CMEIAS Color Segmentation: An Improved Computing Technology to Process Color Images for Quantitative Microbial Ecology Studies at Single-Cell Resolution

Quantitative microscopy and digital image analysis are underutilized in microbial ecology largely because of the laborious task to segment foreground object pixels from background, especially in complex color micrographs of environmental samples. In this paper, we describe an improved computing technology developed to alleviate this limitation. The system’s uniqueness is its ability to edit digital images accurately when presented with the difficult yet commonplace challenge of removing background pixels whose three-dimensional color space overlaps the range that defines foreground objects. Image segmentation is accomplished by utilizing algorithms that address color and spatial relationships of user-selected foreground object pixels. Performance of the color segmentation algorithm evaluated on 26 complex micrographs at single pixel resolution had an overall pixel classification accuracy of 99+%. Several applications illustrate how this improved computing technology can successfully resolve numerous challenges of complex color segmentation in order to produce images from which quantitative information can be accurately extracted, thereby gain new perspectives on the in situ ecology of microorganisms. Examples include improvements in the quantitative analysis of (1) microbial abundance and phylotype diversity of single cells classified by their discriminating color within heterogeneous communities, (2) cell viability, (3) spatial relationships and intensity of bacterial gene expression involved in cellular communication between individual cells within rhizoplane biofilms, and (4) biofilm ecophysiology based on ribotype-differentiated radioactive substrate utilization. The stand-alone executable file plus user manual and tutorial images for this color segmentation computing application are freely available at . This improved computing technology opens new opportunities of imaging applications where discriminating colors really matter most, thereby strengthening quantitative microscopy-based approaches to advance microbial ecology in situ at individual single-cell resolution.     

基于F3Net显著性目标检测的蝴蝶图像前背景自动分割

【目的】具有复杂背景的蝴蝶图像前背景分割难度大。本研究旨在探索基于深度学习显著性目标检测的蝴蝶图像自动分割方法。【方法】应用DUTS-TR数据集训练F³Net显著性目标检测算法构建前背景预测模型,然后将模型用于具有复杂背景的蝴蝶图像数据集实现蝴蝶前背景自动分割。在此基础上,采用迁移学习方法,保持ResNet骨架不变,利用蝴蝶图像及其前景蒙板数据,使用交叉特征模块、级联反馈解码器和像素感知损失方法重新训练优化模型参数,得到更优的自动分割模型。同时,将其他5种基于深度学习显著性检测算法也用于自动分割,并比较了这些算法和F³Net算法的性能。【结果】所有算法均获得了很好的蝴蝶图像前背景分割效果,其中,F³Net是更优的算法,其7个指标S测度、E测度、F测度、平均绝对误差(MAE)、精度、召回率和平均IoU值分别为0.940, 0.945, 0.938, 0.024, 0.929,0.978和0.909。迁移学习则进一步提升了F³Net的上述指标值,分别为0.961, 0.964, 0.963, 0.013, 0.965, 0.967和0.938。【结论】研究结果证明结合迁移学习的F³Net算法是其中最优的分割方法。本研究提出的方法可用于野外调查中拍摄的昆虫图像的自动分割,并拓展了显著性目标检测方法的应用范围。     

Correlation statistics for cDNA microarray image analysis

In this paper, correlation of the pixels comprising a microarray spot is investigated. Subsequently, correlation statistics, namely, Pearson correlation and Spearman rank correlation, are used to segment the foreground and background intensity of microarray spots. The performance of correlation-based segmentation is compared to clustering-based (PAM, k-means) and seeded-region growing techniques (SPOT). It is shown that correlation-based segmentation is useful in flagging poorly hybridized spots, thus minimizing false-positives. The present study also raises the intriguing question of whether a change in correlation can be an indicator of differential gene expression.     

基于邻域最大差值与区域合并的油茶毒蛾幼虫图像分割

【目的】油茶树害虫的种类较多,其中油茶毒蛾Euproctis pseudoconspersa幼虫是危害较大的害虫之一。为完成油茶毒蛾幼虫的自动检测需要对其图像进行分割,油茶毒蛾幼虫图像的分割效果直接影响到图像的自动识别。【方法】本文提出了基于邻域最大差值与区域合并的油茶毒蛾幼虫图像分割算法,该方法主要是对相邻像素RGB的3个分量进行差值运算,最大差值若为0,则进行相邻像素合并得出初始的分割图像,根据合并准则进一步合并,得到最终分割结果。【结果】实验结果表明,该算法可以快速有效地将油茶毒蛾幼虫图像中的背景和虫体分割开来。【结论】使用JSEG分割算法、K均值聚类分割算法、快速几何可变形分割算法和本文算法对油茶毒蛾幼虫图像进行分割,将结果进行对比发现本文方法的分割效果最佳,且处理时间较短。     

CT image segmentation using FEM with optimized boundary condition

The authors propose a CT image segmentation method using structural analysis that is useful for objects with structural dynamic characteristics. Motivation of our research is from the area of genetic activity. In order to reveal the roles of genes, it is necessary to create mutant mice and measure differences among them by scanning their skeletons with an X-ray CT scanner. The CT image needs to be manually segmented into pieces of the bones. It is a very time consuming to manually segment many mutant mouse models in order to reveal the roles of genes. It is desirable to make this segmentation procedure automatic. Although numerous papers in the past have proposed segmentation techniques, no general segmentation method for skeletons of living creatures has been established. Against this background, the authors propose a segmentation method based on the concept of destruction analogy. To realize this concept, structural analysis is performed using the finite element method (FEM), as structurally weak areas can be expected to break under conditions of stress. The contribution of the method is its novelty, as no studies have so far used structural analysis for image segmentation. The method's implementation involves three steps. First, finite elements are created directly from the pixels of a CT image, and then candidates are also selected in areas where segmentation is thought to be appropriate. The second step involves destruction analogy to find a single candidate with high strain chosen as the segmentation target. The boundary conditions for FEM are also set automatically. Then, destruction analogy is implemented by replacing pixels with high strain as background ones, and this process is iterated until object is decomposed into two parts. Here, CT image segmentation is demonstrated using various types of CT imagery.     

Segmentation and intensity estimation of microarray images using a gamma-t mixture model

MOTIVATION: We present a new approach to the analysis of images for complementary DNA microarray experiments. The image segmentation and intensity estimation are performed simultaneously by adopting a two-component mixture model. One component of this mixture corresponds to the distribution of the background intensity, while the other corresponds to the distribution of the foreground intensity. The intensity measurement is a bivariate vector consisting of red and green intensities. The background intensity component is modeled by the bivariate gamma distribution, whose marginal densities for the red and green intensities are independent three-parameter gamma distributions with different parameters. The foreground intensity component is taken to be the bivariate t distribution, with the constraint that the mean of the foreground is greater than that of the background for each of the two colors. The degrees of freedom of this t distribution are inferred from the data but they could be specified in advance to reduce the computation time. Also, the covariance matrix is not restricted to being diagonal and so it allows for nonzero correlation between R and G foreground intensities. This gamma-t mixture model is fitted by maximum likelihood via the EM algorithm. A final step is executed whereby nonparametric (kernel) smoothing is undertaken of the posterior probabilities of component membership. The main advantages of this approach are: (1) it enjoys the well-known strengths of a mixture model, namely flexibility and adaptability to the data; (2) it considers the segmentation and intensity simultaneously and not separately as in commonly used existing software, and it also works with the red and green intensities in a bivariate framework as opposed to their separate estimation via univariate methods; (3) the use of the three-parameter gamma distribution for the background red and green intensities provides a much better fit than the normal (log normal) or t distributions; (4) the use of the bivariate t distribution for the foreground intensity provides a model that is less sensitive to extreme observations; (5) as a consequence of the aforementioned properties, it allows segmentation to be undertaken for a wide range of spot shapes, including doughnut, sickle shape and artifacts. RESULTS: We apply our method for gridding, segmentation and estimation to cDNA microarray real images and artificial data. Our method provides better segmentation results in spot shapes as well as intensity estimation than Spot and spotSegmentation R language softwares. It detected blank spots as well as bright artifact for the real data, and estimated spot intensities with high-accuracy for the synthetic data. AVAILABILITY: The algorithms were implemented in Matlab. The Matlab codes implementing both the gridding and segmentation/estimation are available upon request. SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.     

The application of convolution neural network based cell segmentation during cryopreservation

For most of the cells, water permeability and plasma membrane properties play a vital role in the optimal protocol for successful cryopreservation. Measuring the water permeability of cells during subzero temperature is essential. So far, there is no perfect segmentation technique to be used for the image processing task on subzero temperature accurately. The ice formation and variable background during freezing posed a significant challenge for most of the conventional segmentation algorithms. Thus, a robust and accurate segmentation approach that can accurately extract cells from extracellular ice that surrounding the cell boundary is needed. Therefore, we propose a convolutional neural network (CNN) architecture similar to U-Net but differs from those conventionally used in computer vision to extract all the cell boundaries as they shrank in the engulfing ice. The images used was obtained from the cryo-stage microscope, and the data was validated using the Hausdorff distance, means ± standard deviation for different methods of segmentation result using the CNN model. The experimental results prove that the typical CNN model extracts cell borders contour from the background in its subzero state more coherent and effective as compared to other traditional segmentation approaches.     

Fruit choice by red howler monkeys (Alouatta seniculus) in a tropical rain forest

This paper presents and discusses aspects of fruit selectivity by red howler monkeys (Alouatta seniculus) in relation with morphological characteristics of fruits. These data are used to provide an answer to the following questions: which are the fruit characteristics that lead fruit choice of howler monkeys and to what extent fruit characteristics play a role in seed dispersal by monkeys? The frugivorous diet of a troop of red howler monkeys was determined during a 2-year field study in French Guiana. The selection of fruit by howler monkeys was analyzed in relation to the fruit availability. Results showed that, although consumption followed availability, fruit species could be classified in three categories according to their selection ratio (percentage of consumption/percentage of abundance) as “high ranking,” “middle ranking,” and “low ranking” species. Also, the 97 species of fruit eaten by the monkeys were grouped according to the morphological characteristics thought to influence the monkeys' choice. This showed that howler monkeys consumed essentially fruits with juicy pulp, bright color, and a small number of well-protected seeds. Most of high ranking species had medium-sized fruits with yellow color, and low ranking species often had small fruits. However, howler monkeys are associated with the dispersal of seeds from fruit with a hard and indehiscent pericarp and/or large seeds, like those of the Sapotaceae family. Consequently, they can be considered as “specialized” frugivores for this fruit syndrome. © l996 Wiley-Liss, Inc.     

Foreground detection in video sequences with probabilistic self-organizing maps

Background modeling and foreground detection are key parts of any computer vision system. These problems have been addressed in literature with several probabilistic approaches based on mixture models. Here we propose a new kind of probabilistic background models which is based on probabilistic self-organising maps. This way, the background pixels are modeled with more flexibility. On the other hand, a statistical correlation measure is used to test the similarity among nearby pixels, so as to enhance the detection performance by providing a feedback to the process. Several well known benchmark videos have been used to assess the relative performance of our proposal with respect to traditional neural and non neural based methods, with favourable results, both qualitatively and quantitatively. A statistical analysis of the differences among methods demonstrates that our method is significantly better than its competitors. This way, a strong alternative to classical methods is presented.     

Donuts, scratches and blanks: robust model-based segmentation of microarray images

MOTIVATION: Inner holes, artifacts and blank spots are common in microarray images, but current image analysis methods do not pay them enough attention. We propose a new robust model-based method for processing microarray images so as to estimate foreground and background intensities. The method starts with a very simple but effective automatic gridding method, and then proceeds in two steps. The first step applies model-based clustering to the distribution of pixel intensities, using the Bayesian Information Criterion (BIC) to choose the number of groups up to a maximum of three. The second step is spatial, finding the large spatially connected components in each cluster of pixels. The method thus combines the strengths of the histogram-based and spatial approaches. It deals effectively with inner holes in spots and with artifacts. It also provides a formal inferential basis for deciding when the spot is blank, namely when the BIC favors one group over two or three. RESULTS: We apply our methods for gridding and segmentation to cDNA microarray images from an HIV infection experiment. In these experiments, our method had better stability across replicates than a fixed-circle segmentation method or the seeded region growing method in the SPOT software, without introducing noticeable bias when estimating the intensities of differentially expressed genes. AVAILABILITY: spotSegmentation, an R language package implementing both the gridding and segmentation methods is available through the Bioconductor project (http://www.bioconductor.org). The segmentation method requires the contributed R package MCLUST for model-based clustering (http://cran.us.r-project.org). CONTACT: fraley@stat.washington.edu.     

Identification of butterfly species with a single neural network system

Growing interest in conservation and biodiversity increased the demand for accurate and consistent identification of biological objects, such as insects, at the level of individual or species. Among the identification issues, butterfly identification at the species level has been strongly addressed because it is directly connected to the crop plants for human food and animal feed products. However, so far, the widely-used reliable methods were not suggested due to the complicated butterfly shape. In the present study, we propose a novel approach based on a back-propagation neural network to identify butterfly species. The neural network system was designed as a multi-class pattern classifier to identify seven different species. We used branch length similarity (BLS) entropies calculated from the boundary pixels of a butterfly shape as the input feature to the neural network. We verified the accuracy and efficiency of our method by comparing its performance to that of another single neural network system in which the binary values (0 or 1) of all pixels on an image shape are used as a feature vector. Experimental results showed that our method outperforms the binary image network in both accuracy and efficiency.     

An improved approach for the segmentation of starch granules in microscopic images

Background

Starches are the main storage polysaccharides in plants and are distributed widely throughout plants including seeds, roots, tubers, leaves, stems and so on. Currently, microscopic observation is one of the most important ways to investigate and analyze the structure of starches. The position, shape, and size of the starch granules are the main measurements for quantitative analysis. In order to obtain these measurements, segmentation of starch granules from the background is very important. However, automatic segmentation of starch granules is still a challenging task because of the limitation of imaging condition and the complex scenarios of overlapping granules.

Results

We propose a novel method to segment starch granules in microscopic images. In the proposed method, we first separate starch granules from background using automatic thresholding and then roughly segment the image using watershed algorithm. In order to reduce the oversegmentation in watershed algorithm, we use the roundness of each segment, and analyze the gradient vector field to find the critical points so as to identify oversegments. After oversegments are found, we extract the features, such as the position and intensity of the oversegments, and use fuzzy c-means clustering to merge the oversegments to the objects with similar features. Experimental results demonstrate that the proposed method can alleviate oversegmentation of watershed segmentation algorithm successfully.

Conclusions

We present a new scheme for starch granules segmentation. The proposed scheme aims to alleviate the oversegmentation in watershed algorithm. We use the shape information and critical points of gradient vector flow (GVF) of starch granules to identify oversegments, and use fuzzy c-mean clustering based on prior knowledge to merge these oversegments to the objects. Experimental results on twenty microscopic starch images demonstrate the effectiveness of the proposed scheme.

     

Sex-linked inheritance of host-plant specialization in a polyphagous butterfly

I investigated the genetic background of intraspecific variation in oviposition specificity in the generalist butterfly Polygonia c-album. Using reciprocal crosses between two populations that differ in their degree of specialization, I show that specificity is strongly sex-linked. This indicates that genes determining this difference are located primarily on the paternally inherited X-chromosome. The results suggest that intraspecific differences in specificity are caused by the same genetic mechanisms that have been shown to determine interspecific differences in host-plant ranking in other butterflies. Accordingly, the common assumption that specialization and ranking are determined by fundamentally different mechanisms was not supported.     

Spot Detection and Image Segmentation in DNA Microarray Data

Following the invention of microarrays in 1994, the development and applications of this technology have grown exponentially. The numerous applications of microarray technology include clinical diagnosis and treatment, drug design and discovery, tumour detection, and environmental health research. One of the key issues in the experimental approaches utilising microarrays is to extract quantitative information from the spots, which represent genes in a given experiment. For this process, the initial stages are important and they influence future steps in the analysis. Identifying the spots and separating the background from the foreground is a fundamental problem in DNA microarray data analysis. In this review, we present an overview of state-of-the-art methods for microarray image segmentation. We discuss the foundations of the circle-shaped approach, adaptive shape segmentation, histogram-based methods and the recently introduced clustering-based techniques. We analytically show that clustering-based techniques are equivalent to the one-dimensional, standard k-means clustering algorithm that utilises the Euclidean distance.     

Global warming and excess nitrogen may induce butterfly decline by microclimatic cooling

Global warming may explain the current poleward shift of species distributions. However, paradoxically, climatic warming can lead to microclimatic cooling in spring by advancing plant growth, an effect worsened by excess nitrogen. We suggest that spring-developing but thermophilous organisms, such as butterflies hibernating as egg or larva, are particularly sensitive to the cooling of microclimates. Using published data on butterfly trends in distribution, we report a comparatively greater decline in egg–larva hibernators in European countries with oceanic climates and high nitrogen deposition, which supports this explanation. Furthermore, trends in abundance from a nationwide butterfly monitoring scheme reveal a 63% decrease over 13 years (1992–2004) for egg–larva hibernators in the Netherlands, contrasting with a nonsignificant trend in adult–pupa hibernators. This evidence supports the hypothesis that these environmental changes pose new threats to spring-developing, thermophilous species. We underline the threat of climate change to biodiversity, as previously suggested on the basis of mobility, habitat fragmentation and evolutionary adaptation, but we here emphasize a different ecological axis of change in habitat quality.     

SMALL-LABS: Measuring Single-Molecule Intensity and Position in Obscuring Backgrounds

Single-molecule and super-resolution imaging relies on successful, sensitive, and accurate detection of the emission from fluorescent molecules. Yet, despite the widespread adoption of super-resolution microscopies, single-molecule data processing algorithms can fail to provide accurate measurements of the brightness and position of molecules in the presence of backgrounds that fluctuate significantly over time and space. Thus, samples or experiments that include obscuring backgrounds can severely, or even completely, hinder this process. To date, no general data analysis approach to this problem has been introduced that is capable of removing obscuring backgrounds for a wide variety of experimental modalities. To address this need, we present the Single-Molecule Accurate LocaLization by LocAl Background Subtraction (SMALL-LABS) algorithm, which can be incorporated into existing single-molecule and super-resolution analysis packages to accurately locate and measure the intensity of single molecules, regardless of the shape or brightness of the background. Accurate background subtraction is enabled by separating the foreground from the background based on differences in the temporal variations of the foreground and the background (i.e., fluorophore blinking, bleaching, or moving). We detail the function of SMALL-LABS here, and we validate the SMALL-LABS algorithm on simulated data as well as real data from single-molecule imaging in living cells.     

A computational model of rapid task-related plasticity of auditory cortical receptive fields

Receptive field properties of neurons in A1 can rapidly adapt their shapes during task performance in accord with specific task demands and salient sensory cues (Fritz et al., Hearing Research, 206:159–176, 2005a, Nature Neuroscience, 6: 1216–1223, 2003). Such modulatory changes selectively enhance overall cortical responsiveness to target (foreground) sounds and thus increase the likelihood of detection against the background of reference sounds. In this study, we develop a mathematical model to describe how enhancing discrimination between two arbitrary classes of sounds can lead to the observed receptive field changes in a variety of spectral and temporal discrimination tasks. Cortical receptive fields are modeled as filters that change their spectro-temporal tuning properties so as to respond best to the discriminatory acoustic features between foreground and background stimuli. We also illustrate how biologically plausible constraints on the spectro-temporal tuning of the receptive fields can be used to optimize the plasticity. Results of the model simulations are compared to published data from a variety of experimental paradigms.     

基于颜色名和OpponentSIFT特征的鳞翅目昆虫图像识别

【目的】本研究旨在探索使用先进的计算机视觉技术实现对昆虫图像的自动分类方法。【方法】通过预处理对采集的昆虫标本图像去除背景,获得昆虫图像的前景蒙板,并由蒙板确定的轮廓计算出前景图像的最小包围盒,剪切出由最小包围盒确定的前景有效区域,然后对剪切得到的图像进行特征提取。首先提取颜色名特征,把原来的RGB（Red-Green-Blue）图像的像素值映射到11种颜色名空间,其值表示RGB值属于该颜色名的概率,每个颜色名平面划分成3×3像素大小的网格,用每格的概率均值作为网格中心点的描述子,最后用空阈金字塔直方图统计的方式形成颜色名视觉词袋特征;其次提取OpponentSIFT(Opponent Scale Invariant Feature Transform)特征,首先把RGB图像变换到对立色空间,对该空间每通道提取SIFT特征,最后用空域池化和直方图统计方法形成OpponentSIFT视觉词袋。将两种词袋特征串接后得到该昆虫图像的特征向量。使用昆虫图像样本训练集提取到的特征向量训练SVM（Support Vector Machine）分类器,使用这些训练得到的分类器即可实现对鳞翅目昆虫的分类识别。【结果】该方法在包含10种576个样本的昆虫图像数据库中进行了测试,取得了100%的识别正确率。【结论】试验结果证明基于颜色名和OpponentSIFT特征可以有效实现对鳞翅目昆虫图像的识别。     

Square-cut: a segmentation algorithm on the basis of a rectangle shape

We present a rectangle-based segmentation algorithm that sets up a graph and performs a graph cut to separate an object from the background. However, graph-based algorithms distribute the graph's nodes uniformly and equidistantly on the image. Then, a smoothness term is added to force the cut to prefer a particular shape. This strategy does not allow the cut to prefer a certain structure, especially when areas of the object are indistinguishable from the background. We solve this problem by referring to a rectangle shape of the object when sampling the graph nodes, i.e., the nodes are distributed non-uniformly and non-equidistantly on the image. This strategy can be useful, when areas of the object are indistinguishable from the background. For evaluation, we focus on vertebrae images from Magnetic Resonance Imaging (MRI) datasets to support the time consuming manual slice-by-slice segmentation performed by physicians. The ground truth of the vertebrae boundaries were manually extracted by two clinical experts (neurological surgeons) with several years of experience in spine surgery and afterwards compared with the automatic segmentation results of the proposed scheme yielding an average Dice Similarity Coefficient (DSC) of 90.97±2.2%.