Methods for acquisition of quantitative from confocal images of gene expression in situ

In this review we summarize original methods for the extraction quantitative information from the confocal images of gene expression patterns. These methods include image segmentation, extraction of quantitative numerical data on gene expression, removal of background signal and spatial registration. Finally it is possible to construct a spatiotemporal atlas of gene expression form individual images obtained at each developmental stage. Initially all methods were developed to extract quantitative numerical information form confocal images of segmentation gene expression in Drosophila melanogaster. Application of these methods to Drosophila images makes it possible to reveal new mechanisms of formation of segmentation gene expression domains, as well as to construct the quantitative atlas of segmentation gene expression. Most image processing procedures can be easily adapted to process a wide range of biological images.     

Methods for acquisition of quantitative data from confocal images of gene expression in situ

In this review, we summarize original methods for the extraction of quantitative information from confocal images of gene-expression patterns. These methods include image segmentation, the extraction of quantitative numerical data on gene expression, and the removal of background signal and spatial registration. Finally, it is possible to construct a spatiotemporal atlas of gene expression from individual images recorded at each developmental stage. Initially all methods were developed to extract quantitative numerical information from confocal images of segmentation gene expression in Drosophila melanogaster. The application of these methods to Drosophila images makes it possible to reveal new mechanisms in the formation of segmentation gene expression domains, as well as to construct a quantitative atlas of segmentation gene expression. Most image processing procedures can be easily adapted to process a wide range of biological images.     

Workflow and metrics for image quality control in large-scale high-content screens

Automated microscopes have enabled the unprecedented collection of images at a rate that precludes visual inspection. Automated image analysis is required to identify interesting samples and extract quantitative information for high-content screening (HCS). However, researchers are impeded by the lack of metrics and software tools to identify image-based aberrations that pollute data, limiting experiment quality. The authors have developed and validated approaches to identify those image acquisition artifacts that prevent optimal extraction of knowledge from high-content microscopy experiments. They have implemented these as a versatile, open-source toolbox of algorithms and metrics readily usable by biologists to improve data quality in a wide variety of biological experiments.     

Automated Confocal Laser Scanning Microscopy and Semiautomated Image Processing for Analysis of Biofilms

The purpose of this study was to develop and apply a quantitative optical method suitable for routine measurements of biofilm structures under in situ conditions. A computer program was designed to perform automated investigations of biofilms by using image acquisition and image analysis techniques. To obtain a representative profile of a growing biofilm, a nondestructive procedure was created to study and quantify undisturbed microbial populations within the physical environment of a glass flow cell. Key components of the computer-controlled processing described in this paper are the on-line collection of confocal two-dimensional (2D) cross-sectional images from a preset 3D domain of interest followed by the off-line analysis of these 2D images. With the quantitative extraction of information contained in each image, a three-dimensional reconstruction of the principal biological events can be achieved. The program is convenient to handle and was generated to determine biovolumes and thus facilitate the examination of dynamic processes within biofilms. In the present study, Pseudomonas fluorescens or a green fluorescent protein-expressing Escherichia coli strain, EC12, was inoculated into glass flow cells and the respective monoculture biofilms were analyzed in three dimensions. In this paper we describe a method for the routine measurements of biofilms by using automated image acquisition and semiautomated image analysis.     

A microcomputer-based algorithm for digitized image simplification and information extraction

Spatial analysis of objects often requires significant image simplification prior to information extraction and application of a decision-making algorithm. Much decision making based on images (e.g., histologic diagnoses) requires identifying patterns in complex backgrounds (image simplification) and comparison of those patterns to other patterns (decision making). Automated extraction of information from images commonly requires the extraction system to recognize edges (contours) of structures and their internal discontinuities (such as gradations in density) and to selectively suppress irrelevant data in order to conserve memory and speed computation; data from homogeneous image areas occupy memory, but are noncontributory or redundant. This paper describes the development of a microcomputer-based algorithm that deletes all homogeneous information from overlaid digitized images, generating contours in the place of nonhomogeneities. Contours corresponding to different areas or objects depend on color differences between an object and its surroundings. Any set of contours can be deleted almost instantaneously, leaving only those of interest. Contours can be highlighted by an operator-driven interactive process if desired and can be deleted and retrieved until an appropriate image is obtained. This contour-generating and image-simplification algorithm facilitates three-dimensional reconstruction of an object from serial images by reducing the number of calculations required and yielding a cleaner final image.     

A red line not to cross: Evaluating the limitation and properness of gel image tuning procedures

Currently, results of gel electrophoresis are commonly documented in digital formats by image acquisition instruments. In this study, gel images tuned by a common image processing software package, Photoshop, were assessed to understand the transforming algorithms and their impacts on quantitative analysis. TotalLab 100, an electrophoresis gel image analysis software package, was applied for image quantitation and evaluation. The three most frequently used image tuning functions—adjustments of the brightness, contrast, and grayscale span (level) of images—were investigated using both data generated from a standard grayscale tablet and an actual electrophoresis gel image. The influences of these procedures were analyzed for the grayscale transformation between the input and output images. Although all three procedures differentially improved the visualization of the input image, adjusting the contrast of images disrupted the quantitative information because of its nonlinear transforming algorithm. Under certain conditions, adjusting the brightness or the level of images could preserve the quantitative information because of the linear transforming algorithms. It was found that when the minimum and maximum grayscales of a gel image were recognized, using a commercial software package to maximally stretch the level may significantly improve the quality of a gel image without jeopardizing quantitative analysis.     

MITICS (MALDI Imaging Team Imaging Computing System): a new open source mass spectrometry imaging software

MITICS is a new software developed for MALDI imaging. We tried to render this software compatible with all types of instruments. MITICS is divided in two parts: MITICS control for data acquisition and MITICS Image for data processing and images reconstruction. MITICS control is available for Applied BioSystems MALDI-TOF instruments and MITICS Image for both Applied BioSystems and Bruker Daltonics ones. MITICS Control provides an interface to the user for setting the acquisition parameters for the imaging sequence, namely set instruments acquisition parameters, create the raster of acquisition and control post-acquisition data processing, and provide this settings to the automatic acquisition software of the MALDI instrument. MITICS Image ensures image reconstruction, files are first converted to XML files before being loaded in a database. In MITICS image we have chosen to implement different data representations and calculations for image reconstruction. MITICS Image uses three different representations that have shown to ease extraction of information from the whole data set. It also offers image reconstruction base either on the maximum peak intensity or the peak area. Image reconstruction is possible for single ions but also by summing signals of different ions. MITICS was validated on biological cases.     

Digital microscopy in phycological research, with special reference to microalgae

Digital imaging technology is gradually being incorporated into all areas of biological research, but there is a distinct lack of information resources targeted at scientists in their specialist areas. There is a wealth of potential applications for digital images in phycology, including morphometric or visual analysis of specimens, taxonomic databases and publication of digital micrographs in lieu of photomicrographs. Here, we provide a review of digital imaging in general and its potential for the field of microalgal research in particular. We also present a number of imaging techniques that are critical for image acquisition and optimization, which can enable beginners to build their own libraries of high quality digital images. Resolution requirements of digital cameras are explained and related to microscope resolution. The benefits of digital imaging technology are discussed and contrasted with those of traditional silver halide technology.     

Accelerated Optical Projection Tomography Applied to In Vivo Imaging of Zebrafish

Optical projection tomography (OPT) provides a non-invasive 3-D imaging modality that can be applied to longitudinal studies of live disease models, including in zebrafish. Current limitations include the requirement of a minimum number of angular projections for reconstruction of reasonable OPT images using filtered back projection (FBP), which is typically several hundred, leading to acquisition times of several minutes. It is highly desirable to decrease the number of required angular projections to decrease both the total acquisition time and the light dose to the sample. This is particularly important to enable longitudinal studies, which involve measurements of the same fish at different time points. In this work, we demonstrate that the use of an iterative algorithm to reconstruct sparsely sampled OPT data sets can provide useful 3-D images with 50 or fewer projections, thereby significantly decreasing the minimum acquisition time and light dose while maintaining image quality. A transgenic zebrafish embryo with fluorescent labelling of the vasculature was imaged to acquire densely sampled (800 projections) and under-sampled data sets of transmitted and fluorescence projection images. The under-sampled OPT data sets were reconstructed using an iterative total variation-based image reconstruction algorithm and compared against FBP reconstructions of the densely sampled data sets. To illustrate the potential for quantitative analysis following rapid OPT data acquisition, a Hessian-based method was applied to automatically segment the reconstructed images to select the vasculature network. Results showed that 3-D images of the zebrafish embryo and its vasculature of sufficient visual quality for quantitative analysis can be reconstructed using the iterative algorithm from only 32 projections—achieving up to 28 times improvement in imaging speed and leading to total acquisition times of a few seconds.     

Remote assessment of ovarian response and follicular status using visual analysis of ultrasound images

Computer assisted evaluation of ultrasonographic image attributes indicative of viability and atresia of ovarian follicles has the potential to become an integral part of ovarian superstimulation protocols. However, in many cases, animal handling facilities, laboratories providing image analysis services and the individual making clinical decisions are geographically separated. The feasibility of remote assessment of follicular status and ovarian response to superstimulation is demonstrated using internet and video conferencing techniques. A cohort of heifers (n = 6) was subjected to ovarian superstimulation. Ultrasound images of the ovarian responses were digitally acquired and transmitted to a distant laboratory for quantitative assessment. Images from follicles which ovulated in response to luteolysis and GnRH treatments were visually and quantitatively different from follicles committed to atresia. Two types of atresia were observed; images of non-ovulatory follicles were characterized as being reflective of the potential to develop into either follicular or luteal cysts. It is probable that the response of individual follicles to the pharmacologic agents used for ovarian superstimulation and ovulation induction may be as important as the total number of follicles recruited. Thus, assessment of the progress and fates of individual follicles may be used to tailor ovarian stimulation to individual donor animals to increase the probability of successful ovarian stimulation and embryo production. The use of the internet for data transfer, image analysis and clinical evaluation places the prospects of providing useful information within the grasp of practitioners who wish to have access to the biological information, but do not wish to invest in the equipment required to make quantitative assessments of visual data.     

Performance evaluation of a machine vision system for insect pests identification of field crops using artificial neural networks

Feature extraction is a crucial part of advanced image recognition systems. In this research, an autonomous detection device was designed and developed for insect pest detection to improve the ability of intelligent systems in order to annihilate harmful insect pests in agricultural crop fields. Device included a dark chamber, a CCD digital camera, a LDR lightening module and a personal computer. The proposed programme for precise insect pest detection was based on an image processing algorithm and artificial neural networks (ANNs). After image acquisition, the insect pests’ images were extracted from original images with Canny filtration. Afterwards, four morphological and three textural features from the obtained images were measured and normalised. Performance of ANN model was tested successfully for Beet armyworm (Spodoptera exigua) recognition in images using back-propagation supervised learning method and inspection data. Results showed that proposed system was able to identify S. exigua in the images from other species. Such this machine vision system can be used in autonomous field robots to achieve a modern farmer’s assistant.     

Feature extraction from three-dimensional images in quantitative microscopy

Different methods are investigated in selecting and generating the appropriate microscope images for analysis of three-dimensional objects in quantitative microscopy. Traditionally, the ‘best’ focused image from a set is used for quantitative analysis. Such an objectively determined image is optimal for the extraction of some features, but may not be the best image for the extraction of all features. Various methods using multiple images are here developed to obtain a tighter distribution for all features.Three different approaches for analysis of images of stained cervical cells were analyzed. In the first approach, features are extracted from each image in the set. The feature values are then averaged to give the final result. In the second approach, a set of varying focused images are reconstructed to obtain a set of in-focus images. Features are then extracted from this set and averaged. In the third approach, a set of images in the three-dimensional scene is compressed into a single two-dimensional image. Four different compression methods are used. Features are then extracted from the resulting two-dimensional image. The third approach is employed on both the raw and transformed images.Each approach has its advantages and disadvantages. The first approach is fast and produces reasonable results. The second approach is more computationally expensive but produces the best results. The last approach overcomes the memory storage problem of the first two approaches since the set of images is compressed into one. The method of compression using the highest gradient pixel produces better results overall than other data reduction techniques and produces results comparable to the first approach.     

Sea water chlorophyll-a estimation using hyperspectral images and supervised Artificial Neural Network

The use of satellite hyperspectral images has improved the extraction of information compared to multispectral images. Although designed as a technical demonstration for land applications, Hyperion satellite hyperspectral images are used to estimate sea water parameters in the coastal area. A combination of turbid river inputs, as well as the open sea flushing, determines the quality of the sea water in the coastal area and the status of its environment. In addition, the existence of different source of pollution adds to the complexity of the coastal sea water analysis. The field campaigns to retrieve sea water parameters provided by the past completed projects were coincident with acquisition of the Hyperion image covering the pilot area. A robust method based on a supervised Feed-Forward Back-Propagation Artificial Neural Network (ANN-BP) algorithm is applied to retrieve the concentration of chlorophyll-a from hyperspectral image. In addition, Hyperion images are used to show the variation of chlorophyll-a during two different periods of time. The variation is due to many manmade environmental disasters such as oil spill and continuous discharge of chemical and solid wastes. The research proves that the new method based on ANN has improved the mathematical regression methods to a coefficient of determination almost equal 1 compared to about 0.4 for the methods not based on ANN-BP.     

Isolation of bright aggregate fluctuations in a multipopulation image correlation spectroscopy system using intensity subtraction

Image correlation spectroscopy allows sensitive measurement of the spatial distribution and aggregation state of fluorescent membrane macro molecules. When studying a single population system (i.e., aggregates of similar brightness), an accurate measure can be made of the aggregate number per observation area, but this measurement becomes much more complex in a distributed population system (i.e., bright and faint aggregates). This article describes an alternate solution that involves extraction of the bright aggregate population information. This novel development for image correlation spectroscopy, termed intensity subtraction analysis, uses sequential uniform intensity subtraction from raw confocal images. Sequential intensity subtraction results in loss of faint aggregate fluctuations that are smaller in magnitude than fluctuations due to the brightest aggregates. The resulting image has correlatable fluctuations originating from only the brightest population, permitting quantification of this population's distribution and further cross-correlation measurements. The feasibility of this technique is demonstrated using fluorescent microsphere images and biological samples. The technique is further used to examine the spatial distribution of a plasma-membrane-labeled fluorescent synthetic ganglioside, and to cross-correlate this probe with various membrane markers. The evidence provided demonstrates that bright aggregates of the fluorescent ganglioside are associated with clathrin-coated pits, membrane microvilli, and detergent-resistant membranes.     

无人机高光谱影像与冠层树种多样性监测

冠层树种多样性是自然森林生态系统功能和服务的重要基础。及时掌握冠层多样性的现状及变化趋势, 是探讨诸多重要生态学问题的前提, 更是制定合理生物多样性保护策略的基础。但受制于传统的多样性信息采集方法, 区域尺度的高精度冠层多样性监测发展较为缓慢; 许多在气候变化和人类干扰下的生物多样性分布信息得不到及时更新。近年来基于无人机的冠层高光谱影像收集与分析技术的发展, 使得冠层多样性监测迎来了新的发展契机。本文从森林冠层高光谱影像出发, 介绍了与多样性监测相关的无人机航拍和基于深度学习的图像处理技术, 并结合已有文献, 探讨了无人机高光谱应用于森林冠层树种多样性监测的研究现状、可行性、优势及缺陷等。我们认为冠层高光谱影像为多样性监测提供了不可或缺且丰富的原始信息; 而无人机与高光谱相机的结合, 使得区域化高频率(如每周)、高精度(如分米乃至厘米级)的冠层多样性信息自动化收集成为可能。然而高光谱影像数据量大、数据维度高与数据结构非线性的特点为影像处理带来了挑战, 而深度学习技术的飞跃, 使得从冠层高光谱影像中提取个体及物种信息达到了极高精度。恰当地使用这些技术将大大提升冠层树种多样性的自动化监测水平, 由此也将帮助我们在当前剧变环境下及时掌握森林冠层多样性的现状与变化, 为生物多样性研究与保护提供可靠的数据支撑。     

Inferring biological structures from super-resolution single molecule images using generative models

Localization-based super resolution imaging is presently limited by sampling requirements for dynamic measurements of biological structures. Generating an image requires serial acquisition of individual molecular positions at sufficient density to define a biological structure, increasing the acquisition time. Efficient analysis of biological structures from sparse localization data could substantially improve the dynamic imaging capabilities of these methods. Using a feature extraction technique called the Hough Transform simple biological structures are identified from both simulated and real localization data. We demonstrate that these generative models can efficiently infer biological structures in the data from far fewer localizations than are required for complete spatial sampling. Analysis at partial data densities revealed efficient recovery of clathrin vesicle size distributions and microtubule orientation angles with as little as 10% of the localization data. This approach significantly increases the temporal resolution for dynamic imaging and provides quantitatively useful biological information.     

基于中分辨率TM数据的湿地水生植被提取

利用湿地水生植被生长旺盛、光谱反射较强、光谱信息比较丰富的8月份中分辨率Landsat TM和ETM+多光谱遥感影像,采用面向对象的分类方法,进行野鸭湖湿地水生植被的提取。研究表明:在提取过程中,通过对原始影像进行主成分变换和穗帽变换,将主要信息与噪声分离,不仅减小了数据冗余和波段间的相关性,而且增大了影像上湿地水生植被与其他地物类型光谱和空间信息的差异性,并结合野外水生植被光谱特征分析,选择归一化植被指数NDVI与归一化水体指数NDWI辅助分类,构建特征波段或波段组合,然后,确定适当的隶属度函数和阈值范围,构建分类决策树,完成湿地水生植被的自动分类,提高了影像分割与面向对象分类的精度,取得了较为理想的湿地水生植被提取结果。2002年和2008年两景影像的总体分类精度分别达到86.5%和85.44%,表明中分辨率TM影像可以满足湿地水生植被提取的需要,又因为其具有较高的波谱分辨率、极为丰富的信息量、相对较低的价格、长时间序列,可以作为近20a湿地水生植被提取和动态变化监测的主要数据源。     

A Reference-Free Method for Brightness Compensation and Contrast Enhancement of Micrographs of Serial Sections

Three-dimensional (3D) reconstruction of an organ or tissue from a stack of histologic serial sections provides valuable morphological information. The procedure includes section preparation of the organ or tissue, micrographs acquisition, image registration, 3D reconstruction, and visualization. However, the brightness and contrast through the image stack may not be consistent due to imperfections in the staining procedure, which may cause difficulties in micro-structure identification using virtual sections, region segmentation, automatic target tracing, etc. In the present study, a reference-free method, Sequential Histogram Fitting Algorithm (SHFA), is therefore developed for adjusting the severe and irregular variance of brightness and contrast within the image stack. To apply the SHFA, the gray value histograms of individual images are first calculated over the entire image stack and a set of landmark gray values are chosen. Then the histograms are transformed so that there are no abrupt changes in progressing through the stack. Finally, the pixel gray values of the original images are transformed into the desired ones based on the relationship between the original and the transformed histograms. The SHFA is tested on an image stacks from mouse kidney sections stained with toluidine blue, and captured by a slide scanner. As results, the images through the entire stack reveal homogenous brightness and consistent contrast. In addition, subtle color differences in the tissue are well preserved so that the morphological details can be recognized, even in virtual sections. In conclusion, compared with the existing histogram-based methods, the present study provides a practical method suitable for compensating brightness, and improving contrast of images derived from a large number of serial sections of biological organ.