基于PET医学图像边缘检测算法的研究

目的:边缘检测在图像处理中至关重要,可被广泛应用于目标区域识别、区域形状检测、图像分割等图像分析领域。边缘是图像中不平稳现象和不规则结构的重要表现,往往携带着图像中的大量信息,并给出图像轮廓。在医学图像三维显示技术中,为了更精确的临床判别需要得到单像素的清晰轮廓,因此我们提出一种新的边缘检测算法。方法:在传统的小波边缘检测的基础上,提出了一种新的边缘算法,即基于小波极大值边缘检测算法,应用模糊算法构造相应的隶属函数,再对得到的极大值进一步筛选。结果:将该算法应用到医学图像中,最终可以得到较清楚的单像素边缘轮廓,实验结果证明了该算法的可行性。结论:运用这种算法处理过的医学图像边缘锐化更好,更清晰,能够为肿瘤的早期识别提供依据,满足医学影像识别的需要。     

3D surface analysis and classification in neuroimaging segmentation

This work emphasizes new algorithms for 3D edge and corner detection used in surface extraction and new concept of image segmentation in neuroimaging based on multidimensional shape analysis and classification. We propose using of NifTI standard for describing input data which enables interoperability and enhancement of existing computing tools used widely in neuroimaging research. In methods section we present our newly developed algorithm for 3D edge and corner detection, together with the algorithm for estimating local 3D shape. Surface of estimated shape is analyzed and segmented according to kernel shapes.     

Effects of quadrat size and data measurement on the detection of boundaries

Abstract. With sampled field data, the accuracy of delineation of ecotones is directly related to the resolution of the data (i.e. spatial and measurement type) and to the edge detection algorithm used. In the present study the reliability is investigated of an edge detection algorithm (lattice-wombling) to delimit vegetation boundaries when different spatial resolutions (quadrat sizes) are used. To quantify whether the edge detection algorithm is robust, it was applied to data from woody species in a second-growth woodland measured at different spatial resolutions with different data types. Boundaries were found at similar locations using any reasonable quadrat size (ca. 200 m²) but there were some slight differences when using different vegetation measures (density versus presence/absence data).     

Online Community Detection for Large Complex Networks

Complex networks describe a wide range of systems in nature and society. To understand complex networks, it is crucial to investigate their community structure. In this paper, we develop an online community detection algorithm with linear time complexity for large complex networks. Our algorithm processes a network edge by edge in the order that the network is fed to the algorithm. If a new edge is added, it just updates the existing community structure in constant time, and does not need to re-compute the whole network. Therefore, it can efficiently process large networks in real time. Our algorithm optimizes expected modularity instead of modularity at each step to avoid poor performance. The experiments are carried out using 11 public data sets, and are measured by two criteria, modularity and NMI (Normalized Mutual Information). The results show that our algorithm''s running time is less than the commonly used Louvain algorithm while it gives competitive performance.     

基于组合探针识别的大规模细菌分类16 S rRNA基因芯片设计

随着16 S rRNA序列资源的不断丰富,以及寡核苷酸微阵列基因芯片技术的不断进步,检测复杂微生物菌落中的微生物种群构成成为可能．现有的序列特异性探针设计算法缺乏足够的覆盖度、灵活性以及效率,不能满足大规模细菌检测基因芯片的设计要求．很多组特异性探针设计算法的思路多局限于针对某个目标序列组设计唯一的组特异性探针．在很多应用场合,设计单个探针检测组内所有目标序列的目标是很难达到的．因此,设计多个探针通过组合方式进行检测是很有必要的．每个探针能特异性地检测组内一部分目标序列,通过组合就能提高覆盖率．然而,在所有可能的探针组合中找到一个优化的探针组合是很耗时的．提出了一个可行的基于相对熵和遗传算法的组合探针设计算法．     

Segmentation of microscopic cell scenes

Different methods for the automated segmentation of microscopic cell scenes are presented with examples. The techniques discussed include edge detection by thresholding, "blob" detection by split-and-merge algorithm, global thresholding using gray-level histograms, hierarchic thresholding using color information, global thresholding using two-dimensional histograms and segmentation by "blob" labeling. Methods are more robust against insignificant changes in the scene and perform more reliably as more a priori knowledge about the scene is incorporated in the segmentation algorithm. The inclusion of both photometric and geometric a priori knowledge can result in a high level of correct segmentations, the cost of which is increased computation time.     

基于矩阵广义逆和奇异值分解的运动水果模糊图像恢复

针对利用机器视觉对进行水果分级时,由于水果运动所造成的模糊问题,提出了基于矩阵广义逆和奇异值分解的恢复方法,实验表明恢复的图像比较清晰,并且在保证实时的条件下可将水果大小检测的相对误差从4．17%减小为0．671%,相比于传统的恢复方法而言,提高了速度,消除了误差积累,为后续的边缘检测、形状分析、缺陷分类等打下了基础．     

Way to the detection of Mars life]

In this review, I would like to introduce how we can detect the possible life on Mars. Even though the quantitative estimation of the possibility of biogenesis on Mars is difficult, Dr. McKay and his colleagues work has thrown a tiny light for this possibility. Considering Mars environmental conditions, the possible life is microorganisms. The detection of microorganisms in natural environments is not easy even on Earth due to the premature detection technique. We have developed a method based on the fluorescence microscopic technique. This method proved to be successful for the detection of terrestrial microorganisms. Even some pre-biotic cells can be detected. We are developing a miniature detection apparatus which meet the required standard for installing on the Mars landers. We also propose the ground based experiments using Martian meteorites or pseudo-Martian rocks.     

Segmentation of left ventricle in short-axis echocardiographic sequences by weighted radial edge filtering and adaptive recovery of dropout regions

In this paper, we present a weighted radial edge filtering algorithm with adaptive recovery of dropout regions for the semi-automatic delineation of endocardial contours in short-axis echocardiographic image sequences. The proposed algorithm requires minimal user intervention at the end diastolic frame of the image sequence for specifying the candidate points of the contour. The region of interest is identified by fitting an ellipse in the region defined by the specified points. Subsequently, the ellipse centre is used for originating the radial lines for filtering. A weighted radial edge filter is employed for the detection of edge points. The outliers are corrected by global as well as local statistics. Dropout regions are recovered by incorporating the important temporal information from the previous frame by means of recursive least squares adaptive filter. This ensures fairly accurate segmentation of the cardiac structures for further determination of the functional cardiac parameters. The proposed algorithm was applied to 10 data-sets over a full cardiac cycle and the results were validated by comparing computer-generated boundaries to those manually outlined by two experts using Hausdorff distance (HD) measure, radial mean square error (rmse) and contour similarity index. The rmse was 1.83 mm with a HD of 5.12 ± 1.21 mm. We have also compared our results with two existing approaches, level set and optical flow. The results indicate an improvement when compared with ground truth due to incorporation of temporal clues. The weighted radial edge filtering algorithm in conjunction with adaptive dropout recovery offers semi-automatic segmentation of heart chambers in 2D echocardiography sequences for accurate assessment of global left ventricular function to guide therapy and staging of the cardiovascular diseases.     

Segmentation of left ventricle in short-axis echocardiographic sequences by weighted radial edge filtering and adaptive recovery of dropout regions

In this paper, we present a weighted radial edge filtering algorithm with adaptive recovery of dropout regions for the semi-automatic delineation of endocardial contours in short-axis echocardiographic image sequences. The proposed algorithm requires minimal user intervention at the end diastolic frame of the image sequence for specifying the candidate points of the contour. The region of interest is identified by fitting an ellipse in the region defined by the specified points. Subsequently, the ellipse centre is used for originating the radial lines for filtering. A weighted radial edge filter is employed for the detection of edge points. The outliers are corrected by global as well as local statistics. Dropout regions are recovered by incorporating the important temporal information from the previous frame by means of recursive least squares adaptive filter. This ensures fairly accurate segmentation of the cardiac structures for further determination of the functional cardiac parameters. The proposed algorithm was applied to 10 data-sets over a full cardiac cycle and the results were validated by comparing computer-generated boundaries to those manually outlined by two experts using Hausdorff distance (HD) measure, radial mean square error (rmse) and contour similarity index. The rmse was 1.83 mm with a HD of 5.12 ± 1.21 mm. We have also compared our results with two existing approaches, level set and optical flow. The results indicate an improvement when compared with ground truth due to incorporation of temporal clues. The weighted radial edge filtering algorithm in conjunction with adaptive dropout recovery offers semi-automatic segmentation of heart chambers in 2D echocardiography sequences for accurate assessment of global left ventricular function to guide therapy and staging of the cardiovascular diseases.     

Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images

Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells.     

An integrative method for identifying the over-annotated protein-coding genes in microbial genomes

The falsely annotated protein-coding genes have been deemed one of the major causes accounting for the annotating errors in public databases. Although many filtering approaches have been designed for the over-annotated protein-coding genes, some are questionable due to the resultant increase in false negative. Furthermore, there is no webserver or software specifically devised for the problem of over-annotation. In this study, we propose an integrative algorithm for detecting the over-annotated protein-coding genes in microorganisms. Overall, an average accuracy of 99.94% is achieved over 61 microbial genomes. The extremely high accuracy indicates that the presented algorithm is efficient to differentiate the protein-coding genes from the non-coding open reading frames. Abundant analyses show that the predicting results are reliable and the integrative algorithm is robust and convenient. Our analysis also indicates that the over-annotated protein-coding genes can cause the false positive of horizontal gene transfers detection. The webserver of the proposed algorithm can be freely accessible from www.cbi.seu.edu.cn/RPGM.     

Clinical applications of molecular biology for infectious diseases

Molecular biological methods for the detection and characterisation of microorganisms have revolutionised diagnostic microbiology and are now part of routine specimen processing. Polymerase chain reaction (PCR) techniques have led the way into this new era by allowing rapid detection of microorganisms that were previously difficult or impossible to detect by traditional microbiological methods. In addition to detection of fastidious microorganisms, more rapid detection by molecular methods is now possible for pathogens of public health importance. Molecular methods have now progressed beyond identification to detect antimicrobial resistance genes and provide public health information such as strain characterisation by genotyping. Treatment of certain microorganisms has been improved by viral resistance detection and viral load testing for the monitoring of responses to antiviral therapies. With the advent of multiplex PCR, real-time PCR and improvements in efficiency through automation, the costs of molecular methods are decreasing such that the role of molecular methods will further increase. This review will focus on the clinical utility of molecular methods performed in the clinical microbiology laboratory, illustrated with the many examples of how they have changed laboratory diagnosis and therefore the management of infectious diseases.     

The art of community detection

Networks in nature possess a remarkable amount of structure. Via a series of data-driven discoveries, the cutting edge of network science has recently progressed from positing that the random graphs of mathematical graph theory might accurately describe real networks to the current viewpoint that networks in nature are highly complex and structured entities. The identification of high order structures in networks unveils insights into their functional organization. Recently, Clauset, Moore, and Newman, introduced a new algorithm that identifies such heterogeneities in complex networks by utilizing the hierarchy that necessarily organizes the many levels of structure. Here, we anchor their algorithm in a general community detection framework and discuss the future of community detection.     

Morphological characterisation of yeast colony growth on solid media using image processing

To rapidly determine the effect of environmental factors on yeast growth, a cell counting and colony sizing image analysis method was developed to characterise colony growth on solid media. A digitised microscopic image of the yeast was analysed using the Watershed algorithm for cell number determination and a morphological edge detection for colony size determination. The influence of temperature and physiological stress on yeast growth was then investigated over 12.5 h and data extracted by the image analysis method. © Rapid Science Ltd. 1998     

New methods for the diagnosis of enteric infections

Gastrointestinal disease remains a major cause of mortality and morbidity throughout the world. Recently, a number of viral, bacterial, and protozoan agents have been identified which can cause a range of gastrointestinal disorders. The effective management of these diseases requires the prompt identification of the infecting micro-organism and the early institution of preventative and therapeutic interventions. The detection of infecting microorganisms in fecal and intestinal fluids presents a particular challenge to the diagnostic microbiologist. Cultivation can be difficult due to the fastidious nature of the microorganisms and the presence of cytotoxic materials in the specimen. In the past, immunoassays have been used for the detection of some microorganisms. However, immunoassays have limited sensitivity and cannot detect all infecting microorganisms. Recently, nucleic acid amplification techniques have been developed for the direct detection of pathogenic microbial DNA and RNA in human body fluids. We have found that these methods can be applied for the accurate detection of intestinal vlruses provided that inhibitors of enzymatic amplification are removed from the sample. Using affinity binding purification and non-isotopic DNA measurement techniques, we have developed sensitive and specific assays for the quantitation of a wide range of infecting microorganisms in intestinal fluids. Nucleic acid amplification provides a Abstract continued on next page unique tool for the study of enteric pathogens and for the development of strategies for their eventual elimination from the human environment.This paper was presented at the IUMS Symposium on New Developments in Diagnosis and Control of Infectious Diseases held in conjunction with the Eighth International Congress of Virology, Berlin, Germany, 24–31 August 1990.     

Ultrafast cell edge detection by line‐scan time‐stretch microscopy

Ultrafast time‐stretch imaging technique recently attracts an increasing interest for applications in cell classification due to high throughput and high sensitivity. A novel imaging modality of time‐stretch imaging technique for edge detection is proposed. Edge detection based on the directional derivative is realized using differential detection. As the image processing is mainly implemented in the physical layer, the computation complexity of edge extraction is significantly reduced. An imaging system for edge detection with the scan rate of 77.76 MHz is experimentally demonstrated. Resolution target is first measured to verify the feasibility of the edge extraction. Furthermore, various cells, including red blood cells, lung cancer cells and breast cancer cells, are detected. The edges of cancerous cells present in a completely different form. The imaging system for edge detection would be a good candidate for high‐throughput cell classification.      

微生物分子生态学技术及其在环境污染研究中的应用

较为系统地概述了核酸探针检测技术、利用引物的PCR技术、DNA序列分析技术和电泳分离及显示技术在国内外的研究进展,并探讨了这些技术在环境污染研究中的应用及其方向。结果表明,这些被认为是重要的微生物分子生态学技术,在探索微生物与污染环境之间的相互关系中发挥了重要作用。促进了污染环境的微生物遗传适应进化机制的研究,污染物的微生物降解有关基因的定位及微生物工程菌的构建等方面的工作,从而推进了污染环境微生物修复的分子生态学的发展。     

The use of microorganisms for the formation of metal nanoparticles and their application

Nanomaterials are at the leading edge of the rapidly developing field of nanotechnology. The development of reliable experimental protocols for the synthesis of nanomaterials over a range of chemical compositions, sizes, and high monodispersity is one of the challenging issues in current nanotechnology. In the context of the current drive to develop green technologies in material synthesis, this aspect of nanotechnology is of considerable importance. Biological systems, masters of ambient condition chemistry, synthesize inorganic materials that are hierarchically organized from the nano- to the macroscale. Recent studies on the use of microorganisms in the synthesis of nanoparticles are a relatively new and exciting area of research with considerable potential for development. This review describes a brief overview of the current research worldwide on the use of microorganisms in the biosynthesis of metal nanoparticles and their applications.     

中国寒温带森林与温带森林之间过渡边缘的检测

 根据1995年黑龙江省森林资源调查的固定样地数据,以多个树种的信息为分类因子,采用1994年Fortin 提出的二维数据边缘检测的方法并考虑树种垂直分布对边缘确定的影响,对我国寒温带针叶林与温带阔叶混交林的过渡中心边缘进行了科学定位,并对所获得的边缘的显著性进行了检验,为进一步研究其结构和功能提供了基础数据。结果表明,寒温带针叶林与温带针阔混交林的边缘大致位于48°30′ N,南北浮动10′,126°～130° E之间,行政区划上属于北安_乌伊岭_嘉荫一线,基本是沿小兴安岭山体的北缘。该边缘与目前天然红松