Colocalization analysis yields superior results after image restoration

Colocalization analysis is a powerful tool for the demonstration of spatial and temporal overlap in the distribution patterns of fluorescent probes. In unprocessed images, background affects image quality by impairing resolution and obscuring image detail in the low-intensity range. Because confocal images suffer from background levels up to 30% maximum intensity, colocalization analysis, which is a typical segmentation process, is limited to high-intensity signal. In addition, noise-induced, false-positive events ("dust") may skew the results. Therefore, suppression of background is crucial for this type of image analysis. Analysis of synthetic and biological objects demonstrates that median filtering is able to eliminate noise-induced colocalization events successfully. Its disadvantages include the occasional generation of false-positive and false-negative results as well as the inherent impairment of resolution. In contrast, image restoration by deconvolution suppresses background to very low levels (<10% maximum intensity), which makes additional objects in the low-intensity but high-frequency range available for analysis. The improved resolution makes this technique extremely suitable for examination of objects of near resolution size as demonstrated by correlation coefficients. Deconvolution is, however, sensitive to overestimation of the background level. Conclusions for practical application are: (1) In raw images, colocalization analysis is limited to the intensity range above the background level. This means the higher the RS/N the better. Unfortunately, images of most biological specimens have a low RS/N. (2) Filtering improves the result substantially. The reduction of background levels and the concomitant increase of the RS/N are generated at the expense of resolution. This is a quick and simple method in cases where resolution is not a major concern. (3) If colocalization in the low-intensity range and/or maximum resolution play a role, deconvolution should be used.     

基于驰振的压电能量采集器建模与实验研究

针对低信噪比环境下核电站松动件的检测，以降低误报率、漏报率为目标，提出了一种基于盲解卷积算法的松动部件冲击响应提取方法，并进一步结合支持向量机分类辨识算法，给出了一种低信噪比环境下核电站松动部件检测方法。利用叠加实堆背景噪声的平板钢球跌落实验数据开展了报警研究，并对盲解卷积算法进行了参数优化设计。结果表明：优化后的盲解卷积算法能够很好地恢复出信噪比低至-20dB的冲击响应信号，并使噪声能量降低了75%，有效抑制了噪声；给出的松动部件检测方法在信噪比低至-14dB时，仍具有极低的漏报率，并且噪声误报率和脉冲干扰误报率为零，因而具有良好的抗误报、抗漏报能力。     

基于驰振的压电能量采集器建模与实验研究

在压电能量采集器分布参数模型的基础上，对驰振作用下悬臂式压电能量采集器的振动和能量采集情况作进一步理论分析，得到质量块起振风速以及从起振到驰振过程中所采集到功率的解析解。利用直流式闭口低速风洞分别对正三棱柱、正四棱柱悬臂式压电能量采集器进行实验测试，结果表明，正四棱柱能量采集器和正三棱柱能量采集器的采集功率分别达到0.248 5 mW和0.125 9 mW。通过对采集功率和电压时程曲线进行对比，其理论解和实验值吻合程度较高，理论模型具有较高精度。通过理论模型分析发现：风速越大，质量块质量越小，能量采集器采集功率越高；对于不同条件下的能量采集器均存在最优外载阻值，低风速下还会出现双最优外载阻值。     

Background intensity correction for terabyte‐sized time‐lapse images

Several computational challenges associated with large‐scale background image correction of terabyte‐sized fluorescent images are discussed and analysed in this paper. Dark current, flat‐field and background correction models are applied over a mosaic of hundreds of spatially overlapping fields of view (FOVs) taken over the course of several days, during which the background diminishes as cell colonies grow. The motivation of our work comes from the need to quantify the dynamics of OCT‐4 gene expression via a fluorescent reporter in human stem cell colonies. Our approach to background correction is formulated as an optimization problem over two image partitioning schemes and four analytical correction models. The optimization objective function is evaluated in terms of (1) the minimum root mean square (RMS) error remaining after image correction, (2) the maximum signal‐to‐noise ratio (SNR) reached after downsampling and (3) the minimum execution time. Based on the analyses with measured dark current noise and flat‐field images, the most optimal GFP background correction is obtained by using a data partition based on forming a set of submosaic images with a polynomial surface background model. The resulting image after correction is characterized by an RMS of about 8, and an SNR value of a 4 × 4 downsampling above 5 by Rose criterion. The new technique generates an image with half RMS value and double SNR value when compared to an approach that assumes constant background throughout the mosaic. We show that the background noise in terabyte‐sized fluorescent image mosaics can be corrected computationally with the optimized triplet (data partition, model, SNR driven downsampling) such that the total RMS value from background noise does not exceed the magnitude of the measured dark current noise. In this case, the dark current noise serves as a benchmark for the lowest noise level that an imaging system can achieve. In comparison to previous work, the past fluorescent image background correction methods have been designed for single FOV and have not been applied to terabyte‐sized images with large mosaic FOVs, low SNR and diminishing access to background information over time as cell colonies span entirely multiple FOVs. The code is available as open‐source from the following link https://isg.nist.gov/ .     

改善激光共焦扫描显微镜像质的方法

为解决传统光学显微镜样本上每一点的图像都受到邻近点衍射或散射光干扰的问题,研发了一套基于C#WinForm控制平台进行连续扫描方式的激光共焦扫描显微镜(LCSM)系统,并且成功地对生物细胞进行了扫描成像。针对共焦显微镜图像像质不高的问题,提出合理选取探测器针孔直径,并通过高斯低通滤波、盲解卷积的方法,确保实现高像质。实验结果表明,基于上述方法改进后的LCSM具有较高图像质量,该方法简单易行,便于实施。     

Strain mapping accuracy improvement using super‐resolution techniques

Super‐resolution (SR) software‐based techniques aim at generating a final image by combining several noisy frames with lower resolution from the same scene. A comparative study on high‐resolution high‐angle annular dark field images of InAs/GaAs QDs has been carried out in order to evaluate the performance of the SR technique. The obtained SR images present enhanced resolution and higher signal‐to‐noise (SNR) ratio and sharpness regarding the experimental images. In addition, SR is also applied in the field of strain analysis using digital image processing applications such as geometrical phase analysis and peak pairs analysis. The precision of the strain mappings can be improved when SR methodologies are applied to experimental images.     

Application of an advanced maximum likelihood estimation restoration method for enhanced‐resolution and contrast in second‐harmonic generation microscopy

Second‐harmonic generation (SHG) microscopy has gained popularity because of its ability to perform submicron, label‐free imaging of noncentrosymmetric biological structures, such as fibrillar collagen in the extracellular matrix environment of various organs with high contrast and specificity. Because SHG is a two‐photon coherent scattering process, it is difficult to define a point spread function (PSF) for this modality. Hence, compared to incoherent two‐photon processes like two‐photon fluorescence, it is challenging to apply the various PSF‐engineering methods to improve the spatial resolution to be close to the diffraction limit. Using a synthetic PSF and application of an advanced maximum likelihood estimation (AdvMLE) deconvolution algorithm, we demonstrate restoration of the spatial resolution in SHG images to that closer to the theoretical diffraction limit. The AdvMLE algorithm adaptively and iteratively develops a PSF for the supplied image and succeeds in improving the signal to noise ratio (SNR) for images where the SHG signals are derived from various sources such as collagen in tendon and myosin in heart sarcomere. Approximately 3.5 times improvement in SNR is observed for tissue images at depths of up to ～480 nm, which helps in revealing the underlying helical structures in collagen fibres with an ～26% improvement in the amplitude contrast in a fibre pitch. Our approach could be adapted to noisy and low resolution modalities such as micro‐nano CT and MRI, impacting precision of diagnosis and treatment of human diseases.     

A software tool for tomographic axial superresolution in STED microscopy

A method for generating three‐dimensional tomograms from multiple three‐dimensional axial projections in STimulated Emission Depletion (STED) superresolution microscopy is introduced. Our STED< method, based on the use of a micromirror placed on top of a standard microscopic sample, is used to record a three‐dimensional projection at an oblique angle in relation to the main optical axis. Combining the STED< projection with the regular STED image into a single view by tomographic reconstruction, is shown to result in a tomogram with three‐to‐four‐fold improved apparent axial resolution. Registration of the different projections is based on the use of a mutual‐information histogram similarity metric. Fusion of the projections into a single view is based on Richardson‐Lucy iterative deconvolution algorithm, modified to work with multiple projections. Our tomographic reconstruction method is demonstrated to work with real biological STED superresolution images, including a data set with a limited signal‐to‐noise ratio (SNR); the reconstruction software (SuperTomo) and its source code will be released under BSD open‐source license.     

Multiphoton fluorescent images with a spatially varying background signal: a ML deconvolution method

By means of multiphoton laser scanning microscopy, neuroscientists can look inside the brain deeper than has ever been possible before. Multiphoton fluorescent images, as all optical images, suffer from degradation caused by a variety of sources (e.g. light dispersion and absorption in the tissue, laser fluctuations, spurious photodetection and staining deficiency). From a modelling perspective, such degradations can be considered the sum of stochastic noise and a background signal. Among the methods proposed in the literature to perform image deconvolution in either confocal or multiphoton fluorescent microscopy, Vicidomini et al. (2009) were the first to incorporate models for noise (a Poisson process) and background signal (spatially constant) in the context of regularized inverse problems. Unfortunately, the so-called split-gradient deconvolution method (SGM) they used did not consider possible spatial variations in the background signal. In this paper, we extend the SGM by adding a maximum-likelihood estimation step for the determination of a spatially varying background signal. We demonstrate that the assumption of a constant background is not always valid in multiphoton laser microscopy and by using synthetic and actual multiphoton fluorescent images, we evaluate the face of validity of the proposed method, and compare its accuracy with the previously introduced SGM algorithm.     

Crystallographic image processing approach to crystal structure determination

It is shown that the crystallographic image-processing technique based on the weak-phase object approximation and on the combination of high-resolution electron microscopy and electron diffraction is applicable to crystal structure determination. The technique consists of two stages: image deconvolution and phase extension. In the first stage an image taken at an arbitrary defocus condition can be transformed into the structure image with the resolution limited by the resolution of the electron microscope. In the second stage the image resolution is enhanced to the diffraction resolution limit so that most unoverlapped atoms can be resolved individually in the final image. Although the experimental diffraction intensities are available for the image deconvolution, they must be corrected for the phase extension. The proposed empirical method of electron diffraction intensity correction seems effective for obtaining a set of corrected diffraction intensities which are approximately equal to square structure factors.
When the crystal structure under examination belongs to a known typical type, it is easy to propose the structure model by referring to the deconvoluted image which reveals the low-resolution structure, and the high-resolution structure can also be determined by image simulation.     

Measurement of co-localization of objects in dual-colour confocal images

A method to measure the degree of co-localization of objects in confocal dual-colour images has been developed. This image analysis produced two coefficients that represent the fraction of co-localizing objects in each component of a dual-channel image. The generation of test objects with a Gaussian intensity distribution, at well-defined positions in both components of dual-channel images, allowed an accurate investigation of the reliability of the procedure. To do that, the co-localization coefficients were determined before degrading the image with background, cross-talk and Poisson noise. These synthesized sources of image deterioration represent sources of deterioration that must be dealt with in practical confocal imaging, namely dark current, non-specific binding and cross-reactivity of fluorescent probes, optical cross-talk and photon noise. The degraded images were restored by filtering and cross-talk correction. The co-localization coefficients of the restored images were not significantly different from those of the original undegraded images. Finally, we tested the procedure on images of real biological specimens. The results of these tests correspond with data found in the literature. We conclude that the co-localization coefficients can provide relevant quantitative information about the positional relation between biological objects or processes.     

Shack-Hartmann wave front measurements in cortical tissue for deconvolution of large three-dimensional mosaic transmitted light brightfield micrographs

We present a novel approach for deconvolution of 3D image stacks of cortical tissue taken by mosaic/optical‐sectioning technology, using a transmitted light brightfield microscope. Mosaic/optical‐sectioning offers the possibility of imaging large volumes (e.g. from cortical sections) on a millimetre scale at sub‐micrometre resolution. However, a blurred contribution from out‐of‐focus light results in an image quality that usually prohibits 3D quantitative analysis. Such quantitative analysis is only possible after deblurring by deconvolution. The resulting image quality is strongly dependent on how accurate the point spread function used for deconvolution resembles the properties of the imaging system. Since direct measurement of the true point spread function is laborious and modelled point spread functions usually deviate from measured ones, we present a method of optimizing the microscope until it meets almost ideal imaging conditions. These conditions are validated by measuring the aberration function of the microscope and tissue using a Shack‐Hartmann sensor. The analysis shows that cortical tissue from rat brains embedded in Mowiol and imaged by an oil‐immersion objective can be regarded as having a homogeneous index of refraction. In addition, the amount of spherical aberration that is caused by the optics or the specimen is relatively low. Consequently the image formation is simplified to refraction between the embedding and immersion medium and to 3D diffraction at the finite entrance pupil of the objective. The resulting model point spread function is applied to the image stacks by linear or iterative deconvolution algorithms. For the presented dataset of large 3D images the linear approach proves to be superior. The linear deconvolution yields a significant improvement in signal‐to‐noise ratio and resolution. This novel approach allows a quantitative analysis of the cortical image stacks such as the reconstruction of biocytin‐stained neuronal dendrites and axons.     

High‐resolution wide‐field microscopy with adaptive optics for spherical aberration correction and motionless focusing

Live imaging in cell biology requires three‐dimensional data acquisition with the best resolution and signal‐to‐noise ratio possible. Depth aberrations are a major source of image degradation in three‐dimensional microscopy, causing a significant loss of resolution and intensity deep into the sample. These aberrations occur because of the mismatch between the sample refractive index and the immersion medium index. We have built a wide‐field fluorescence microscope that incorporates a large‐throw deformable mirror to simultaneously focus and correct for depth aberration in three‐dimensional imaging. Imaging fluorescent beads in water and glycerol with an oil immersion lens we demonstrate a corrected point spread function and a 2‐fold improvement in signal intensity. We apply this new microscope to imaging biological samples, and show sharper images and improved deconvolution.     

基于L-曲率流滤波器的图像降噪算法

提出了L-曲率流滤波器的图像降噪(滤波)算法,该方法按图像信噪比大小分高、中、低3类,分别由L滤波器降噪、多级L滤波器降噪以及多次迭代的组合滤波器降噪,并进行了实验研究。结果表明:该算法与均值和中值滤波器相比,输入图像信噪比越低,滤波效果越明显。当输入图像为低信噪比时,对于受高斯噪声污染的图像,该算法滤波比均值滤波平均提高2.98 dB;对于受脉冲噪声污染的图像,该算法滤波比中值滤波平均提高11.09 dB,说明该算法对降低不同种类和不同信噪比的图像噪声有较强的适应性。     

面向微纳材料的激光扫描二维成像系统

在对微纳材料光学特性表征中,需要获得分辨率更高的波长和强度的荧光图像。普通的显微镜无法满足测试的要求,因此将普通的成像显微镜、光谱仪以及纳米移动台组成激光扫描显微镜成像系统,并利用LabVIEW开发了一套完整的集二维扫描采集与信号图像处理一体的系统上位机软件。扫描采集过程使用了低通滤波等数字信号处理方法消除光谱仪信号噪声的影响。利用本系统测量硒化镉纳米带、单层二硫化钼得到了荧光强度图像以及荧光峰值波长图像,能分辨出最小波长为0.03 nm的荧光。将采集长度与实际长度进行比较并分析荧光强度差异,取得了较好的效果。     

Effect of shot noise and secondary emission noise in scanning electron microscope images

The effect of shot noise and emission noise due to materials that have different emission properties was simulated. Local variations in emission properties affect the overall signal‐to‐noise ratio (SNR) value of the scanning electron microscope image. In the case in which emission noise is assumed to be absent, the image SNRs for silicon and gold on a black background are identical. This is because only shot noise in the primary beam affects the SNRs, irrespective of the assumed noiseless secondary electron emission or backscattered electron emission processes. The addition of secondary emission noise degrades the SNR. Materials with higher secondary electron yield and backscattering electron yield give rise to higher SNR. For images formed from two types of material, the contrast of the image is lower. The reduction in image signal reduces the overall image SNR. As expected, large differences in δ or η give rise to higher SNR images.     

一种改进自适应阈值的 Ｃａｎｎｙ算法

针对传统Canny算法在滤波时无差别平滑降噪、边缘信息丢失及模糊的现象,且人工选取梯度高低阈值存在局限性的问题,提出了综合考虑图像位置信息与亮度信息的双边滤波代替传统高斯滤波方法,结合最优化阈值分割法和Otsu法分别计算出图像高低阈值,采用边缘信噪比SNR函数对比验证边缘检测效果。改进后的Canny算法有效提高了边缘完整性,且伪边缘更少,对图像中相对较弱的边缘部分也有良好的提取效果,改进后的算法具有更高的信噪比。采用工业机械臂视觉采集零件边缘为背景,实验结果表明,改进后的Canny算法可以有效保留真实边缘信息,边缘更加连续且完整。     

基于MAP估计的复小波域局部自适应绝缘子红外热像去噪方法

为了从强白噪声干扰的红外热像中提取真实的绝缘子盘面温度场信息,提出一种基于MAP估计的复小波域局部自适应去噪方法.首次证实了绝缘子红外热像双树复小波变换(DT-CWT)系数服从拉普拉斯分布,并对不同滤波器组采用各自最精细分解层子带系数估计噪声方差,利用待估计点圆形邻域系数估计信号方差,且随分辨率变化调整圆形邻域半径,使得MAP估计的无噪声系数更为准确,提高了去噪图像质量.实验结果表明,该方法比传统的Wiener滤波法、基于离散小波变换和DT-CWT的贝叶斯阈值去噪方法具有更高的信噪比,在有效去除图像噪声的同时,图像细节信息保留更完好.     

Noise-induced systematic errors in ratio imaging: serious artefacts and correction with multi-resolution denoising

Ratio imaging is playing an increasingly important role in modern cell biology. Combined with ratiometric dyes or fluorescence resonance energy transfer (FRET) biosensors, the approach allows the detection of conformational changes and molecular interactions in living cells. However, the approach is conducted increasingly under limited signal‐to‐noise ratio (SNR), where noise from multiple images can easily accumulate and lead to substantial uncertainty in ratio values. This study demonstrates that a far more serious concern is systematic errors that generate artificially high ratio values at low SNR. Thus, uneven SNR alone may lead to significant variations in ratios among different regions of a cell. Although correct average ratios may be obtained by applying conventional noise reduction filters, such as a Gaussian filter before calculating the ratio, these filters have a limited performance at low SNR and are prone to artefacts such as generating discrete domains not found in the correct ratio image. Much more reliable restoration may be achieved with multi‐resolution denoising filters that take into account the actual noise characteristics of the detector. These filters are also capable of restoring structural details and photometric accuracy, and may serve as a general tool for retrieving reliable information from low‐light live cell images.     

Nano‐level position resolution for particle tracking in digital in‐line holographic microscopy

Three‐dimensional particle tracking in biological systems is a quickly growing field, many techniques have been developed providing tracking characters. Digital in‐line holographic microscopy is a valuable technique for particle tracking. However, the speckle noise, out‐of‐focus signals and twin image influenced the particle tracking. Here an adaptive noise reduction method based on bidimensional ensemble empirical mode decomposition is introduced into digital in‐line holographic microscopy. It can eliminate the speckle noise and background of the hologram adaptively. Combined with the three‐dimensional deconvolution approach in the reconstruction, the particle feature would be identified effectively. Tracking the fixed beads on the cover‐glass with piezoelectric stage through multiple holographic images demonstrate the tracking resolution, which approaches 2 nm in axial direction and 1 nm in transverse direction. This would facilitate the development and use in the biological area such as living cells and single‐molecule approaches.