数字化可视人体的构建技术与应用研究

数字化可视人体，或称虚拟可视人体，是根据人体解剖学研究的全部数据综合构成的一系列数字化三维图像，而且可以虚拟地进行人体的一切生理活动。这种数字化可视人体可用于进行医学教学、模拟临床手术和放射治疗等，效果显著。本文阐述了制作方法和主要用途。     

VPN技术在数字电影传输中的应用

随着电影数字化技术的发展,计算机网络安全技术中的虚拟专用网(VPN,vitual private network)技术可利用公共通信网络(如因特网)实现影片数据安全的保密的通信,只需要将进行影片传输的两个端点均连接在公共通信网上,当需要进行影片数据传输时,通过端点上的VPN设备在公共网上建立一条虚拟的专用通信通道,就可以将所有影片数据均经过加密后再在网上传输,同时可确保机密数据的安全传输.     

数字人的来龙去脉——钟世镇、唐雷访谈

钟世镇院士是我国着名的解剖学家,1925年9月出生。中国工程院院士、教授。他的主要学术成就,是建立了以解决临床外科发展需要的应用解剖学研究体系,开展了工医结合的生物力学的研究。2001年和2003年作为执行主席,主持了香山科学会议第174次和208次主题为"中国数字化虚拟人体"的研讨会,参加了"863"项目有关"数字化人体"的两项课题,担任"中国数字人研究联结组组长"。他在高层次人才培养方面,建立了有推广价值的"人体解剖学跨学科培养外科博士新模式"。他在人体管道铸型标本制作方法研究上也有创新性成就,建成了一个享誉国际的"南方医科大学人体标本陈列馆"。     

基于深度图像动作捕捉技术虚拟主持人的应用研究

本项目应用基于深度图像的运动捕捉技术,针对运动捕捉到的人体深度图像对人体运动特征进行实时提取,实时分析人体骨骼节点的运动状态,与虚拟人物相应的骨骼节点绑定,以实时地控制虚拟主持人的运动状态和人脸表情,把虚拟主持人放置在真三维虚拟演播环境中,使其与整个虚拟环境场景融合,并可以通过手势识别来控制虚拟物体(比如虚拟屏幕或虚拟道具等)。     

基于明墓出土文物资料的数字化女性人体模型复原——以李英姑为例

传统服饰的复原不仅涉及服装本身的形制结构、纹案、织物展现，还包括在真实人体穿着状态下的展现。而目前在对墓葬出土的服饰文物复原工作中，涉及的墓主人人体尺寸数据问题，缺乏相关的资料记载和在经过时间的流逝下已经很难获取墓主人较为准确的尺寸信息。因此在服饰复原的过程中，对服饰文物的真实展现成为了当下一个棘手的问题。以江西南城益宣王朱翊鈏夫妇合葬墓中的李英姑棺为例，结合出土文物简报中所提供数据进行推导，通过查找相关文物资料，研究人体部位之间的线性关系，从而为求得墓主人数据得到合理的推导。计算出人体各部位之间相对应的数值，进而求出人体数据的数值范围，得出人体数据平均值，最后对虚拟模特的身体数据进行调整，得到了更加真实的人体模型，在此基础上利用CLO3D软件将服饰进行复原，可以呈现出一种更加真实、贴切的服装穿着效果，为传统服饰的数字化研究提供新的思路。     

英国电影市场再掀数字化发展浪潮

近日,英国第二大独立电影院线 Reel Cinemas 和欧洲数字电影集成商艺术联盟媒体(Arts Alliance Media,简称 AAM)签订了基于虚拟拷贝费(VPF)的影院数字化改造协议.     

传统陶瓷艺术作品的三维数字化重建及应用的研究与实践

目的 研究获取传统陶瓷艺术作品完整彩色点云数据,并高效进行三维重建的技术方法,探索陶艺作品数字内容虚拟展示平台的构建及推广利用的有效途径.方法 采用拍照式三维扫描设备采集陶艺作品的三维彩色点云数据,研究并提出了融合、修补等后处理的新方法,得到完整的优化彩色点云信息.通过网格化处理获得陶瓷艺术品的高保真三维网格模型和简化网格,利用虚拟现实技术开发数字化展示平台.结果 获取了石湾陶瓷传统艺术作品的三维彩色点云数据,完成了点云优化、修复,生成了高精度的彩色网格模型并进行了数字化的推广与宣传.结论 提出的三维重建技术和虚拟展示方法为我国陶瓷艺术作品的保护、传承与推广作出了积极的探索,为利用现代科技进行文化遗产数字化保护作出了新的尝试.     

军用电子装备结构数字化设计与制造标准研究

国内三维数字化技术的发展,推动了企业数字化设计与制造技术的广泛应用,对数字化设计与制造标准的需求也越来越强烈。文中介绍了数字化设计与制造领域国内外相关标准的研究现状,并结合军用电子装备的研制特点,针对三维CAD数据集、三维建模、虚拟装配和数字样机等方面开展标准研究,通过该系列标准研究,总结和归纳了标准研究的一般方法,为该系列标准的发布和应用奠定了基础。     

欢迎订阅《包装工程》杂志

<正>邮发代号:78-30(半月刊)《包装工程》杂志1980年创刊,是国内外公开发行的全国印刷包装技术领域权威性科技期刊,连续六版全国中文核心期刊、中国科学引文数据库(CSCD)源刊、中国学术期刊综合评价数据库来源期刊、《中国期刊网》用刊、《中国学术期刊(光盘版)》用刊、"万方数据——数字化期刊群"入编刊物、"中文科技期刊数据     

牙科椅不同俯仰角度的人体舒适度研究

目的通过研究牙科椅俯仰角度的变化与人体舒适度之间的关系,为改善牙科椅的设计提供有力的参考依据。方法测试不同俯仰角度下人体主观舒适度,基于CATIA(交互式CAD/CAE/CAM系统)人机工程设计与分析模块,在导入的牙科椅模型中引入三维人体模型,建立虚拟人机关系,结合人机工程学理论,对不同俯仰角度下人体姿态的舒适度进行仿真分析,利用SPSS统计分析软件对所得的数据处理。结论牙科椅俯仰角度从0°转到60°的过程中,人体舒适度分值呈先上升后下降的趋势。人体最舒适的牙科椅俯仰角度为30°,最不舒适的角度为0°,同时验证了CATIA人机工程设计与分析的可行性。     

Modified Global Flower Pollination Algorithm‐based image fusion for medical diagnosis using computed tomography and magnetic resonance imaging

Recently, the computed tomography (CT) and magnetic resonance imaging (MRI) medical image fusion have turned into a challenging issue in the medical field. The optimal fused image is a significant component to detect the disease easily. In this research, we propose an iterative optimization approach for CT and MRI image fusion. Initially, the CT and MRI image fusion is subjected to a multilabel optimization problem. The main aim is to minimize the data and smoothness cost during image fusion. To optimize the fusion parameters, the Modified Global Flower Pollination Algorithm is proposed. Here, six sets of fusion images with different experimental analysis are evaluated in terms of different evaluation metrics such as accuracy, specificity, sensitivity, SD, structural similarity index, feature similarity index, mutual information, fusion quality, and root mean square error (RMSE). While comparing to state‐of‐art methods, the proposed fusion model provides best RMSE with higher fusion performance. Experiments on a set of MRI and CT images of medical data set show that the proposed method outperforms a very competitive performance in terms of fusion quality.     

Survey and analysis of various image fusion techniques for clinical CT and MRI images

The research and development of biomedical imaging techniques requires more number of image data from medical image acquisition devices, like computed tomography (CT), magnetic resonance imaging (MRI), positron emission technology, and single photon emission computed tomography. Multimodal image fusion is the process of combining information from various images to get the maximum amount of content captured by a single image acquisition device at different angles and different times or stages. This article analyses and compares the performance of different existing image fusion techniques for the clinical images in the medical field. The fusion techniques compared are simple or pixel‐based fusion, pyramid‐based fusion, and transform‐based fusion techniques. Four set of CT and MRI images are used for the above fusion techniques. The performance of the fused results is measured with seven parameters. The experimental results show that out of seven parameters the values of four parameters, such as average difference, mean difference, root mean square error, and standard deviation are minimum and the values of remaining three parameters, such as peak signal to noise ratio, entropy, and mutual information are maximum. From the experimental results, it is clear that out of 14 fusion techniques taken for survey, image fusion using dual tree complex wavelet transform gives better fusion result for the clinical CT and MRI images. Advantages and limitations of all the techniques are discussed with their experimental results and their relevance. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 193–202, 2014.     

Fusion of MRI and CT images using guided image filter and image statistics

In medical imaging using different modalities such as MRI and CT, complementary information of a targeted organ will be captured. All the necessary information from these two modalities has to be integrated into a single image for better diagnosis and treatment of a patient. Image fusion is a process of combining useful or complementary information from multiple images into a single image. In this article, we present a new weighted average fusion algorithm to fuse MRI and CT images of a brain based on guided image filter and the image statistics. The proposed algorithm is as follows: detail layers are extracted from each source image by using guided image filter. Weights corresponding to each source image are calculated from the detail layers with help of image statistics. Then a weighted average fusion strategy is implemented to integrate source image information into a single image. Fusion performance is assessed both qualitatively and quantitatively. Proposed method is compared with the traditional and recent image fusion methods. Results showed that our algorithm yields superior performance.     

Fuzzy clustering-based image segmentation techniques used to segment magnetic resonance imaging/computed tomography scan brain tissues: Comparative analysis

Medical images are obtained with computer-aided diagnosis using electronic devices such as CT scanners and MRI machines. The captured computed tomography (CT)/magnetic resonance imaging (MRI) images typically have limited spatial resolution, low contrast, noise and nonuniform variability in intensity due to environmental effects. Therefore, the distinctions of the objects are blurred, distorted and the meanings of the objects are not quite precise. Fuzzy sets and fuzzy logic are best suited for addressing vagueness and ambiguity. Fuzzy clustering technique has been commonly used for segmentation of images throughout the last decade. This study presents a comparative study of 14 fuzzy-clustered image segmentation algorithms used in the CT scan and MRI brain image segments. This study used 17 data sets including 4 synthetic data sets, namely, Bensaid, Diamond, Square, and its noisy version, 5 real-world digital images, and 8 CT scan/MRI brain images to analyze the algorithms. Ground truth images are used for qualitative analysis. Apart from the qualitative analysis, the study also quantitatively evaluated the methods using three validity metrics, namely, partition coefficient, partition entropy, and Fukuyama-Sugeno. After a thorough and careful review of the results, it is observed that extension of the fuzzy C-means (EFCM) outperformed every other image segmentation algorithm, even in a noisy environment, followed by kernel-based FCM σ, the output of which is also very good after EFCM.     

Precise image fusion standardization for separated modalities using dedicated multimodal heart phantom

The standardization of images derived from different medical modalities should be ensured when image fusion brings essential information and hybrid scanners are not available. The aim of this study was to show that precise image fusion standardization can be obtained using special and unique multimodal heart phantom (MHP) which is compatible with all applied diagnostic methods. MHP was designed and constructed according to International Commission on Radiological Protection reports, scanners requirements and personal experience. Three different types of acquisitions were done: physiological perfusion, myocardial ischaemia and intestines artefacts. The measurements were done using different modalities (single photon emission computed tomography (SPECT), positron emission tomography (PET), MRI, CT) as well as hybrid scanners (PET/CT, SPECT/CT). It was shown that MHP can be used not only for improvement of image fusion standardization protocol but also for verification of proper implementation of the fusion protocol in hybrid scanners.     

CNMAN: a Chinese adult male voxel phantom constructed from color photographs of a visible anatomical data set

A voxel phantom of Chinese adult male called CNMAN was constructed from color photographs of the first Chinese visible human data set, for radiation protection purpose. This data set was obtained from a 35-y-old Chinese male cadaver by a medical university in China. The man, 170 cm in height and 65 kg in weight, was dead without any pathological changes. The image size for transversal anatomical photographs of the whole body was 3072 x 2048. After the photographs were semi-automatically segmented, the voxel phantom (CNMAN) with a voxel size of 0.16 mm x 0.16 mm x 1 mm, consisting of 29 tissues or organs was constructed. Combined with the MCNP Monte Carlo transport code, preliminary results for radiation protection dosimetry were obtained based on this Chinese voxel phantom.     

基于特征能量加权的红外与可见光图像融合

目前红外与可见光图像直接融合存在红外目标取舍和场景信息提取困难,结合非采样Contourlet的多尺度、多方向性和平移不变性的优点,本文提出了一种基于非采样Contourlet变换(NSCT)的红外与可见光图像融合方法.首先对源图像进行分解,然后低频子带通过构造基于区域的特征像素能量,进行加权融合,高频子带直接选用方差取大法融合.使用该算法进行了融合实验,并给出了融合质量评价.实验结果表明,本文提出的基于NSCT的图像融合算法在保留图像细节信息、增加信息量方面都有显著地提高.     

多网络联合的红外与可见光图像融合算法研究

目的针对红外与可见光图像在融合过程中,融合图像失真以及可见光图像信息融合不足的问题,提出一种联合多网络结构的红外与可见光图像融合算法。方法首先采用基于密集残差连接的编码器对输入的红外与可见光图像进行特征提取,然后利用融合策略对得到的特征图进行融合,最后将融合后的特征图送入基于GAN网络的解码器中。结果通过与可见光图像对抗优化训练,使得融合后的图像保留了更多可见光图像的细节、背景信息,增强了图像的视觉效果。结论实验表明,与现有的融合算法相比,该算法达到了更好的实验效果,在主观感知和客观评价上都具有更好的表现力。     

A curvelet transform approach for the fusion of MR and CT images

There are several medical imaging techniques such as the magnetic resonance (MR) and the computed tomography (CT) techniques. Both techniques give sophisticated characteristics of the region to be imaged. This paper proposes a curvelet based approach for fusing MR and CT images to obtain images with as much detail as possible, for the sake of medical diagnosis. This approach is based on the application of the additive wavelet transform (AWT) on both images and the segmentation of their detail planes into small overlapping tiles. The ridgelet transform is then applied on each of these tiles, and the fusion process is performed on the ridgelet transforms of the tiles. Simulation results show the superiority of the proposed curvelet fusion approach to the traditional fusion techniques like the multiresolution discrete wavelet transform (DWT) technique and the principal component analysis (PCA) technique. The fusion of MR and CT images in the presence of noise is also studied and the results reveal that unlike the DWT fusion technique, the proposed curvelet fusion approach doesn't require denoising.     

Compatible abnormality detection technique for CT and MRI brain images

Abstract

An automated computerised tomography (CT) and magnetic resonance imaging (MRI) brain images are used to perform an efficient classification. The proposed technique consists of three stages, namely, pre-processing, feature extraction and classification. Initially, pre-processing is performed to remove the noise from the medical MRI images. Then, in the feature extraction stage, the features that are related with MRI and CT images are extracted and these extracted features which are given to the Feed Forward Back-propagation Neural Network (FFBNN) is exploited in order to classify the brain MRI and CT images into two types: normal and abnormal. The FFBNN is well trained by the extracted features and uses the unknown medical brain MRI images for classification in order to achieve better classification performance. The proposed method is validated by various MRI and CT scan images. A classification with an accomplishment of 96% and 70% has been obtained by the proposed FFBNN classifier. This achievement shows the effectiveness of the proposed brain image classification technique when compared with other recent research works.