一种改进型灰度差分聚焦算法研究与实现

为解决显微图像采集中聚焦的准确性和快速性,设计并实现了一种包括图像采集端、图像处理算法实现模块、OSD菜单实现模块、系统控制模块和HDMI输出模块等硬件采集系统。结合实际应用和现有的图像处理算法,提出了适合数字显微图像的图像预处理自动聚焦算法,在灰度差分绝对值之和函数的基础上增加了对周围点的判断,提高了算法的精确度。实验结果表明,通过对切片图像在显微镜物镜10倍、40倍、100倍下采集到的离、聚焦图像以及聚焦曲线呈现单峰性,无偏性等特点,能实现良好的聚焦能力。     

Time-lapse scanning electron microscopy for measurement of contamination rate and stage drift

Measurements done with scanning electron microscopes (SEMs) may lose their validity due to contamination and charging caused by the primary electron beam. The sample stage and the electron beam also slightly drift during the course of the measurements. Consequently, it is essential to find out the time limit of valid measurements, that is, the maximum time before the sample or its position changes too much. This paper describes digital time-lapse SEM, a useful tool for investigating the extent and effects of contamination and stage drift. It works with hardware and software that create a stack of sequential images. Later these images can be viewed as a short movie; it is also possible to apply all the image processing and analysis procedures that are otherwise applied to separate, individual images. This method gives a reliable way of measuring the rate of contaminant deposition and of stage and electron beam drift of SEMs, and it could be helpful in controlling these problems.     

基于投影图像特征匹配的微纳CT焦点漂移校正

微纳CT射线源焦点热漂移是影响图像清晰度的重要因素之一。通过理论和仿真实验分析了射线源焦点漂移对图像清晰度的影响。利用实际微纳CT系统,实验发现焦点漂移主要是缓慢热漂移,漂移量与X射线源功率正相关,且焦点漂移具有一定随机性。据此,提出一种基于投影图像特征匹配的焦点漂移校正方法。首先,在实际CT扫描后快速采集少量参考投影,根据实际CT投影和参考投影自适应特征匹配结果求取特定角度下的焦点漂移量;其次,采用样条插值获取CT扫描过程所有的焦点漂移量;最后,根据焦点漂移量修正实际投影数据,重建得到校正后的图像。实验表明,该方法定位精度高,可大幅度减少图像畸变,图像清晰度提高近10%。     

Halton sampling for autofocus

Reliable autofocusing is a critical part of any automated microscopy system: by precisely positioning the sample in the focal plane, the acquired images are sharp and can be accurately segmented and quantified. The three main components of an autofocus algorithm are a contrast function, an optimization algorithm and a sampling strategy. The latter has not been given much attention in the literature. It is however a very important part of the autofocusing algorithm, especially in high content and high throughput image-based screening. It deals with the problem of sampling the focus surface as sparsely as possible to reduce bleaching and computation time while with sufficient detail as to permit a faithful interpolation. We propose a new strategy that has higher performance compared to the classical square grid or the hexagonal lattice, which is based on the concept of low discrepancy point sets and in particular on the Halton point set. We tested the new algorithm on nine different focus surfaces, each under 24 different combinations of Signal-to-Noise ratio (SNR) and sampling rate, obtaining that in 88% of the tested conditions, Halton sampling outperforms its counterparts.     

Optimization of EFTEM image acquisition by using elastically filtered images for drift correction

Because of its high spatial resolution, energy-filtering transmission electron microscopy (EFTEM) has become widely used for the analysis of the chemical composition of nanostructures. To obtain the best spatial resolution, the precise correction of instrumental influences and the optimization of the data acquisition procedure are very important. In this publication, we discuss a modified image acquisition procedure that optimizes the acquisition process of the EFTEM images, especially for long exposure times and measurements that are affected by large spatial drift. To alleviate the blurring of the image caused by the spatial drift, we propose to take several EFTEM images with a shorter exposure time (sub-images) and merge these sub-images afterwards. To correct for the drift between these sub-images, elastically filtered images are acquired between two subsequent sub-images. These elastically filtered images are highly suitable for spatial drift correction based on the cross-correlation method. The use of the drift information between two elastically filtered images permits to merge the drift-corrected sub-images automatically and with high accuracy, resulting in sharper edges and an improved signal intensity in the final EFTEM image. Artefacts that are caused by prominent noise-peaks in the dark reference image have been suppressed by calculating the dark reference image from three images. Furthermore, using the information given by the elastically filtered images, it is possible to drift-correct a set of EFTEM images already during the acquisition. This simplifies the post-processing for elemental mapping and offers the possibility for active drift correction using the image shift function of the microscope, leading to an increased field of view.     

基于动态因子自适应搜索算法的自动调焦研究

为提高自动调焦的性能,采取动态因子自适应搜索算法。首先,建立分辨率逐步递减的调焦窗口,以减少非聚焦区域对调焦评价函数计算的影响;其次,通过动态调焦因子方法修正目标变化对清晰度评价函数的影响;最后,通过适应变步长爬山算法结合清晰度评价函数搜索聚焦点,提高搜索速度和调焦精度。试验仿真显示,本文提出的算法获得的图像清晰,其性能受噪声影响小,可满足自动调焦中对稳定性、实时性和清晰度的要求。     

Fast,high-precision autofocus on a motorised microscope: Automating blood sample imaging on the OpenFlexure Microscope

The OpenFlexure Microscope is a 3D-printed, low-cost microscope capable of automated image acquisition through the use of a motorised translation stage and a Raspberry Pi imaging system. This automation has applications in research and healthcare, including in supporting the diagnosis of malaria in low-resource settings. The plasmodium parasites that cause malaria require high magnification imaging, which has a shallow depth of field, necessitating the development of an accurate and precise autofocus procedure. We present methods of identifying the focal plane of the microscope, and procedures for reliably acquiring a stack of focused images on a system affected by backlash and drift. We also present and assess a method to verify the success of autofocus during the scan. The speed, reliability and precision of each method are evaluated, and the limitations discussed in terms of the end users' requirements.     

Correction of image drift and distortion in a scanning electron microscopy

Continuous research on small‐scale mechanical structures and systems has attracted strong demand for ultrafine deformation and strain measurements. Conventional optical microscope cannot meet such requirements owing to its lower spatial resolution. Therefore, high‐resolution scanning electron microscope has become the preferred system for high spatial resolution imaging and measurements. However, scanning electron microscope usually is contaminated by distortion and drift aberrations which cause serious errors to precise imaging and measurements of tiny structures. This paper develops a new method to correct drift and distortion aberrations of scanning electron microscope images, and evaluates the effect of correction by comparing corrected images with scanning electron microscope image of a standard sample. The drift correction is based on the interpolation scheme, where a series of images are captured at one location of the sample and perform image correlation between the first image and the consequent images to interpolate the drift–time relationship of scanning electron microscope images. The distortion correction employs the axial symmetry model of charged particle imaging theory to two images sharing with the same location of one object under different imaging fields of view. The difference apart from rigid displacement between the mentioned two images will give distortion parameters. Three‐order precision is considered in the model and experiment shows that one pixel maximum correction is obtained for the employed high‐resolution electron microscopic system.     

Autofocusing in computer microscopy: selecting the optimal focus algorithm

Autofocusing is a fundamental technology for automated biological and biomedical analyses and is indispensable for routine use of microscopes on a large scale. This article presents a comprehensive comparison study of 18 focus algorithms in which a total of 139,000 microscope images were analyzed. Six samples were used with three observation methods (brightfield, phase contrast, and differential interference contrast (DIC)) under two magnifications (100x and 400x). A ranking methodology is proposed, based on which the 18 focus algorithms are ranked. Image preprocessing was also conducted to extensively reveal the performance and robustness of the focus algorithms. The presented guidelines allow for the selection of the optimal focus algorithm for different microscopy applications.     

Multidimensional long-term time-lapse microscopy of in vitro peripheral nerve regeneration

In order to test the effectiveness of a new advanced time-lapse microscopy imaging and image processing and analysis system, and to do quantitative and qualitative temporal analyses of in vitro peripheral nerve regeneration, long-term time-lapse imaging of cultures of mouse dorsal root ganglia (DRGs) was performed. DRGs were placed in a Petri dish, covered with collagen gel, their attached peripheral nerves were cut in the middle, creating a gap, and the dish was filled with culture medium. Six preparations were kept on the time-lapse imaging system, which provides a suitable incubation environment and enables to capture images from multiple coordinates at x,y,z axes at desired time intervals for 13 days. In general, the time-lapse imaging system proved quite stable and efficient, although some improvements are certainly required. Two main components of peripheral nerve regeneration, outgrowth of axons and activities of resident cells, were examined. Axons started to grow during the first hour of incubation with a 16.5 microm/h rate and showed the slowest rates (0.7 microm/h) on days 8 and 9, after which they resumed higher speeds again. The first cell came out of the proximal end of the cut nerve on the second day and it was a Schwann cell (SC), which was the prominent cell type in the preparations throughout the experiment. SCs were higher in number (83.15% of all cells) but slower in migration (3.4 vs. 7.3 microm/h, P < 0.001) than other cells. Other observed characteristics of axonal outgrowth and cellular activity and interactions between axons and the cells are discussed.     

An algorithm selection methodology for automated focusing in optical microscopy

Autofocus systems are essential in optical microscopy. These systems typically sweep the sample through the focal range and apply an algorithm to determine the contrast value of each image, where the highest value indicates the optimal focus position. As the optimal algorithm may vary according to the images' content, we evaluate the 15 most used algorithms in the field using 150 stacks of images from four different kinds of tissue. We use four measuring criteria and two types of analysis and propose a general methodology to apply to select the best fitting algorithm for any given application. In this paper, we present the results of this evaluation and a detailed discussion of different features: the threshold used for the algorithms, the criteria parameters, the analysis used, the bit depth of the images, their magnification, and the type of tissue, reaching the conclusion that some of these parameters are more relevant to the study than others, and the implementation of the proposed methodology can lead to a fast and reliable autofocus system capable of performing an analysis and selection of algorithms with no supervision required.     

Accurate shape from focus based on focus adjustment in optical microscopy

Optical microscopy allows a magnified view of the sample while decreasing the depth of focus. Although the acquired images from limited depth of field have both blurred and focused regions, they can provide depth information. The technique to estimate the depth and 3D shape of an object from the images of the same sample obtained at different focus settings is called shape from focus (SFF). In SFF, the measure of focus–sharpness–is the crucial part for final 3D shape estimation. The conventional methods compute sharpness by applying focus measure operator on each 2D image frame of the image sequence. However, such methods do not reflect the accurate focus levels in an image because the focus levels for curved objects require information from neighboring pixels in the adjacent frames too. To address this issue, we propose a new method based on focus adjustment which takes the values of the neighboring pixels from the adjacent image frames that have approximately the same initial depth as of the center pixel and then it re-adjusts the center value accordingly. Experiments were conducted on synthetic and microscopic objects, and the results show that the proposed technique generates better shape and takes less computation time in comparison with previous SFF methods based on focused image surface (FIS) and dynamic programming. Microsc. Res. Tech., 2009. © 2008 Wiley-Liss, Inc.     

Information content analysis in automated microscopy imaging using an adaptive autofocus algorithm for multimodal functions

We present a new algorithm to analyse information content in images acquired using automated fluorescence microscopy. The algorithm belongs to the group of autofocusing methods, but differs from its predecessors in that it can handle thick specimens and operate also in confocal mode. It measures the information content in images using a ‘content function’, which is essentially the same concept as a focus function. Unlike previously presented algorithms, this algorithm tries to find all significant axial positions in cases where the content function applied to real data is not unimodal, which is often the case. This requirement precludes using algorithms that rely on unimodality. Moreover, choosing a content function requires careful consideration, because some functions suppress local maxima. First, we test 19 content functions and evaluate their ability to show local maxima clearly. The results show that only six content functions succeed. To save time, the acquisition procedure needs to vary the step size adaptively, because a wide range of possible axial positions has to be passed so as not to miss a local maximum. The algorithm therefore has to assess the steepness of the content function online so that it can decide to use a bigger or smaller step size to acquire the next image. Therefore, the algorithm needs to know about typical behaviour of content functions. We show that for normalized variance, one of the most promising content functions, this knowledge can be obtained after normalizing with respect to the theoretical maximum of this function, and using hierarchical clustering. The resulting algorithm is more reliable and efficient than a simple procedure with constant steps.     

Intensity correction of fluorescent confocal laser scanning microscope images by mean-weight filtering

This paper addresses the problem of intensity correction of fluorescent confocal laser scanning microscope images. Confocal laser scanning microscope images are frequently used in medicine for obtaining 3D information about specimen structures by imaging a set of 2D cross sections and performing 3D volume reconstruction afterwards. However, 2D images acquired from fluorescent confocal laser scanning microscope images demonstrate significant intensity heterogeneity, for example, due to photo‐bleaching and fluorescent attenuation in depth. We developed an intensity heterogeneity correction technique that (a) adjusts the intensity heterogeneity of 2D images, (b) preserves fine structural details and (c) enhances image contrast, by performing spatially adaptive mean‐weight filtering. Our solution is obtained by formulating an optimization problem, followed by filter design and automated selection of filtering parameters. The proposed filtering method is experimentally compared with several existing techniques by using four quality metrics: contrast, intensity heterogeneity (entropy) in a low frequency domain, intensity distortion in a high frequency domain and saturation. Based on our experiments and the four quality metrics, the developed mean‐weight filtering outperforms other intensity correction methods by at least a factor of 1.5 when applied to fluorescent confocal laser scanning microscope images.     

A novel autofocusing method using the angle of hilbert space for microscopy

Autofocusing technology is indispensable for routine use of microscopes on a large scale in biological field. The autofocusing method using the angle of Hilbert space is brought forward to measure whether the image is focused or not. The angle of Hillbert space can be used to evaluate accurately the similarity degree of two images. The experiment results show that the autofocusing method can decrease the computational cost and get accuracy for real‐time biological and biomedical images with noise robustness. The focus curves are smooth and possess the unimodality, the monotonicity and the symmetry. Compared with other classic and optimum focus method, the Hilbert method demonstrates its robustness to noise and can improve the focus speed. The experiments showed that the proposed method can increase the overall performance of an autofocus system and has strong applicability in various autofocusing algorithms. Microsc. Res. Tech. 77:289–295, 2014. © 2014 Wiley Periodicals, Inc.     

Dynamic evaluation of autofocusing for automated microscopic analysis of blood smear and pap smear

Autofocusing is a fundamental procedure towards automated microscopic evaluation of blood smear and pap smear samples for clinical diagnosis. This paper presents comparison results of 16 selected focus algorithms based on 8000 static bright‐field images and 1600 dynamic autofocusing trials using 10 blood smear and pap smear samples. Besides static behaviour, dynamic autofocusing performance is introduced for ranking the 16 focus algorithms. The Fibonacci search algorithm is employed for controlling the z‐motor of the microscope to reach the focus position that is determined by focus objective functions. Experimental results demonstrate that the variance algorithm provides the best overall performance. Together with our previously reported findings, it is demonstrated that the variance algorithm or the normalized variance algorithm is the optimal focus algorithm for non‐fluorescence microscopy applications including pap smear and blood smear imaging.     

Out‐of‐focus background subtraction for fast structured illumination super‐resolution microscopy of optically thick samples

We propose a structured illumination microscopy method to combine super resolution and optical sectioning in three‐dimensional (3D) samples that allows the use of two‐dimensional (2D) data processing. Indeed, obtaining super‐resolution images of thick samples is a difficult task if low spatial frequencies are present in the in‐focus section of the sample, as these frequencies have to be distinguished from the out‐of‐focus background. A rigorous treatment would require a 3D reconstruction of the whole sample using a 3D point spread function and a 3D stack of structured illumination data. The number of raw images required, 15 per optical section in this case, limits the rate at which high‐resolution images can be obtained. We show that by a succession of two different treatments of structured illumination data we can estimate the contrast of the illumination pattern and remove the out‐of‐focus content from the raw images. After this cleaning step, we can obtain super‐resolution images of optical sections in thick samples using a two‐beam harmonic illumination pattern and a limited number of raw images. This two‐step processing makes it possible to obtain super resolved optical sections in thick samples as fast as if the sample was two‐dimensional.     

基于小波分析的显微序列图像合成系统

由于显微镜物镜焦深范围有限,故造成显微图像局部模糊,研究人员对图像的理解不完整等问题。根据聚焦评价函数分布,采用小波分析,将一序列局部聚焦图像合成全焦图像。改进的小波融合算法得到的融合图像极大地保留了系列图像聚焦部分的信息,合成全焦图像。由此得出的聚焦合成图像使研究人员可直接测量所需的显微结果。     

基于增强卷积神经网络的电子鼻长期漂移抑制方法

为解决电子鼻传感器阵列中的漂移问题,提出了一种增强卷积神经网络的长期漂移抑制方法.首先,通过结合历史数据的方式进行数据库扩增,起到了数据增强的效果;然后,使用增量补偿模块结合增量学习思维进行网络训练,起到了模型增强的效果;最后,分别使用公开数据集和实测数据集来验证模型的漂移抑制效果.实验结果表明:增强卷积神经网络算法的...     

Automated electron tomography with scanning transmission electron microscopy

We report the successful implementation of a fully automated tomographic data collection system in scanning transmission electron microscopy (STEM) mode. Autotracking is carried out by combining mechanical and electronic corrections for specimen movement. Autofocusing is based on contrast difference of a focus series of a small sample area. The focus gradient that exists in normal images due to specimen tilt is effectively removed by using dynamic focusing. An advantage of STEM tomography with dynamic focusing over TEM tomography is its ability to reconstruct large objects with a potentially higher resolution.