Generating useful images for medical applications from the Visible Korean Human

For the Visible Korean Human (VKH), a male cadaver was serially ground off to acquire the serially sectioned images (SSIs) of a whole human body. Thereafter, more than 700 structures in the SSIs were outlined to produce detailed segmented images; the SSIs and segmented images were volume- and surface-reconstructed to create three-dimensional models. For outlining and reconstruction, popular software (Photoshop, MRIcro, Maya, AutoCAD, 3ds max, and Rhino) was mainly used; the technique can be reproduced by other investigators for creating their own images. For refining the segmentation and volume reconstruction, the VOXEL-MAN system was used. The continuously upgraded technique was applied to a female cadaver's pelvis to produce the SSIs with 0.1mm sized intervals and 0.1mm x 0.1mm sized pixels. The VKH data, distributed worldwide, encouraged researchers to develop virtual dissection, virtual endoscopy, and virtual lumbar puncture contributing to medical education and clinical practice. In the future, a virtual image library including all the Visible Human Project data, Chinese Visible Human data, and VKH data will hopefully be established where users will be able to download one of the data sets for medical applications.     

A software implementation for detailed volume conductor modelling in electrophysiology using finite difference method

There is an evolving need for new information available by employing patient tailored anatomically accurate computer models of the electrical properties of the human body. Because construction of a computer model can be difficult and laborious to perform sufficiently well, devised models have varied greatly in the level of anatomical accuracy incorporated in them. This has restricted the validity of conducted simulations. In the present study, a versatile software package was developed to transform anatomic voxel data into accurate finite difference method volume conductor models conveniently and in a short time. The package includes components for model construction, simulation, visualisation and detailed analysis of simulation output based on volume conductor theory. Due to the methods developed, models can comprise more anatomical details than the prior computer models. Several models have been constructed, for example, a highly detailed 3-D anatomically accurate computer model of the human thorax as a volume conductor utilising the US National Library of Medicine's (NLM) Visible Human Man (VHM) digital anatomy data. Based on the validation runs the developed software package is readily applicable in analysis of a wide range of bioelectric field problems.     

Web interface-driven cooperative exception handling in ADOME workflow management system

Exception handling in workflow management systems (WFMSs) is a very important problem since it is not possible to specify all possible outcomes and alternatives. Effective reuse of existing exception handlers can greatly help in dealing with workflow exceptions. On the other hand, cooperative support for user-driven computer supported resolution of unexpected exceptions and workflow evolution at run-time is vital for an adaptive WFMS. We have been developing ADOME-WFMS as a comprehensive framework in which the problem of workflow exception handling can be adequately addressed. In this article, we present an adaptive exception manager and its web-based interface for ADOME-WFMS with procedures for supporting the following: reuse of exception handlers, thorough and automated resolution of expected exceptions, effective management of Problem Solving Agents, cooperative exception handling, user-driven computer supported resolution of unexpected exceptions, and workflow evolution.     

Mangrove above-ground carbon stock mapping of multi-resolution passive remote-sensing systems

This research address several issues related to mangrove above-ground carbon stock (AGC) mapping using the integration of remote-sensing images and field data. These issues are (1) remote-sensing image availability for specific mangrove AGC mapping scale and precision, (2) the impact on mangrove AGC modelling due to the difference between images spatial resolution and plot size of field mangrove AGC measurement, which follow the standardized procedure and not specially developed to be integrated with remote-sensing data, and (3) the accuracy of performing mangrove AGC mapping using image at different spatial resolutions using similar field size mangrove AGC data. Four multispectral data sets, namely Worldview-2, Advanced Land Observation System Advanced Visible and Near-Infrared Radiometer-2 (ALOS AVNIR-2), Advanced Spectral and Thermal Radiometer (ASTER) Visible Near-Infrared (VNIR) and Landsat 8 Operational Land Imager (OLI), and a Hyperion hyperspectral image were tested for their performance for mangrove AGC mapping. These images represent various spatial, spectral, and radiometric resolutions of remote-sensing data available to date. The mapping was performed using their original spatial resolution, and for Worldview-2 the mapping was also conducted using 10 m spatial resolution. Image radiometric corrections, vegetation indices, principal component analysis and minimum noise fraction were applied to each image. These were used as input in the empirical modelling of mangrove AGC. The results indicate that (1) it is not possible to perform empirical modelling of mangrove AGC using image with sub-canopy spatial resolution, (2) decreasing the spatial resolution may be beneficial to obtaining a significant correlation with mangrove AGC, and (3) it is possible to perform empirical modelling of mangrove AGC mapping using field data not specially intended to be integrated with remote-sensing data, along with some adjustments. This opens up the possibility of utilizing the available field mangrove AGC data collected by stakeholders, that is, government institutions, NGOs, academics, private sector, to assist mangrove AGC mapping across the nation.     

The Visible Human<Superscript>®</Superscript> at the University of Colorado 15 years later

The Visible Human has come through ages, providing a foundation of photorealistic anatomy for learner-centered, interactive education. Pathways for improvement of the Visible Human process for reverse engineering the macrostructure of the human body have been developed to provide higher resolution and decreased production time for segmentation and modeling human form. The assignment of physical properties, the development of algorithms for the interaction of surgical tools with this virtual anatomy and the availability of high-fidelity haptic interfaces provide the basis for fully immersive surgical training and certification in an environment that is zero direct-risk to patients. Interactive journal publishing, 3D stereoscopic anatomical visualization software and surgical simulators, all based on the Visible Human, the history of the Project and its utilization and provide a framework for its evolution and role in delivering education, training, certification and credentialing through virtual reality to the health care workforce of tomorrow.

A simple method of rapid and automatic color image segmentation for serialized Visible Human slices

In this paper, a rapid and automatic color image segmentation method for the serialized slices of the Visible Human is proposed. The main strategy is based on region growing and pixel color difference. A rapid color similarity computing method is improved and applied for classifying different pixels. An algorithm based on corrosion from four directions is proposed to automatically extract the seed points for the serialized slices. Utilizing this method, the color slice images of the Visible Human body can be segmented in series automatically. Also, the multithreading frame of parallel computing is introduced in the entire segmentation process. This method is simple but rapid and automatic. The primary organs of the Visible Human can be segmented clearly and accurately. The 3D models of these organs after 3D reconstruction are satisfactory. This novel method can provide support to the Visible Human research.     

数字人体-人体形态和机能计算机仿真百年计划

数字人是在信息时代兴起的跨学科新兴科技领域。是利用计算机技术对人体的形态和机能的仿真。数字人研究分为三个级别：微观(分子、基因、细胞)、中观(组织、器官)、宏观(全身)。数字人研究始于美国国立医学图书馆支持的可视人项目(1991年立项)。2001年美国科学家联盟提出数字人计划，得到国立卫生院承认，美国众议院要求各个政府部门使用数字人概念。我国数字人研究从2001年开始得到863立项，已经取得2个全身断面数字化实验数据集。2002年人类基因组计划提出者DeLiee教授提出为期100年的虚拟人计划建议。基因组研究和数字人研究找到结合点。未来将成为数字人研究的底层。该文回顾了数字人最新研究进展和成果。     

中国虚拟人数据的配准算法

20世纪末出现的基于人体大量信息融合分析的精细化医疗技术,将完整人体的数字化信息和计算机技术相结合,使得基于实际人体信息的模拟和计算成为可能;到目前为止,中国虚拟人的研究刚刚开始。针对中国虚拟人数据的特点,提出了一种图像配准的新方法。根据图像的属性和用途,就配准的各个环节采用合理可行的方法,包括仿射变换、PV插值技术、均方差相似性测量、Powell优化、全局匹配方法。改进了算法流程框架,给出了由参考图像空间到浮动图像空间的逆向几何变换,避免了潜在的“空洞”问题。该算法流程框架将各个部分有机地结合到一起,高效、有序地对中国虚拟人数据图像进行配准。用该配准方法处理中国虚拟人数据,取得了令人满意的效果。     

An attempt for coloring multichannel MR imaging data

This is an elementary research into assigning color values to voxels of multi-channel magnetic resonance imaging (MRI) volume data. The MRI volume data sets obtained under different scanning conditions are transformed into components by independent component analysis (ICA), which enhances the physical characteristics of the tissue. The transfer functions for generating color values from the independent components are obtained by using a radial basis function network, a kind of neural net, by training the network with sample data chosen from the Visible Human female data set (VHF). The resultant color volume data sets correspond well with the full-color cross-sections of the Visible Human data sets     

Multimod Data Manager: a tool for data fusion

Nowadays biomedical engineers regularly have to combine data from multiple medical imaging modalities, biomedical measurements and computer simulations and this can demand the knowledge of many specialised software tools. Acquiring this knowledge to the depth necessary to perform the various tasks can require considerable time and thus divert the researcher from addressing the actual biomedical problems. The aim of the present study is to describe a new application called the Multimod Data Manager, distributed as a freeware, which provides the end user with a fully integrated environment for the fusion and manipulation of all biomedical data. The Multimod Data Manager is generated using a software application framework, called the Multimod Application Framework, which is specifically designed to support the rapid development of computer aided medicine applications. To understand the general logic of the Data Manager, we first introduce the framework from which it is derived. We then illustrate its use by an example--the development of a complete subject-specific musculo-skeletal model of the lower limb from the Visible Human medical imaging data to be used for predicting the stresses in the skeleton during gait. While the Data Manager is clearly still only at the prototype stage, we believe that it is already capable of being used to solve a large number of problems common to many biomedical engineering activities.     

Assessment of MODIS sun-sensor geometry variations effect on observed NDVI using MSG SEVIRI geostationary data

The quality of Earth observation (EO) based vegetation monitoring has improved during recent years, which can be attributed to the enhanced sensor design of new satellites such as MODIS (Moderate Resolution Imaging Spectroradiometer) on Terra and Aqua. It is however expected that sun-sensor geometry variations will have a more visible impact on the Normalized Difference Vegetation Index (NDVI) from MODIS compared to earlier data sources, since noise related to atmosphere and sensor calibration is substantially reduced in the MODIS data stream. For this reason, the effect of varying MODIS viewing geometry on red, near-infrared (NIR) and NDVI needs to be quantified. Data from the geostationary MSG (Meteosat Second Generation) SEVIRI (Spinning Enhanced Visible and Infrared Imager) sensor is well suited for this purpose due to the fixed position of the sensor, the spectral resolution, including a red and NIR band, and the high temporal resolution (15 min) of data, enabling MSG data to be used as a reference for estimating MODIS surface reflectance and NDVI variations caused by varying sun-sensor geometry. The study was performed on data covering West Africa for periods of lowest possible cloud cover for three consecutive years (2004–2006). An analysis covering the entire range of NDVI revealed day-to-day variations in observed MODIS NDVI of 50–60% for medium dense vegetation (NDVI ≈ 0.5) caused by variations in MODIS view zenith angles (VZAs) between nadir and the high forward-scatter view direction. Statistical analysis on red, NIR and NDVI from MODIS and MSG SEVIRI for three transects (characterized by different vegetation densities) showed that both MODIS red and NIR reflectances are highly dependant on MODIS VZA and relative azimuth angle (RAA), due to the anisotropic behaviour of red and NIR reflectances. The anisotropic reflectance in the red and NIR band was to some degree minimized by the ratioing properties of NDVI. The minimization by the NDVI normalization is very dependent on the vegetation density however, since the degree of anisotropy in red and NIR reflectances depends on the amount of vegetation present. MODIS VZA and RAA effects on NDVI were highest for medium dense vegetation (NDVI ≈ 0.5–0.6). The VZA and RAA effects were less for sparsely vegetated areas (NDVI ≈ 0.3–0.35) and the smallest effect on NDVI was found for dense vegetation (NDVI ≈ 0.7). These results have implications for the end users' interpretation of NDVI, and challenge the expediency of the MODIS NDVI compositing technique, which should be refined to distinguish between forward- and backward-scatter viewing direction by taking RAA into account.     

Object-based fusion for urban tree species classification from hyperspectral,panchromatic and nDSM data

This study aims at identifying the best object-based fusion strategy that takes advantage of the complementarity of several heterogeneous airborne data sources for improving the classification of 15 tree species in an urban area (Toulouse, France). The airborne data sources are: hyperspectral Visible Near-Infrared (160 spectral bands, spatial resolution of 0.4 m) and Short-Wavelength Infrared (256 spectral bands, 1.6 m), panchromatic (14 cm), and a normalized Digital Surface Model (12.5 cm). Object-based feature and decision level fusion strategies are proposed and compared when applied to a reference site where the species are previously identified during ground truth collection. This allows the best fusion strategy to be selected with a view to introducing the method in an automatic process (tree crown delineation and species classification) on a test site, independent of the reference site used for learning. In particular, a decision level fusion is selected: based on the Support Vector Machine algorithm, Visible Near-Infrared and Short-Wavelength Infrared classifications use Minimum Noise Fraction components at the original spatial resolution, whereas panchromatic and normalized Digital Surface Model classifications use, respectively, Haralick’s and structural features computed at the object scale. After the computation of a decision profile for each source at the object level based on the classification algorithms’ membership probabilities, these decision profiles are combined and a decision rule is applied to predict the species. Focusing on the reference site, the Visible Near-Infrared exhibits the best performances with F-score values higher than 60% for 13 species out of 15. The Short-Wavelength Infrared is the most powerful for three species with F-score greater than 60% for seven common species with the Visible Near-Infrared. The panchromatic and normalized Digital Surface Model contribute marginally. The best fusion strategy (decision fusion) does not improve significantly the overall accuracy with 77% (kappa = 74%) against 75% (kappa = 72%) for the Visible Near-Infrared but in general, it improves the results for cases where complementarities have been observed. When applied to the test site and assessed for the two majority species (Tilia tomentosa and Platanus x hispanica), the selected approach gives consistent results with an overall accuracy of 63% against 55% for the Visible Near-Infrared.     

基于堆叠沙漏网络的人体姿态估计

人体姿态估计在许多计算机视觉任务中起着重要的作用,然而,由于姿态的多变、光照、遮挡和分辨率低等因素,它仍然是一个具有挑战性的问题.利用深层卷积神经网络的高级语义信息是提高人体姿态估计精度的有效途径,本文提出了一种改进的堆叠沙漏网络,设计了一个大感受野残差模块和预处理模块来更好地获得人体结构特征,以此获得丰富的上下文信息,对部分遮挡、大姿态变化、复杂背景等有较好的效果,此外,还对不同阶段的结果进行了融合,以进一步提高定位精度,在MPII数据集和LSP数据集上对本文提出的模型进行实验和验证,结果证明了本文模型的有效性.     

Land cover 2.0

Interest in knowing more about the Earth’s land cover and how it has changed over time motivated the mission and sensor design of early terrestrial remote sensing systems. Rapid developments in computer hardware and software in the last four decades have greatly increased the capacity for satellite data acquisition, downlink, dissemination, and end user science and applications. In 1992, Townshend reviewed the state of land cover mapping using Earth observation data at a pivotal point in time and in the context of years of research and practical experience with Landsat Thematic Mapper (TM), Satellite Pour l’Observation de la Terre (SPOT) High Resolution Visible (HRV) and Advanced Very-High-Resolution Radiometer (AVHRR) data, demonstrating the opportunities and information content possible with increased spatial, spectral, and temporal resolutions. Townshend characterized the state-of-the-art for land cover at that time, identified trends, and shared insights on research directions. Now, on the 25th anniversary of Townshend’s important work, given numerous advances and emerging trends, we revisit the status of land cover mapping with Earth observation data. We posit that a new era of land cover analysis – Land Cover 2.0 – has emerged, enabled by free and open access data, analysis ready data, high performance computing, and rapidly developing data processing and analysis capabilities. Herein we characterize this new era in land cover information, highlighting institutional, computational, as well as theoretical developments that have occurred over the past 25 years, identifying the key issues and opportunities that have emerged. We conclude that Land Cover 2.0 offers efficiencies in information generation that will result in a proliferation of land cover products, reinforcing the need for transparency regarding the input data and algorithms used as well as adoption, implementation, and communication of rigorous accuracy assessment protocols. Further, land cover and land change assessments are no longer independent activities. Knowledge of land change is available to inform and enrich land cover generation.     

Rapid High Quality Compression of Volume Data for Visualization

Volume data sets resulting from, e.g., computerized tomography (CT) or magnetic resonance (MR) imaging modalities require enormous storage capacity even at moderate resolution levels. Such large files may require compression for processing in CPU memory which, however, comes at the cost of decoding times and some loss in reconstruction quality with respect to the original data. For many typical volume visualization applications (rendering of volume slices, subvolumes of interest, or isosurfaces) only a part of the volume data needs to be decoded. Thus, efficient compression techniques are needed that provide random access and rapid decompression of arbitrary parts the volume data. We propose a technique which is block based and operates in the wavelet transformed domain. We report performance results which compare favorably with previously published methods yielding large reconstruction quality gains from about 6 to 12 dB in PSNR for a512³ -volume extracted from the Visible Human data set. In terms of compression our algorithm compressed the data 6 times as much as the previous state-of-the-art block based coder for a given PSNR quality.     

Recent evolution of modern datasets for human activity recognition: a deep survey

Multimedia Systems - Human activity recognition has been a significant goal of computer vision since its inception and has developed considerably in the last years. Recent approaches to this...     

先进星载热发射和反射辐射仪(ASTER)的特点及应用

简要介绍了ASTER的性能及技术参数,通过与其它传感器(如ETM+)进行比较分析,得出ASTER存在的几大优势,归纳出其目前的应用和研究领域,包括冰川学、火山学、地质学、水文学、农业、城镇变化、自然灾害、气候变化和DEM的生成与应用等,并讨论了其尚需改进的地方。     

Trade-off between resolution and interactivity in spatial taskperformance

Virtual reality displays usually lag far behind classical computer graphics displays in static image quality parameters, such as resolution. Both the popular press and scientific papers often stress that resolution will have to increase greatly before users can experience virtual environments as “the real thing”. Nevertheless, it is already possible to do some useful work in VR environments. The point we experimentally demonstrate here is that resolution is much less important for interactive tasks that employ immersive VR, where users can explore the environment by moving their heads and bodies, than it is in classical computer graphics applications, where users can only explore by gazing at a single picture. In the context of unmanned aerial vehicles, frame rate (read: passive camera movement) is more important than resolution for target detection, recognition, designation, and tracking. In the experiments reported here, we investigated the relative importance of various image parameters like spatial resolution (number of pixels per video frame), intensity resolution (number of gray levels per pixel), and temporal resolution (number of frame updates per second). Most experimental data concerning these resolutions come from classical psychophysics. However, experimental conditions in classical psychophysics feature stationary observers looking at short-term, pointlike flashes on stationary displays, and are thus far more representative of human interaction with pictures and photographs than with highly interactive systems like those employed in virtual reality     

基于矢量尺度的图像滤波

对于医学图像滤波来说,很重要的一点就是滤波后的图像应该尽可能地保留图像中的边缘和细节特征.但通常在滤波过程中,在消除噪声的同时会模糊图像中一些重要的结构信息.在最近几年中,基于尺度的滤波方法已经有效地应用在灰度图像的滤波中.现把基于尺度的方法推广到矢量(彩色)图像的滤波中.在传统滤波方法(矢量中值滤波、基本矢量方向滤波和方向距离滤波)基础上,相应地提出了3种基于球尺度的矢量滤波器.新的滤波方法能根据图像中像素的尺度信息,在图像边缘和细节附近,即区域边界执行较小的平滑,而在区域内部进行较大的平滑,从而能够自适应地控制滤波过程.实验结果表明,所提出的滤波方法与传统滤波方法相比,在消除噪声的同时更能够保留图像中的边缘和细节特征.