Objective threshold selection procedure (OTS) for segmentation of scanning laser confocal microscope images

The determination of volumes and interface areas from confocal laser scanning microscopy (CLSM) images requires the identification of component objects by segmentation. An automated method for the determination of segmentation thresholds for CLSM imaging of biofilms was developed. The procedure, named objective threshold selection (OTS), is a three-dimensional development of the approach introduced by the popular robust automatic threshold selection (RATS) method. OTS is based on the statistical properties of local gray-values and gradients in the image. By characterizing the dependence between a volumetric feature and the intensity threshold used for image segmentation, the former can be determined with an arbitrary confidence level, with no need for user intervention. The identification of an objective segmentation procedure renders the possibility for the full automation of volume and interfacial area measurement. Images from two distinct biofilm systems, acquired using different experimental techniques and instrumental setups were segmented by OTS to determine biofilm volume and interfacial area. The reliability of measurements for each case was analyzed to identify optimal procedure for image acquisition. The automated OTS method was shown to reproduce values obtained manually by an experienced operator.     

Three-dimensional biofilm image reconstruction for assessing structural parameters

Parameters representing three-dimensional (3D) biofilm structure are quantified from confocal laser-scanning microscope (CLSM) images. These 3D parameters describe the distribution of biomass pixels within the space occupied by a biofilm; however, they lack a direct connection to biofilm activity. As a result, researchers choose a handful of parameters without there being a consensus on a standard set of parameters. We hypothesized that a select 3D parameter set could be used to reconstruct a biofilm image and that the reconstructed and original biofilm images would have similar activities. To test this hypothesis, an algorithm was developed to reconstruct a biofilm image with parameters identical to those of the original CLSM image. We introduced an objective method to assess the reconstruction algorithm by comparing the activities of the original and reconstructed biofilm images. We found that biofilm images with identical structural parameters showed nearly identical activities and substrate concentration profiles. This implies that the set containing all common structural parameters can successfully describe biofilm structure. This finding is significant, as it opens the door to the next step, of finding a smaller standard set of biofilm structural parameters that can be used to compare biofilm structure.     

Pré-traitement d’un volume IRM par filtrage anisotrope robuste pour la segmentation de l’épaule

A novel pre-treatment process for image segmentation, based on anisotropic diffusion and robust statistics, is presented in this paper. Image smoothing with edge preservation is shown to help upper limb segmentation (shoulder segmentation in particular) in MRI datasets. The anisotropic diffusion process is mainly controlled by an automated stopping function that depends on the values of voxel gradient. Voxel gradients are divided into two classes: one for high values, corresponding to edge voxels or noisy voxels, one for low values. The anisotropic diffusion process is also controlled by a threshold on voxel gradients that separates both classes. A global estimation of this threshold parameter is classically used. In this paper, we propose a new method based on a local robust estimation. It allows a better removing of noise while preserving edges in the images. An entropy criterion is used to quantify the ability of the algorithm to remove noise with different signal to noise ratios in synthetic images. Another quantitative evaluation criterion based on the Pratt Figure of Merit (FOM) is proposed to evaluate the edge preservation and their location accuracy with respect to a manual segmentation. The results on synthetic and MRI data of shoulder show the assets of the local model in terms of areas homogeneity and edges locations.     

早期胚胎全场OCT图像的边界去模糊研究

全场光学相干层析成像技术(全场OCT)是研究早期胚胎形态发育的最理想成像设备,然而所采集图像难免受噪声干扰.这些噪声可模糊早期胚胎内不同组织结构的边界,从而给基于图像边界的结构划分带来干扰.为解决这一问题,本文运用中值滤波、维纳滤波、各向异性扩散算法处理全场OCT获得的早期胚胎图像,并运用信噪比、均方误差、峰值信噪比和边缘保留等指标评价图像处理效果.结果表明:经各向异性扩散算法处理的早期胚胎图像,可完整地保留原始图像信息,且边界最清晰,视觉效果最好.     

Confocal DNA cytometry: a contour-based segmentation algorithm for automated three-dimensional image segmentation

BACKGROUND: Confocal laser scanning microscopy (CLSM) presents the opportunity to perform three-dimensional (3D) DNA content measurements on intact cells in thick histological sections. So far, these measurements have been performed manually, which is quite time-consuming. METHODS: In this study, an intuitive contour-based segmentation algorithm for automatic 3D CLSM image cytometry of nuclei in thick histological sections is presented. To evaluate the segmentation algorithm, we measured the DNA content and volume of human liver and breast cancer nuclei in 3D CLSM images. RESULTS: A high percentage of nuclei could be segmented fully automatically (e.g., human liver, 92%). Comparison with (time-consuming) interactive measurements on the same CLSM images showed that the results were well correlated (liver, r = 1.00; breast, r = 0.92). CONCLUSIONS: Automatic 3D CLSM image cytometry enables measurement of volume and DNA content of large numbers of nuclei in thick histological sections within an acceptable time. This makes large-scale studies feasible, whereby the advantages of CLSM can be exploited fully. The intuitive modular segmentation algorithm presented in this study detects and separates overlapping objects, also in two-dimensional (2D) space. Therefore, this algorithm may also be suitable for other applications.     

Improved and Robust Detection of Cell Nuclei from Four Dimensional Fluorescence Images

Segmentation-free direct methods are quite efficient for automated nuclei extraction from high dimensional images. A few such methods do exist but most of them do not ensure algorithmic robustness to parameter and noise variations. In this research, we propose a method based on multiscale adaptive filtering for efficient and robust detection of nuclei centroids from four dimensional (4D) fluorescence images. A temporal feedback mechanism is employed between the enhancement and the initial detection steps of a typical direct method. We estimate the minimum and maximum nuclei diameters from the previous frame and feed back them as filter lengths for multiscale enhancement of the current frame. A radial intensity-gradient function is optimized at positions of initial centroids to estimate all nuclei diameters. This procedure continues for processing subsequent images in the sequence. Above mechanism thus ensures proper enhancement by automated estimation of major parameters. This brings robustness and safeguards the system against additive noises and effects from wrong parameters. Later, the method and its single-scale variant are simplified for further reduction of parameters. The proposed method is then extended for nuclei volume segmentation. The same optimization technique is applied to final centroid positions of the enhanced image and the estimated diameters are projected onto the binary candidate regions to segment nuclei volumes.Our method is finally integrated with a simple sequential tracking approach to establish nuclear trajectories in the 4D space. Experimental evaluations with five image-sequences (each having 271 3D sequential images) corresponding to five different mouse embryos show promising performances of our methods in terms of nuclear detection, segmentation, and tracking. A detail analysis with a sub-sequence of 101 3D images from an embryo reveals that the proposed method can improve the nuclei detection accuracy by 9 over the previous methods, which used inappropriate large valued parameters. Results also confirm that the proposed method and its variants achieve high detection accuracies ( 98 mean F-measure) irrespective of the large variations of filter parameters and noise levels.     

Biofilm structure differentiation based on multi-resolution analysis

Quantitative parameters for describing the morphology of biofilms are crucial towards establishing the influence of growing conditions on biofilm structure. Parameters used in earlier studies generally fail to differentiate complex three-dimensional structures. This article presents a novel approach of defining a parameter vector based on the energy signature of multi-resolution analysis, which was applied to differentiating biofilm structures from confocal laser scanning microscopy (CLSM) biofilm images. The parameter vector distinguished differences in the spatial arrangements of synthetic images. For real CLSM images, this parameter vector detected subtle differences in biofilm structure for three sample cases: (1) two adjacent images of a CLSM stack; (2) two partial stacks from the same CLSM stack with equal numbers of images but spatially offset by one image; and (3) three complete CLSM stacks from different bacterial strains. It was also observed that filtering the noise in CLSM images enhanced the sensitivity of the differentiation using our parameter vector.     

Biofilm structure differentiation based on multi-resolution analysis

Quantitative parameters for describing the morphology of biofilms are crucial towards establishing the influence of growing conditions on biofilm structure. Parameters used in earlier studies generally fail to differentiate complex three-dimensional structures. This article presents a novel approach of defining a parameter vector based on the energy signature of multi-resolution analysis, which was applied to differentiating biofilm structures from confocal laser scanning microscopy (CLSM) biofilm images. The parameter vector distinguished differences in the spatial arrangements of synthetic images. For real CLSM images, this parameter vector detected subtle differences in biofilm structure for three sample cases: (1) two adjacent images of a CLSM stack; (2) two partial stacks from the same CLSM stack with equal numbers of images but spatially offset by one image; and (3) three complete CLSM stacks from different bacterial strains. It was also observed that filtering the noise in CLSM images enhanced the sensitivity of the differentiation using our parameter vector.     

3D cell nuclei segmentation based on gradient flow tracking

Background  

Reliable segmentation of cell nuclei from three dimensional (3D) microscopic images is an important task in many biological studies. We present a novel, fully automated method for the segmentation of cell nuclei from 3D microscopic images. It was designed specifically to segment nuclei in images where the nuclei are closely juxtaposed or touching each other. The segmentation approach has three stages: 1) a gradient diffusion procedure, 2) gradient flow tracking and grouping, and 3) local adaptive thresholding.     

Unsupervised Retinal Vessel Segmentation Using Combined Filters

Image segmentation of retinal blood vessels is a process that can help to predict and diagnose cardiovascular related diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels’ appearance. This work proposes an unsupervised method for the segmentation of retinal vessels images using a combined matched filter, Frangi’s filter and Gabor Wavelet filter to enhance the images. The combination of these three filters in order to improve the segmentation is the main motivation of this work. We investigate two approaches to perform the filter combination: weighted mean and median ranking. Segmentation methods are tested after the vessel enhancement. Enhanced images with median ranking are segmented using a simple threshold criterion. Two segmentation procedures are applied when considering enhanced retinal images using the weighted mean approach. The first method is based on deformable models and the second uses fuzzy C-means for the image segmentation. The procedure is evaluated using two public image databases, Drive and Stare. The experimental results demonstrate that the proposed methods perform well for vessel segmentation in comparison with state-of-the-art methods.     

Liver vessel segmentation based on extreme learning machine

Liver-vessel segmentation plays an important role in vessel structure analysis for liver surgical planning. This paper presents a liver-vessel segmentation method based on extreme learning machine (ELM). Firstly, an anisotropic filter is used to remove noise while preserving vessel boundaries from the original computer tomography (CT) images. Then, based on the knowledge of prior shapes and geometrical structures, three classical vessel filters including Sato, Frangi and offset medialness filters together with the strain energy filter are used to extract vessel structure features. Finally, the ELM is applied to segment liver vessels from background voxels. Experimental results show that the proposed method can effectively segment liver vessels from abdominal CT images, and achieves good accuracy, sensitivity and specificity.     

Segmentation of thin structures in electron micrographs using orientation fields

In this paper, we introduce a new approach for segmenting thin structures in electron micrographs. We introduce two new transforms, the Line Filter Transform (LFT) and the Orientation Filter Transform (OFT). The LFT can be viewed as an alternative to anisotropic diffusion algorithms that is particularly useful for thin structures. The OFT utilizes geometrical information about the structure by measuring correlations of local orientations in the image. By combining these methods with a contour extraction and labeling method we construct a segmentation method for thin structures in 2D images. We discuss how the method can be applied slice-by-slice to electron tomograms and illustrate the process by constructing two models of membrane structures from cellular tomograms. The suggested method has the advantage of being relatively insensitive to non-uniform contrast and high-contrast features such as ribosomes.     

The use of microscopy and three-dimensional visualization to evaluate the structure of microbial biofilms cultivated in the calgary biofilm device

Microbes frequently live within multicellular, solid surface-attached assemblages termed biofilms. These microbial communities have architectural features that contribute to population heterogeneity and consequently to emergent cell functions. Therefore, three-dimensional (3D) features of biofilm structure are important for understanding the physiology and ecology of these microbial systems. This paper details several protocols for scanning electron microscopy and confocal laser scanning microscopy (CLSM) of biofilms grown on polystyrene pegs in the Calgary Biofilm Device (CBD). Furthermore, a procedure is described for image processing of CLSM data stacks using amira™, a virtual reality tool, to create surface and/or volume rendered 3D visualizations of biofilm microorganisms. The combination of microscopy with microbial cultivation in the CBD — an apparatus that was designed for highthroughput susceptibility testing — allows for structure-function analysis of biofilms under multivariate growth and exposure conditions.     

Object Segmentation and Ground Truth in 3D Embryonic Imaging

Many questions in developmental biology depend on measuring the position and movement of individual cells within developing embryos. Yet, tools that provide this data are often challenged by high cell density and their accuracy is difficult to measure. Here, we present a three-step procedure to address this problem. Step one is a novel segmentation algorithm based on image derivatives that, in combination with selective post-processing, reliably and automatically segments cell nuclei from images of densely packed tissue. Step two is a quantitative validation using synthetic images to ascertain the efficiency of the algorithm with respect to signal-to-noise ratio and object density. Finally, we propose an original method to generate reliable and experimentally faithful ground truth datasets: Sparse-dense dual-labeled embryo chimeras are used to unambiguously measure segmentation errors within experimental data. Together, the three steps outlined here establish a robust, iterative procedure to fine-tune image analysis algorithms and microscopy settings associated with embryonic 3D image data sets.     

Investigation of the mesoscale structure and volumetric features of biofilms using optical coherence tomography

Optical coherence tomography (OCT) was successfully applied to visualize the mesoscale structure of three different heterotrophic biofilms. For this purpose, biofilm volumes of 4 × 4 × 1.6 mm³ were scanned with spatial resolutions lower than 20 µm within an acquisition time of 2 min. A heterogeneous structure was detected for biofilms cultivated in laminar as well as transient flow conditions. The structure was found to be more homogeneous for the biofilm grown in turbulent flow. This biofilm structure was characterized by a volumetric porosity of 0.36, whereas the porosity calculated for biofilms grown in laminar and transient conditions was 0.65. These results were directly generated from the distribution of porosity calculated from the OCT images acquired and can be linked to structural properties. Up to now, the mesoscale biofilm structure was only observable with time‐consuming and expensive studies, for example, magnetic resonance microscopy. OCT will most certainly be helpful for improved understanding and prediction of biofilm physics with respect to macroscale processes, for example, mass transfer and detachment as the information about mesoscale is easily accessible using this method. In the context of this study, we show that CLSM images do not necessarily provide an accurate representation of the biofilm structure at the mesoscale. Additionally, the typical characteristic parameters obtained from CLSM image stacks differ largely from those calculated from OCT images. Nevertheless, to determine the local distribution of biofilm constituents, microscopic methods such as confocal laser scanning microscopy are required. Biotechnol. Bioeng. 2010;107: 844–853. © 2010 Wiley Periodicals, Inc.     

Feature-based,Automated Segmentation of Cerebral Infarct Patterns Using T 2- and Diffusion-weighted Imaging

Diffusion-weighted imaging enables the diagnosis of cerebral ischemias very early, thus supporting therapies such as thrombolysis. However, morphology and tissue-characterizing parameters (e.g. relaxation times or water diffusion) may vary strongly in ischemic regions, indicating different underlying pathologic processes. As the determination of the parameters by a supervised segmentation is very time consuming, we evaluated whether different infarct patterns may be segmented by an automated, multidimensional feature-based method using a unified segmentation procedure. Ischemias were classified into 5 characteristic patterns. For each class, a 3D histogram based on T 2 - and diffusion-weighted images as well as calculated apparent diffusion coefficients (ADC) was generated from a representative data set. Healthy and pathologic tissue classes were segmented in the histogram as separate, local density maxima with freely shaped borders. Segmentation control parameters were optimized in a 3-step procedure. The method was evaluated using synthetic images as well as results of a supervised segmentation. For the analysis of cerebral ischemias, the optimal control parameter set led to sensitivities and specificities between 1.0 and 0.9.     

Feature-based,automated segmentation of cerebral infarct patterns using T2- and diffusion-weighted imaging

Diffusion-weighted imaging enables the diagnosis of cerebral ischemias very early, thus supporting therapies such as thrombolysis. However, morphology and tissue-characterizing parameters (e.g. relaxation times or water diffusion) may vary strongly in ischemic regions, indicating different underlying pathologic processes. As the determination of the parameters by a supervised segmentation is very time consuming, we evaluated whether different infarct patterns may be segmented by an automated, multidimensional feature-based method using a unified segmentation procedure. Ischemias were classified into 5 characteristic patterns. For each class, a 3D histogram based on T(2)- and diffusion-weighted images as well as calculated apparent diffusion coefficients (ADC) was generated from a representative data set. Healthy and pathologic tissue classes were segmented in the histogram as separate, local density maxima with freely shaped borders. Segmentation control parameters were optimized in a 3-step procedure. The method was evaluated using synthetic images as well as results of a supervised segmentation. For the analysis of cerebral ischemias, the optimal control parameter set led to sensitivities and specificities between 1.0 and 0.9.     

冷激条件下预形成副溶血性弧菌生物被膜的发展变化

【目的】生物被膜可附着在食品或医药生物材料表面引起持续性的感染,而现今极少有关于温度变化对预形成生物被膜影响的研究。本文分析了冷激条件下副溶血性弧菌预形成生物被膜的发展变化。【方法】以改进的结晶紫染色法检测生物被膜总量,以改进的超声法和Lowry法量化胞外多糖和蛋白质,以RNA试剂盒提取并纯化生物被膜形成的相关基因。同时,激光共聚焦扫描显微镜直观显示了冷激条件下预形成生物被膜形态结构的变化,并深入分析了生物被膜结构的变化,以及生物被膜结构参数和基因转录的相关性。【结果】在冷激条件下,副溶血性弧菌生物被膜总量增加,同时,副溶血性弧菌预形成的生物被膜胞外多聚物的主要成分胞外多糖和蛋白质也逐渐增加,被膜形成相关鞭毛基因和毒力基因的转录活跃。生物被膜的平均厚度(MT)、平均扩散距离(ADD)、孔隙率(P)、生物被膜粗糙度(BR)和均匀性(H)在冷激过程中也发生了变化,同时这些参数之间存在显著相关性(P<0.01),而生物被膜结构参数与生物被膜相关基因转录的相关性较弱(P<0.05)。【结论】因此,4°C和10°C冷激不能完全抑制副溶血性弧菌预形成生物被膜的生长,风险评估人员在制定控制食源性感染风险的战略时应考虑到这一因素。     

激光扫描共聚焦显微镜观察细菌生物膜形成的方法学研究

目的:建立激光扫描共聚焦显微镜观察生物膜形成过程的方法,为进一步研究生物膜的形成机制奠定基础。方法:以临床分离金葡菌X428为研究对象,在盖玻片上形成生物膜,分别于接种后的4、8、12、16、24和48h取出玻片,采用免疫荧光技术标记多糖和细菌,激光扫描共聚焦显微镜(CLSM)观察生物膜形成情况。结果:取得了生物膜形成过程的不同时间点的CLSM图像,4h时细菌在盖玻片上粘附形成小菌落,8h和12h细菌聚集成簇,多糖基质产生并逐渐增多,至16h形成成熟生物膜结构;24h和48h生物膜已经播散,其结构变小。结论:应用免疫荧光技术和激光扫描共聚焦显微镜技术研究生物膜形成过程是一种简便可行的方法。