Automated segmentation of wood fibres in micro‐CT images of paper

A novel algorithm has been developed and validated to isolate individual papermaking fibres in micro‐computed tomographic images of paper handsheets as a first step to characterize the structure of the paper. The three‐step fibre segmentation algorithm segments the papermaking fibres by (i) tracking the hollow inside the fibres via a modified connected component methodology, (ii) extracting the fibre walls using a distance transform and (iii) labelling the fibres through collapsed sections by a final refinement step. Furthermore, postprocessing algorithms have been developed to calculate the length and coarseness of the segmented fibres. The fibre segmentation algorithm is the first ever reported method for the automated segmentation of the tortuous three‐dimensional morphology of papermaking fibres within microstructural images of paper handsheets. The method is not limited to papermaking fibres, but can be applied to any material consisting of tortuous and hollow fibres.     

Fluorescence microscopy of rat embryo sections stained with haematoxylin–eosin and Masson's trichrome method

The fluorescence pattern induced by haematoxylin–eosin (HE) and Masson's trichrome (MT) staining methods on paraffin sections of rat embryos (from 13 to 18 days old) has been studied. Using optimal excitation (green light, 545 nm), HE- or MT-stained sections showed a selective red emission of the acidophilic tissue components, which was due to eosin Y in the case of HE and to acid fuchsin and/or xylidine ponceau in the case of MT. The fluorescence intensity induced by these anionic dyes was variable and related to the substrate nature and the embryo age. Whereas in young embryos only the immature red blood cells showed a noticeable fluorescence, in the oldest embryos there were also other tissue components that selectively fluoresced with these dyes, in particular fibre lens cells, elastic fibres, zymogen granules and muscle cells. Spectrofluorometric studies on free dyes and densitometric analysis of protein blots confirmed microscopical observations. Our results indicate that the standard HE and MT staining methods can be used in recognizing the appearance of specific protein structures in embryonic tissues by means of fluorescence microscopy.     

Cryoultramicrotomy of muscle: improved preservation and resolution of muscle ultrastructure using negatively stained ultrathin cryosections

Ultrathin sections of rapidly frozen, briefly pre-treated muscle tissue are cut and thereafter are thawed and contrasted using a negative staining technique. The method has provided micrographs in which the in-vivo order in the muscle fibres has been preserved well enough to enable both a more complete interpretation of X-ray diffraction evidence from muscle, and also a gain of new ultrastructural information on aspects of myofibril and myofilament architecture in different types of fibre. Examples here are taken from chicken, rabbit and fish muscles and show both the M-band and the bridge region of the A-band in great detail. To enhance the detail in the original images, one-dimensional (1-D) and 2-D averaging techniques (lateral smearing and step averaging, respectively) are used. Although there is major shrinkage in section thickness to about one-third of its original value, demonstrated here for the first time is the fact that the characteristic A-band lattice planes are preserved in these sections in 3-D. This confirms the usefulness of cryosections not just for 1-D and 2-D image processing, but also for 3-D reconstruction. Thus, in combination with techniques of image processing, cryoultramicrotomy can give the muscle morphologist the detailed data that are needed to match the molecular biologists, biochemists and immunologists in the interpretation of their data about physiological and pathophysiological events in muscle fibres at the macromolecular level.     

Entropy‐assisted image segmentation for nano‐ and micro‐sized networks

Image analysis is an important tool for characterizing nano/micro network structures. To understand the connection, organization and proper alignment of network structures, the knowledge of the segments that represent the materials inside the image is very necessary. Image segmentation is generally carried out using statistical methods. In this study, we developed a simple and reliable masking method that improves the performance of the indicator kriging method by using entropy. This method selectively chooses important pixels in an image (optical or electron microscopy image) depending on the degree of information required to assist the thresholding step. Reasonable threshold values can be obtained by selectively choosing important pixels in a complex network image composed of extremely large numbers of thin and narrow objects. Thus, the overall image segmentation can be improved as the number of disconnected objects in the network is minimized. Moreover, we also proposed a new method for analyzing high‐pixel resolution images on a large scale and optimized the time‐consuming steps such as covariance estimation of low‐pixel resolution image, which is rescaled by performing the affine transformation on high‐pixel resolution images. Herein, image segmentation is executed in the original high‐pixel resolution image. This entropy‐based masking method of low‐pixel resolution significantly decreases the analysis time without sacrificing accuracy.     

Evaluation of the effective cross‐sections for recombination and trapping in the case of pure spinel

In this paper, we have investigated the evolution of the secondary electron emission in the case of pure spinel during electron irradiation, achieved in a scanning electron microscope at room temperature, which is derived from the measurement of the induced and the secondary electron currents. It was observed from the experimental results, that there are two regimes during the charging process: a plateau followed by a linear variation, which are better identified by plotting the logarithm of the secondary electron emission yield lnσ as function of the total surface density of trapped charges in the material Q_T. For positive charging, E₀ = 1.1 and 5 keV, the slope of the linear part, whose value is of about 10^?10 cm² charge^?1, is independent of the primary electron energy. It is interpreted as a microscopic cross section for electron–hole recombination. For negative charging of pure spinel, E₀ = 15 and 30 keV, the slope is associated with an electron trapping cross section close to 10^?14 cm² charge^?1, which can be assigned to the microscopic cross section for electron trapping. This trapping cross section is four orders of magnitude lower than the recombination one.     

Technical advances in the sectioning of dental tissue and of on‐section cross‐linked collagen detection in mineralized teeth

Immunohistochemical detection of cross‐linked fibrillar collagens in mineralized tissues is much desired for exploring the mechanisms of biomineralization in health and disease. Mineralized teeth are impossible to section when embedded in conventional media, thus limiting on‐section characterization of matrix proteins by immunohistochemistry. We hypothesized that by using an especially formulated acrylic resin suitable for mineralized dental tissues, not only sectioning of teeth would be possible, but also our recently developed immunofluorescence labeling technique would be amenable to fully calcified tissues for characterization of dentinal fibrillar collagens, which remains elusive. The hypothesis was tested on fixed rodent teeth embedded in Technovit 9100 New®. It was possible to cut thin (1 μm) sections of mineralized teeth, and immunofluorescence characterization of cross‐linked type I fibrillar collagen was selected due to its abundance in dentine. Decalcified samples of teeth embedded in paraffin wax were also used to compare immunolabeling from either method using the same immunoreagents in equivalent concentrations. In the decalcified tissue sections, type I collagen labeling in the dentine along the tubules was “patchy” and the signal in the predentine was very weak. However, enhanced signal in mineralized samples with type I collagen was detected not only in the predentine but also at the limit between intertubular dentine, within the elements of the enamel organ and subgingival stroma. This report offers advances in sectioning mineralized dental tissues and allows the application of immunofluorescence not only for on‐section protein detection but importantly for detecting cross‐linked fibrous collagens in both soft and mineralized tissue sections. Microsc. Res. Tech. 73:741–745, 2010. © 2009 Wiley‐Liss, Inc.     

Phase‐contrast hard X‐ray microscopy using synchrotron radiation for the properties of skeletal muscle in mouse hind limbs

Radiation beam interface contrast X‐ray microscopy provides resolution of a few dozen nanometers from fixed whole muscle biopsies, allowing better reconstruction of the microstructure of the muscle than is currently possible with classic histological techniques. Fixed soleus muscle biopsies have been evaluated from the walk‐in mouse model using phase‐contrast X‐ray microscopy, and results presented that corroborate the accuracy of the method used, and its potential for application in physiotherapy and occupational therapy studies. We believe that this method will enhance existing morphometric methods of analysis, leading to accurate reconstruction of other thick specimens that would otherwise require thin sectioning and reconstruction through deconvolution algorithms.     

3D multiscale segmentation and morphological analysis of x‐ray microtomography from cold‐sprayed coatings

X‐ray microtomography from cold‐sprayed coatings brings a new insight on this deposition process. A noise‐tolerant segmentation algorithm is introduced, based on the combination of two segmentations: a deterministic multiscale segmentation and a stochastic segmentation. The stochastic approach uses random Poisson lines as markers. Results on a X‐ray microtomographic image of aluminium particles are presented and validated.     

Breast histopathology image segmentation using spatio‐colour‐texture based graph partition method

This paper proposes a novel integrated spatio‐colour‐texture based graph partitioning method for segmentation of nuclear arrangement in tubules with a lumen or in solid islands without a lumen from digitized Hematoxylin–Eosin stained breast histology images, in order to automate the process of histology breast image analysis to assist the pathologists. We propose a new similarity based super pixel generation method and integrate it with texton representation to form spatio‐colour‐texture map of Breast Histology Image. Then a new weighted distance based similarity measure is used for generation of graph and final segmentation using normalized cuts method is obtained. The extensive experiments carried shows that the proposed algorithm can segment nuclear arrangement in normal as well as malignant duct in breast histology tissue image. For evaluation of the proposed method the ground‐truth image database of 100 malignant and nonmalignant breast histology images is created with the help of two expert pathologists and the quantitative evaluation of proposed breast histology image segmentation has been performed. It shows that the proposed method outperforms over other methods.     

Automated detection of fluorescent cells in in‐resin fluorescence sections for integrated light and electron microscopy

Integrated array tomography combines fluorescence and electron imaging of ultrathin sections in one microscope, and enables accurate high‐resolution correlation of fluorescent proteins to cell organelles and membranes. Large numbers of serial sections can be imaged sequentially to produce aligned volumes from both imaging modalities, thus producing enormous amounts of data that must be handled and processed using novel techniques. Here, we present a scheme for automated detection of fluorescent cells within thin resin sections, which could then be used to drive automated electron image acquisition from target regions via ‘smart tracking’. The aim of this work is to aid in optimization of the data acquisition process through automation, freeing the operator to work on other tasks and speeding up the process, while reducing data rates by only acquiring images from regions of interest. This new method is shown to be robust against noise and able to deal with regions of low fluorescence.     

Efficient three‐phase reconstruction of heterogeneous material from 2D cross‐sections via phase‐recovery algorithm

Digital reconstruction of a complex heterogeneous media from the limited statistical information, mostly provided by different imaging techniques, is the key to the successful computational analysis of this important class of materials. In this study, a novel approach is presented for three‐dimensional (3D) reconstruction of a three‐phase microstructure from its statistical information provided by two‐dimensional (2D) cross‐sections. In this three‐step method, first two‐point correlation functions (TPCFs) are extracted from the cross‐section(s) using a spectral method suitable for the three‐phase media. In the next step, 3D TPCFs are approximated for all vectors in a representative volume element (RVE). Finally, the 3D microstructure is realized from the full‐set TPCFs obtained in the previous step, using a modified phase‐recovery algorithm. The method is generally applicable to any complex three‐phase media, here illustrated for an SOFC anode microstructure. The capabilities and shortcomings of the method are then investigated by performing a qualitative comparison between example cross‐sections obtained computationally and their experimental equivalents. Finally, it is shown that the method almost conserves key microstructural properties of the media including tortuosity, percolation and three‐phase boundary length (TPBL).     

Structure similarity‐guided image binarization for automatic segmentation of epidermis surface microstructure images

Partitioning epidermis surface microstructure (ESM) images into skin ridge and skin furrow regions is an important preprocessing step before quantitative analyses on ESM images. Binarization segmentation is a potential technique for partitioning ESM images because of its computational simplicity and ease of implementation. However, even for some state‐of‐the‐art binarization methods, it remains a challenge to automatically segment ESM images, because the grey‐level histograms of ESM images have no obvious external features to guide automatic assessment of appropriate thresholds. Inspired by human visual perceptual functions of structural feature extraction and comparison, we propose a structure similarity‐guided image binarization method. The proposed method seeks for the binary image that best approximates the input ESM image in terms of structural features. The proposed method is validated by comparing it with two recently developed automatic binarization techniques as well as a manual binarization method on 20 synthetic noisy images and 30 ESM images. The experimental results show: (1) the proposed method possesses self‐adaption ability to cope with different images with same grey‐level histogram; (2) compared to two automatic binarization techniques, the proposed method significantly improves average accuracy in segmenting ESM images with an acceptable decrease in computational efficiency; (3) and the proposed method is applicable for segmenting practical EMS images. (Matlab code of the proposed method can be obtained by contacting with the corresponding author.)     

Correcting the axial shrinkage of skeletal muscle thick sections visualized by confocal microscopy

Confocal microscopy is a suitable method for measurements and visualization of skeletal muscle fibres and the neighbouring capillaries. When using 3D images of thick sections the tissue deformation effects should be avoided. We studied the deformation in thick sections of the rat skeletal muscle from complete stacks of images captured with confocal microscope. We measured the apparent thickness of the stacks and compared it to the slice thickness deduced from calibrated microtome settings. The ratio of both values yielded the axial scaling factor for every image stack. Careful sample preparation and treatment of the tissue cryosections with cold Ringer solution minimize the tissue deformation. We conclude that rescaling by the inverse of the axial scaling factor of the stack of optical slices in the direction of the microscope optical axis satisfactorily corrects the axial deformation of skeletal muscle samples.     

Performance of new signal‐to‐noise ratio estimation for SEM images based on single image noise cross‐correlation

A new technique for estimation of signal‐to‐noise ratio in scanning electron microscope images is reported. The method is based on the image noise cross‐correlation estimation model recently developed. We derive the basic performance limits on a single image signal‐to‐noise ratio estimation using the Cramer–Rao inequality. The results are compared with those from existing estimation methods including the nearest neighbourhood (the simple method), the first order linear interpolator, and the autoregressive based estimator. The comparisons were made using several tests involving different images within the performance bounds. From the results obtained, the efficiency and accuracy of image noise cross‐correlation estimation technique is considerably better than the other three methods.     

结合粒子群优化和综合评价的脉冲耦合神经网络图像自动分割

为了解决脉冲耦合神经网络(Pulse Coupled Neural Network,PCNN)在图像分割中多参数设定以及评价准则单一的问题,提出了一种结合粒子群优化算法(Particle Swarm Optimization,PSO)和综合评价准则的PCNN图像自动分割方法。采用单调递增阈值搜索策略的PCNN改进模型,将PSO优化原理与由交叉熵参数,边缘匹配度和噪点控制度共同构成的综合评价相结合,以综合评价作为粒子的适应度函数,自动寻优获取PCNN图像分割模型的目标时间常数,连接系数以及迭代次数n,从而实现全参数自适应的PCNN图像分割。实验结果表明算法在保证PCNN运行效率下对不同类型图像都能进行正确完整的分割并兼顾纹理细节的保留。从实验数据可以看到,本文算法在综合评价和通用综合指标上均优于其他对比算法,综合评价平均优于其他算法10.5%。客观评价结果与视觉主观评价相一致,分割较理想,算法具有较高的鲁棒性。     

Empirical gradient threshold technique for automated segmentation across image modalities and cell lines

New microscopy technologies are enabling image acquisition of terabyte‐sized data sets consisting of hundreds of thousands of images. In order to retrieve and analyze the biological information in these large data sets, segmentation is needed to detect the regions containing cells or cell colonies. Our work with hundreds of large images (each 21 000×21 000 pixels) requires a segmentation method that: (1) yields high segmentation accuracy, (2) is applicable to multiple cell lines with various densities of cells and cell colonies, and several imaging modalities, (3) can process large data sets in a timely manner, (4) has a low memory footprint and (5) has a small number of user‐set parameters that do not require adjustment during the segmentation of large image sets. None of the currently available segmentation methods meet all these requirements. Segmentation based on image gradient thresholding is fast and has a low memory footprint. However, existing techniques that automate the selection of the gradient image threshold do not work across image modalities, multiple cell lines, and a wide range of foreground/background densities (requirement 2) and all failed the requirement for robust parameters that do not require re‐adjustment with time (requirement 5). We present a novel and empirically derived image gradient threshold selection method for separating foreground and background pixels in an image that meets all the requirements listed above. We quantify the difference between our approach and existing ones in terms of accuracy, execution speed, memory usage and number of adjustable parameters on a reference data set. This reference data set consists of 501 validation images with manually determined segmentations and image sizes ranging from 0.36 Megapixels to 850 Megapixels. It includes four different cell lines and two image modalities: phase contrast and fluorescent. Our new technique, called Empirical Gradient Threshold (EGT), is derived from this reference data set with a 10‐fold cross‐validation method. EGT segments cells or colonies with resulting Dice accuracy index measurements above 0.92 for all cross‐validation data sets. EGT results has also been visually verified on a much larger data set that includes bright field and Differential Interference Contrast (DIC) images, 16 cell lines and 61 time‐sequence data sets, for a total of 17 479 images. This method is implemented as an open‐source plugin to ImageJ as well as a standalone executable that can be downloaded from the following link: https://isg.nist.gov/ .     

结合先验稀疏字典和空洞填充的CT图像肝脏分割

针对复杂多变的肝脏图像,提出了一种基于先验稀疏字典和空洞填充的三维肝脏图像分割方法。对腹部CT图像进行Gabor特征提取,并分别在Gabor图像和灰度图像的肝脏金标准边界上选择大小相同的图像块作为两组训练集,利用训练集得到两种查询字典及稀疏编码。将金标准图像与待分割图像配准,并将配准后的肝脏边界作为待分割图像的肝脏初始边界;在初始边界点上的十邻域内选择大小相同的两组图像块作为测试样本,利用测试样本与查询字典计算稀疏编码及重构误差,并选择重构误差最小的图像块的中心作为待分割肝脏的边界点;最后,设计一种空洞填充方法对肝脏边界进行补全和平滑处理,得到最终分割结果。利用医学图像计算和计算机辅助介入国际会议中提供的肝脏数据进行了实验验证。结果表明,该方法对肝脏分割图像具有较好的适用性和鲁棒性,并获得了较高的分割精度。其中,平均体积重叠率误差为(5.21±0.45)%,平均相对体积误差为(0.72±0.12)%,平均对称表面距离误差为(0.93±0.14)mm。