A fast global fitting algorithm for fluorescence lifetime imaging microscopy based on image segmentation

Global fitting algorithms have been shown to improve effectively the accuracy and precision of the analysis of fluorescence lifetime imaging microscopy data. Global analysis performs better than unconstrained data fitting when prior information exists, such as the spatial invariance of the lifetimes of individual fluorescent species. The highly coupled nature of global analysis often results in a significantly slower convergence of the data fitting algorithm as compared with unconstrained analysis. Convergence speed can be greatly accelerated by providing appropriate initial guesses. Realizing that the image morphology often correlates with fluorophore distribution, a global fitting algorithm has been developed to assign initial guesses throughout an image based on a segmentation analysis. This algorithm was tested on both simulated data sets and time-domain lifetime measurements. We have successfully measured fluorophore distribution in fibroblasts stained with Hoechst and calcein. This method further allows second harmonic generation from collagen and elastin autofluorescence to be differentiated in fluorescence lifetime imaging microscopy images of ex vivo human skin. On our experimental measurement, this algorithm increased convergence speed by over two orders of magnitude and achieved significantly better fits.     

Size and shape in similarity coefficients based on metric characters

A variety of quantitative measures of distance and similarity have been used in physical anthropology, often without sufficient prior testing of their usefulness and understanding of their meaning. A variety of such coefficients were compared by applying them to an odontometric sample. It was found that Penrose's size-shape dichotomy extends not only to his coefficients but to all other similarity measures as well, such as the coefficient of racial likeness, generalized distance, canonical variates, and Q-mode correlation. Only size differences were detected by the C.R.L., Penrose's size distance, D², and canonical variates, and as a result these methods failed to produce an accurate classification. Penrose's shape distance and Q-mode correlation coefficients produced better results due to their determination of similarity on the basis of more important shape and morphological differences. The D² and canonical variates methods were converted to shape measures through Q-mode standardization of the raw data, whereupon they also produced more meaningful results.     

Topology-based fluorescence image analysis for automated cell identification and segmentation

Cell segmentation refers to the body of techniques used to identify cells in images and extract biologically relevant information from them; however, manual segmentation is laborious and subjective. We present Topological Boundary Line Estimation using Recurrence Of Neighbouring Emissions (TOBLERONE), a topological image analysis tool which identifies persistent homological image features as opposed to the geometric analysis commonly employed. We demonstrate that topological data analysis can provide accurate segmentation of arbitrarily-shaped cells, offering a means for automatic and objective data extraction. One cellular feature of particular interest in biology is the plasma membrane, which has been shown to present varying degrees of lipid packing, or membrane order, depending on the function and morphology of the cell type. With the use of environmentally-sensitive dyes, images derived from confocal microscopy can be used to quantify the degree of membrane order. We demonstrate that TOBLERONE is capable of automating this task.     

Medical image segmentation based on level set and isoperimetric constraint

Level set based methods are being increasingly used in image segmentation. In these methods, various shape constraints can be incorporated into the energy functionals to obtain the desired shapes of the contours represented by their zero level sets of functions. Motivated by the isoperimetric inequality in differential geometry, we propose a segmentation method in which the isoperimetric constrain is integrated into a level set framework to penalize the ratio of its squared perimeter to its enclosed area of an active contour. The new model can ensure the compactness of segmenting objects and complete missing or/and blurred parts of their boundaries simultaneously. The isoperimetric shape constraint is free of explicit expressions of shapes and scale-invariant. As a result, the proposed method can handle various objects with different scales and does not need to estimate parameters of shapes. Our method can segment lesions with blurred or/and partially missing boundaries in ultrasound, Computed Tomography (CT) and Magnetic Resonance (MR) images efficiently. Quantitative evaluation also confirms that the proposed method can provide more accurate segmentation than two well-known level set methods. Therefore, our proposed method shows potential of accurate segmentation of lesions for applying in diagnoses and surgical planning.     

Influence of staining on fast automated cell segmentation, feature extraction and cell image analysis

FAZYTAN, a system for fast automated cell segmentation, cell image analysis and extraction of nuclear features, was used to analyze cervical cell images variously stained by the conventional Papanicolaou stain, the new Papanicolaou stain and hematoxylin and thionin only; the last two dyes are used as the nuclear stains in the two versions of the Papanicolaou stain. Other dyes were also tried in cell classification experiments. All cell images in the variously stained samples could be described by the same nuclear features as had been adapted for the discrimination of conventional-Papanicolaou-stained cells. Variances were lower for thionin-stained cells as compared with hematoxylin-stained cells. By application of spectrophotometry, it was confirmed that the spectra of the cytoplasmic counterstains are superimposed on those of the nuclear stains. It appears that a variety of dyes are suitable as cytologic stains for cell classification by the FAZYTAN system, provided that they achieve sufficiently strong nuclear-cytoplasmic contrast by precisely delineating the chromatin texture.     

基于K-means聚类算法的叶螨图像分割与识别

【目的】叶螨(spider mite)是为害多种农作物的主要害虫,叶螨识别传统方法依靠肉眼,比较费时费力,为研究快速自动识别方法,引入计算机图像分析算法。【方法】该方法基于K-means聚类算法对田间作物上的叶螨图像进行分割与识别。【结果】对比传统RGB彩色分割方法,K-means聚类算法能够有效地对叶片上叶螨图像进行分割和识别。K-means聚类算法平均识别时间为3.56 s,平均识别准确率93.95%。识别时间 T 随图像总像素 Pi 的增加而增加。【结论】K-means聚类组合算法能够应用于叶螨图像分割与识别。     

基于目标颜色基及梯度方向匹配的菌落分割计数算法

【目的】通过菌落测试片提取菌落并计数,在农业、食品业、医疗卫生等领域中是一项常用且重要的工作。目前,菌落自动计数算法大都是以菌落培养皿为主要工作对象,对菌落测试片适用性较差。另外,目前相关技术在常规的粘连物体分割中有着较好的效果,但在菌落分割计数中,由于菌落本身的形态特征,对粘连菌落分割计数的效果尚不够精准。【方法】为解决此类问题,本文提出一种基于目标颜色基及梯度方向匹配的菌落分割计数算法。首先利用图像中菌落的颜色特征作为基,将图像转换到基空间内,以增强菌落与背景之间的差异,其次利用菌落图像的梯度幅值特征对梯度方向进行滤波,然后通过梯度方向进行匹配,进而将粘连的菌落分割,最后利用非极大值抑制的方法筛选出菌落并计数。【结果】经试验,本研究算法的计数精度可达98.00%,能够满足实际需求。【结论】在针对菌落的目标分割计数中,本研究算法不仅计数精度高,而且具有较好的鲁棒性,在对不同厂家的菌落总数测试片菌落分割计数中均有优异效果;然而在对大面积目标的检测分割中算法的准确率会有所下降,因此,该算法更适合于菌落等小目标的检测分割。     

Apportionment of global human genetic diversity based on craniometrics and skin color

A number of analyses of classical genetic markers and DNA polymorphisms have shown that the majority of human genetic diversity exists within local populations (approximately 85%), with much less among local populations (approximately 5%) or between major geographic regions or "races" (approximately 10%). Previous analysis of craniometric variation (Relethford [1994] Am J Phys Anthropol 95:53-62) found that between 11-14% of global diversity exists among geographic regions, with the remaining diversity existing within regions. The methods used in this earlier paper are extended to a hierarchical partitioning of genetic diversity in quantitative traits, allowing for assessment of diversity among regions, among local populations within regions, and within local populations. These methods are applied to global data on craniometric variation (57 traits) and skin color. Multivariate analysis of craniometric variation shows results similar to those obtained from genetic markers and DNA polymorphisms: roughly 13% of the total diversity is among regions, 6% among local populations within regions, and 81% within local populations. This distribution is concordant with neutral genetic markers. Skin color shows the opposite pattern, with 88% of total variation among regions, 3% among local populations within regions, and 9% within local populations, a pattern shaped by natural selection. The apportionment of genetic diversity in skin color is atypical, and cannot be used for purposes of classification. If racial groups are based on skin color, it appears unlikely that other genetic and quantitative traits will show the same patterns of variation.     

On global and local critical points of extended contact process on homogeneous trees

We study spatial stochastic epidemic models called households models. The households models have more than two states at each vertex of a graph in contrast to the contact process. We show that, in the households models on trees, two thresholds of infection rates characterize epidemics. The global critical infection rate is defined by epidemic occurrence. However, some households may be eventually disease-free even for infection rates above the global critical infection rate, in as far as they are smaller than the local critical point. Whether the global one is smaller than the local one depends on the graph and the model. We show that, in the households models, the global one is smaller than the local one on homogeneous trees.     

Recurrent use of evolutionary importance for functional annotation of proteins based on local structural similarity

The annotation of protein function has not kept pace with the exponential growth of raw sequence and structure data. An emerging solution to this problem is to identify 3D motifs or templates in protein structures that are necessary and sufficient determinants of function. Here, we demonstrate the recurrent use of evolutionary trace information to construct such 3D templates for enzymes, search for them in other structures, and distinguish true from spurious matches. Serine protease templates built from evolutionarily important residues distinguish between proteases and other proteins nearly as well as the classic Ser-His-Asp catalytic triad. In 53 enzymes spanning 33 distinct functions, an automated pipeline identifies functionally related proteins with an average positive predictive power of 62%, including correct matches to proteins with the same function but with low sequence identity (the average identity for some templates is only 17%). Although these template building, searching, and match classification strategies are not yet optimized, their sequential implementation demonstrates a functional annotation pipeline which does not require experimental information, but only local molecular mimicry among a small number of evolutionarily important residues.     

A new multi-population artificial bee algorithm based on global and local optima for numerical optimization

Artificial Bee Colony (ABC) algorithm is a nature-inspired algorithm that showed its efficiency for optimizations. However, the ABC algorithm showed some imbalances between exploration and exploitation. In order to improve the exploitation and enhance the convergence speed, a multi-population ABC algorithm based on global and local optimum (namely MPGABC) is proposed in this paper. First, in MPGABC, the initial population is generated using both chaotic systems and opposition-based learning methods. The colony in MPGABC is divided into several sub-populations to increase diversity. Moreover, the solution search mechanism is modified by introducing global and local optima in the solution search equations of both employed and onlookers. The scout bees in the proposed algorithm are generated similarly to the initial population. Finally, the proposed algorithm is compared with several state-of-art ABC algorithm variants on a set of 13 classical benchmark functions. The experimental results show that MPGABC competes and outperforms other ABC algorithm variants.

     

Proliferation and apoptosis measurements by color image analysis based on differential absorption.

Cell proliferation and apoptosis indices are important indicators for the prognosis and treatment of a variety of cancers. A method is described using differential absorption color image analysis to measure proliferation and apoptosis in tumor sections using BrdU (5' bromodeoxyuridine) incorporation and immunohistochemistry and terminal deoxytransferase nick end-labeling (TUNEL). Nuclei were labeled with streptavidin-peroxidase-diaminobenzidine (DAB) secondary detection. The differential absorption method uses a computer-controlled microscope equipped with a tunable filter and digital camera to take advantage of the spectral differences of stained objects of interest. Images collected at defined wavelengths are divided and scaled to form ratio images in which the hematoxylin- or DAB-stained nuclei have intensity ranges far above those of surrounding structures. Using brightness thresholding followed by selection based on nuclear size and shape parameters, binary images were formed of the BrdU/apoptotic-positive tumor and all the tumor nuclei for subsequent counting and calculations of proliferation and apoptotic indices.     

基于面向对象的QuickBird遥感影像林隙分割与分类

传统的实地调查和人工解译方法已经不能满足区域尺度的林隙获取,高空间分辨率遥感影像的出现为区域尺度的林隙获取提供了可能.本研究采用QuickBird高空间分辨率光学遥感影像,结合面向对象分类技术对福建省三明市将乐县将乐国有林场进行林隙分割与分类.在面向对象分类过程中,采用10种尺度(10~100,步长为10)对QuickBird遥感影像进行分割,应用参考对象相交面积(RA_or)和分割对象相交面积(RA_os)进行分割结果评价.对每个尺度分割结果应用16个光谱特征,采用向量机分类器(SVM)进行林隙、非林隙和其他类型分类.结果表明:通过RA_or和RA_os等值法获得最优分割尺度参数为40.不同尺度参数之间的分类总精度最高相差22%.在最优尺度下,应用SVM分类器对林隙、非林隙和其他类型分类的总精度高达88%(Kappa=0.82).采用高空间分辨率遥感数据并结合面向对象的方法,可以代替传统的实地调查和人工解译对区域尺度的林隙进行识别分类.     

Precision cell location and relocation techniques. An application for cell image analysis

Two methods for precise cell location and relocation were developed at the Cell Image Analysis Laboratory, NASA, to increase the efficiency of the cell image acquisition process. The first method uses a Zeiss Zonax with a 10 micron scanning stage and automatically records cell coordinates on 5.25-inch floppy disks. The second method uses a Digital Positioning Device with a 10 micron scanning stage, and the cell coordinates displayed on light-emitting diodes (LEDs) are recorded manually. Because of its superior speed and automation, the Zonax was selected as the method of choice. By using a Zonax for off-line cell location and another Zonax for on-line cell relocation, more than three times the number of digitized images were collected in ten months than in the previous three years. Other beneficial applications of the cell location and relocation methods were realized in photomicrography, data base verification and cytopathology.     

New technologies for automated cell counting based on optical image analysis `The Cellscreen'

A prototype of a newly developed apparatus for measuring cell growth characteristics of suspension cells in micro titre plates over a period of time was examined. Fully automated non-invasive cell counts in small volume cultivation vessels, e.g. 96 well plates, were performed with the Cellscreen system by Innovatis AG, Germany. The system automatically generates microscopic images of suspension cells which had sedimented on the base of the well plate. The total cell number and cell geometry was analysed without staining or sampling using the Cedex image recognition technology. Thus, time course studies of cell growth with the identical culture became possible. Basic parameters like the measurement range, the minimum number of images which were required for statistically reliable results, as well as the influence of the measurement itself and the effect of evaporation in 96 well plates on cell proliferation were determined. A comparison with standard methods including the influence of the cultured volume per well (25 l to 200 l) on cell growth was performed. Furthermore, the toxic substances ammonia, lactate and butyrate were used to show that the Cellscreen system is able to detect even the slightest changes in the specific growth rate.     

基于最小自由能和协变信息预测带伪结RNA二级结构的迭代化方法

多数RNA分子的结构在进化中是高度保守的, 其中很多包含伪结。而RNA伪结的预测一直是一个棘手问题, 很多RNA 二级结构预测算法都不能预测伪结。文章提出一种基于迭代法预测带伪结RNA 二级结构的新方法。该方法在给潜在碱基对打分时综合了热力学和协变信息, 通过基于最小自由能RNA折叠算法的多次迭代选出所有的碱基对。测试结果表明: 此方法几乎能预测到所有的伪结。与其他方法相比, 敏感度接近最优, 而特异性达到最优。