stages F2–3 and F4. The receiver operating characteristic(ROC) analysis and repeated measurement analysis of variance were used for statistical analysis. Results Among these ADC parameters, ADC value(b = 500 s/mm^2) was the most consistent in differentiating between stage F2–3 and F4 liver fibrosis. Repeated measurement analysis showed that the intra-group and inter-group differences were 0.447 and 0.024, respectively. T1 relaxation time could not consistently differentiate between the F2–3 and F4 groups; however, it was repeatable, and the intra-group and inter-group differences were 0.410 and 0.042, respectively. Conclusion MRI-ADC value at a b value of 500 s/mm^2 can be a promising biomarker for differentiating stages F2–3 and F4 liver fibrosis. A combination of this biomarker with repeatable T1 relaxation time may function as a non-invasive tool for follow-up liver fibrosis in patients who reject repeated image-guided biopsy.

LAND-COVER DENSITY-BASED APPROACH TO URBAN LAND USE MAPPING USING HIGH-RESOLUTION IMAGERY

Nowadays, remote sensing imagery, especially with its high spatial resolution, has become an indispensable tool to provide timely up-gradation of urban land use and land cover information, which is a prerequisite for proper urban planning and management. The possible method described in the present paper to obtain urban land use types is based on the principle that land use can be derived from the land cover existing in a neighborhood. Here, moving window is used to represent the spatial pattern of land cover within a neighborhood and seven window sizes (61m×61m, 68m×68m, 75m×75m, 87m×87m, 99m×99m, 110m×110m and 121m×121m) are applied to determining the most proper window size. Then, the unsupervised method of ISODATA is employed to classify the layered land cover density maps obtained by the moving window. The results of accuracy evaluation show that the window size of 99m×99m is proper to infer urban land use categories and the proposed method has produced a land use map with a total accuracy of 85%.