Comparative evaluation of autofocus algorithms for a real-time system for automatic detection of Mycobacterium tuberculosis

Microscopy images must be acquired at the optimal focal plane for the objects of interest in a scene. Although manual focusing is a standard task for a trained observer, automatic systems often fail to properly find the focal plane under different microscope imaging modalities such as bright field microscopy or phase contrast microscopy. This article assesses several autofocus algorithms applied in the study of fluorescence-labeled tuberculosis bacteria. The goal of this work was to find the optimal algorithm in order to build an automatic real-time system for diagnosing sputum smear samples, where both accuracy and computational time are important. We analyzed 13 focusing methods, ranging from well-known algorithms to the most recently proposed functions. We took into consideration criteria that are inherent to the autofocus function, such as accuracy, computational cost, and robustness to noise and to illumination changes. We also analyzed the additional benefit provided by preprocessing techniques based on morphological operators and image projection profiling.     

Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination

Structured illumination microscopy is a method that can increase the spatial resolution of wide-field fluorescence microscopy beyond its classical limit by using spatially structured illumination light. Here we describe how this method can be applied in three dimensions to double the axial as well as the lateral resolution, with true optical sectioning. A grating is used to generate three mutually coherent light beams, which interfere in the specimen to form an illumination pattern that varies both laterally and axially. The spatially structured excitation intensity causes normally unreachable high-resolution information to become encoded into the observed images through spatial frequency mixing. This new information is computationally extracted and used to generate a three-dimensional reconstruction with twice as high resolution, in all three dimensions, as is possible in a conventional wide-field microscope. The method has been demonstrated on both test objects and biological specimens, and has produced the first light microscopy images of the synaptonemal complex in which the lateral elements are clearly resolved.     

γH2AX foci as a measure of DNA damage: a computational approach to automatic analysis

The γH2AX focus assay represents a fast and sensitive approach for the detection of one of the critical types of DNA damage - double-strand breaks (DSB) induced by various cytotoxic agents including ionising radiation. Apart from research applications, the assay has a potential in clinical medicine/pathology, such as assessment of individual radiosensitivity, response to cancer therapies, as well as in biodosimetry. Given that generally there is a direct relationship between numbers of microscopically visualised γH2AX foci and DNA DSB in a cell, the number of foci per nucleus represents the most efficient and informative parameter of the assay. Although computational approaches have been developed for automatic focus counting, the tedious and time consuming manual focus counting still remains the most reliable way due to limitations of computational approaches. We suggest a computational approach and associated software for automatic focus counting that minimises these limitations. Our approach, while using standard image processing algorithms, maximises the automation of identification of nuclei/cells in complex images, offers an efficient way to optimise parameters used in the image analysis and counting procedures, optionally invokes additional procedures to deal with variations in intensity of the signal and background in individual images, and provides automatic batch processing of a series of images. We report results of validation studies that demonstrated correlation of manual focus counting with results obtained using our computational algorithm for mouse jejunum touch prints, mouse tongue sections and human blood lymphocytes as well as radiation dose response of γH2AX focus induction for these biological specimens.     

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

Weakly-scattering objects, such as small colloidal particles and most biological cells, are frequently encountered in microscopy. Indeed, a range of techniques have been developed to better visualize these phase objects; phase contrast and DIC are among the most popular methods for enhancing contrast. However, recording position and shape in the out-of-imaging-plane direction remains challenging. This report introduces a simple experimental method to accurately determine the location and geometry of objects in three dimensions, using digital inline holographic microscopy (DIHM). Broadly speaking, the accessible sample volume is defined by the camera sensor size in the lateral direction, and the illumination coherence in the axial direction. Typical sample volumes range from 200 µm x 200 µm x 200 µm using LED illumination, to 5 mm x 5 mm x 5 mm or larger using laser illumination. This illumination light is configured so that plane waves are incident on the sample. Objects in the sample volume then scatter light, which interferes with the unscattered light to form interference patterns perpendicular to the illumination direction. This image (the hologram) contains the depth information required for three-dimensional reconstruction, and can be captured on a standard imaging device such as a CMOS or CCD camera. The Rayleigh-Sommerfeld back propagation method is employed to numerically refocus microscope images, and a simple imaging heuristic based on the Gouy phase anomaly is used to identify scattering objects within the reconstructed volume. This simple but robust method results in an unambiguous, model-free measurement of the location and shape of objects in microscopic samples.     

In situ microscopy for on-line characterization of cell-populations in bioreactors, including cell-concentration measurements by depth from focus

A new technique is presented which allows the use of a front-end sensor head for in situ and on-line characterization of cell concentration and cell size during fermentation. An epifluorescence microscope is mounted in a port of a bioreactor viewing directly into the agitated broth. Still images from cells are generated using pulsed illumination. They are directly visualized on a monitor and used for automatic image analysis. The cell concentration and morphological information are determined by counting and evaluating the cell images with respect to their depth from focus characteristic. An in situ microscope was successfully tested during yeast fermentations and yielded results which correlated well with results from a hemocytometer. (c) 1995 John Wiley & Sons, Inc.     

Evaluation of image analysis and laser granulometry for microbial cell sizing

A direct cell size measurement technique and an image analysis based sizing method were developed. The former consisted of a manual size measurement of the two-dimensional cell images on a video screen, with automatic data recording. This method was chosen as the reference. The latter, a semiautomatic method took advantage of a commercial computer program designed for image processing and particle morphology analysis. It gave average and median size values which were compatible with the manual method. However, the performance of these time consuming methods is limited. Hence, the laser granulometry technique, intrinsically far more powerful while capable of analysing millions of sample objects in a short time delay, was applied. The comparison revealed that this method gives too low size values, particularly in disagreement with the known dimensions of the bacterial (Zymomonas mobilis) cells. A size correction method was developed to realign the granulometry results ofZ. mobilis cell samples with those of the direct manual measurement method.     

Markov random field based automatic image alignment for electron tomography

We present a method for automatic full-precision alignment of the images in a tomographic tilt series. Full-precision automatic alignment of cryo electron microscopy images has remained a difficult challenge to date, due to the limited electron dose and low image contrast. These facts lead to poor signal to noise ratio (SNR) in the images, which causes automatic feature trackers to generate errors, even with high contrast gold particles as fiducial features. To enable fully automatic alignment for full-precision reconstructions, we frame the problem probabilistically as finding the most likely particle tracks given a set of noisy images, using contextual information to make the solution more robust to the noise in each image. To solve this maximum likelihood problem, we use Markov Random Fields (MRF) to establish the correspondence of features in alignment and robust optimization for projection model estimation. The resulting algorithm, called Robust Alignment and Projection Estimation for Tomographic Reconstruction, or RAPTOR, has not needed any manual intervention for the difficult datasets we have tried, and has provided sub-pixel alignment that is as good as the manual approach by an expert user. We are able to automatically map complete and partial marker trajectories and thus obtain highly accurate image alignment. Our method has been applied to challenging cryo electron tomographic datasets with low SNR from intact bacterial cells, as well as several plastic section and X-ray datasets.     

四种昆虫饲养设备在昆虫学实验室的应用比较

本文比较了智能人工气候箱、智能型光照培养箱、全自动控制养虫室和简易型养虫室等4种常用昆虫饲养设备的优点和不足，分析了不同设备的适用情况。以西北农林科技大学应用昆虫学重点实验室正在应用的昆虫饲养设备为例，详细介绍了几种设备使用中的注意事项及维护方法。智能型人工气候箱和全自动控制养虫室可用于饲养对温度、湿度及光照要求较高的寄主植物及昆虫；智能型光照培养箱适用于对温度和光照有一定要求而对湿度要求不严格的试验；简易养虫室造价低，较容易建立，适用于饲养普通试验观察的昆虫种群。本文对4种设备的比较为农业科学研究中昆虫饲养设备的选择及管理维护提供了参考。     

Increasing the Contrast of the Brain MR FLAIR Images Using Fuzzy Membership Functions and Structural Similarity Indices in Order to Segment MS Lesions

Segmentation is an important step for the diagnosis of multiple sclerosis (MS). This paper presents a new approach to the fully automatic segmentation of MS lesions in Fluid Attenuated Inversion Recovery (FLAIR) Magnetic Resonance (MR) images. With the aim of increasing the contrast of the FLAIR MR images with respect to the MS lesions, the proposed method first estimates the fuzzy memberships of brain tissues (i.e., the cerebrospinal fluid (CSF), the normal-appearing brain tissue (NABT), and the lesion). The procedure for determining the fuzzy regions of their member functions is performed by maximizing fuzzy entropy through Genetic Algorithm. Research shows that the intersection points of the obtained membership functions are not accurate enough to segment brain tissues. Then, by extracting the structural similarity (SSIM) indices between the FLAIR MR image and its lesions membership image, a new contrast-enhanced image is created in which MS lesions have high contrast against other tissues. Finally, the new contrast-enhanced image is used to segment MS lesions. To evaluate the result of the proposed method, similarity criteria from all slices from 20 MS patients are calculated and compared with other methods, which include manual segmentation. The volume of segmented lesions is also computed and compared with Gold standard using the Intraclass Correlation Coefficient (ICC) and paired samples t test. Similarity index for the patients with small lesion load, moderate lesion load and large lesion load was 0.7261, 0.7745 and 0.8231, respectively. The average overall similarity index for all patients is 0.7649. The t test result indicates that there is no statistically significant difference between the automatic and manual segmentation. The validated results show that this approach is very promising.     

Automatic wheat ear counting using machine learning based on RGB UAV imagery

In wheat (Triticum aestivum L) and other cereals, the number of ears per unit area is one of the main yield‐determining components. An automatic evaluation of this parameter may contribute to the advance of wheat phenotyping and monitoring. There is no standard protocol for wheat ear counting in the field, and moreover it is time consuming. An automatic ear‐counting system is proposed using machine learning techniques based on RGB (red, green, blue) images acquired from an unmanned aerial vehicle (UAV). Evaluation was performed on a set of 12 winter wheat cultivars with three nitrogen treatments during the 2017–2018 crop season. The automatic system uses a frequency filter, segmentation and feature extraction, with different classification techniques, to discriminate wheat ears in micro‐plot images. The relationship between the image‐based manual counting and the algorithm counting exhibited high levels of accuracy and efficiency. In addition, manual ear counting was conducted in the field for secondary validation. The correlations between the automatic and the manual in‐situ ear counting with grain yield were also compared. Correlations between the automatic ear counting and grain yield were stronger than those between manual in‐situ counting and GY, particularly for the lower nitrogen treatment. Methodological requirements and limitations are discussed.     

Assessment and value of the polybrene microplate test for study and identification of irregular anti-erythrocyte antibodies

We have adapted Lalezari's manual polybrene test for use with microplate technology for screening and identification of anti-erythrocytes antibodies with a view to future automation. The technical conditions have been standardized, firstly by using a programmable centrifuge and a sequential shaking, secondly by using a preservative medium for panel after dispensing onto microplates. This methodology has been run in parallel with papain test and LIS indirect antiglobulin test: 7,000 screenings have been performed and their results are considered here. Our results are comparable to those described for automatic and manual techniques. The polybrene-microplate test affords a fast, reliable, handy and inexpensive means of screening and identification for irregular antibodies. It appears as an additional method for enzymatic tests in microplate. An antiglobulin test can be carried out after negative tests.     

High-performance confocal system for microscopic or endoscopic applications

We designed a high-performance confocal system that can be easily adapted to an existing light microscope or coupled with an endoscope for remote imaging. The system employs spatially and temporally patterned illumination produced by one of several mechanisms, including a micromirror array video projection device driven by a computer video source or a microlens array scanned by a piezo actuator in the microscope illumination path. A series of subsampled "component" video images are acquired from a solid-state video camera. Confocal images are digitally reconstructed using "virtual pinhole" synthetic aperture techniques applied to the collection of component images. Unlike conventional confocal techniques that raster scan a single detector and illumination point, our system samples multiple locations in parallel, with particular advantages for monitoring fast dynamic processes. We compared methods of patterned illumination and confocal image reconstruction by characterizing the point spread function, contrast, and intensity of imaged objects. Sample 3D reconstructions include a diatom and a Golgi-stained nerve cell collected in transmission.     

Neural network approach for image chromatic adaptation for skin color detection

The goal of image chromatic adaptation is to remove the effect of illumination and to obtain color data that reflects precisely the physical contents of the scene. We present in this paper an approach to image chromatic adaptation using Neural Networks (NN) with application for detecting--adapting human skin color. The NN is trained on randomly chosen color images containing human subject under various illuminating conditions, thereby enabling the model to dynamically adapt to the changing illumination conditions. The proposed network predicts directly the illuminant estimate in the image so as to adapt to human skin color. The comparison of our method with Gray World, White Patch and NN on White Patch methods for skin color stabilization is presented. The skin regions in the NN stabilized images are successfully detected using a computationally inexpensive thresholding operation. We also present results on detecting skin regions on a data set of test images. The results are promising and suggest a new approach for adapting human skin color using neural networks.     

Canopy-cover thematic-map generation for Military Map products using remote sensing data in inaccessible areas

Canopy cover is one of the most important elements in concealing military structures and enemy reconnaissance. In this study, we propose an algorithm for automatic generation of density measure of percent canopy cover, which is an attribute of the digital Military Map product, using high-resolution satellite images of inaccessible areas. The thematic mapping process of canopy cover can be divided into image classification, segmentation, and texture analysis. QuickBird and SPOT-5 high-resolution images are classified using Landsat images and military maps. Then, forested areas are extracted from the classified images, and closing and opening operations are executed through morphology filtering. The extracted region is divided into unit-zone objects using a segmentation technique, and the percentage of canopy cover of each object is categorized as one of four levels (0–25, 26–50, 51–75, 76–100%). Two methods were used to establish the percentage of canopy cover for each segment: the discriminant method, using statistical analysis, and the classified canopy ratio method, which calculates the percentage of forest in the segment. The discriminant method showed 48% (QuickBird) and 61% (SPOT-5) accuracy and classified canopy ratio method showed 71% (QuickBird) and 87% (SPOT-5) accuracy.     

Method of receiving differential images of objects autofluorescence in the process of photobleaching

The microscope with a high sensitive video camera and laser illumination was used to study autofluorescence changes of different structures in the photobleached region with a different speed. The work with images using the ImageJ program is described in application how to receive differential images of objects autofluorescence in the process of photobleaching.     

Haptic Exploratory Behavior During Object Discrimination: A Novel Automatic Annotation Method

In order to acquire information concerning the geometry and material of handheld objects, people tend to execute stereotypical hand movement patterns called haptic Exploratory Procedures (EPs). Manual annotation of haptic exploration trials with these EPs is a laborious task that is affected by subjectivity, attentional lapses, and viewing angle limitations. In this paper we propose an automatic EP annotation method based on position and orientation data from motion tracking sensors placed on both hands and inside a stimulus. A set of kinematic variables is computed from these data and compared to sets of predefined criteria for each of four EPs. Whenever all criteria for a specific EP are met, it is assumed that that particular hand movement pattern was performed. This method is applied to data from an experiment where blindfolded participants haptically discriminated between objects differing in hardness, roughness, volume, and weight. In order to validate the method, its output is compared to manual annotation based on video recordings of the same trials. Although mean pairwise agreement is less between human-automatic pairs than between human-human pairs (55.7% vs 74.5%), the proposed method performs much better than random annotation (2.4%). Furthermore, each EP is linked to a specific object property for which it is optimal (e.g., Lateral Motion for roughness). We found that the percentage of trials where the expected EP was found does not differ between manual and automatic annotation. For now, this method cannot yet completely replace a manual annotation procedure. However, it could be used as a starting point that can be supplemented by manual annotation.     

Image Generation Using Bidirectional Integral Features for Face Recognition with a Single Sample per Person

In face recognition, most appearance-based methods require several images of each person to construct the feature space for recognition. However, in the real world it is difficult to collect multiple images per person, and in many cases there is only a single sample per person (SSPP). In this paper, we propose a method to generate new images with various illuminations from a single image taken under frontal illumination. Motivated by the integral image, which was developed for face detection, we extract the bidirectional integral feature (BIF) to obtain the characteristics of the illumination condition at the time of the picture being taken. The experimental results for various face databases show that the proposed method results in improved recognition performance under illumination variation.     

Use of three-dimensional microscopy with multiple oblique illumination in evaluating cytopathology preparations

OBJECTIVE: To analyze whether the use of multiple oblique illumination (MOI), in contrast to axial illumination (AI), improves imagery in bright field microscopy. Specimens containing thick cell clusters, such as cervical Pap smears, are often misread because of out-of-focus cell clusters. We hypothesized that visualization of these specimens with MOI would enhance this information as compared with AI. STUDY DESIGN: Micrometer images and Pap smears were analyzed in focus, and 8 and 40 microns out of focus by MOI and AI. All fields were captured to a remote computer, histograms were constructed, and mean light intensity was calculated. Mathematical formulation was used to define the histogram distribution of the micrometer images. Statistical analysis was performed using one-way ANOVA and Newman-Keuls test. RESULTS: K(focus) was improved (P < .01) and at 20 and 40 microns out of focus, mean light intensities were greater (P < .003, P < .05, respectively) with MOI as compared to AI. CONCLUSION: MOI improves out-of-focus information by increasing light penetration through the specimen and enhancing contrast and resolution.     

Karyotyping of comparative genomic hybridization human metaphases using kernel nearest-neighbor algorithm

BACKGROUND: Comparative genomic hybridization (CGH) is a relatively new molecular cytogenetic method that detects chromosomal imbalances. Automatic karyotyping is an important step in CGH analysis because the precise position of the chromosome abnormality must be located and manual karyotyping is tedious and time-consuming. In the past, computer-aided karyotyping was done by using the 4',6-diamidino-2-phenylindole, dihydrochloride (DAPI)-inverse images, which required complex image enhancement procedures. METHODS: An innovative method, kernel nearest-neighbor (K-NN) algorithm, is proposed to accomplish automatic karyotyping. The algorithm is an application of the "kernel approach," which offers an alternative solution to linear learning machines by mapping data into a high dimensional feature space. By implicitly calculating Euclidean or Mahalanobis distance in a high dimensional image feature space, two kinds of K-NN algorithms are obtained. New feature extraction methods concerning multicolor information in CGH images are used for the first time. RESULTS: Experiment results show that the feature extraction method of using multicolor information in CGH images improves greatly the classification success rate. A high success rate of about 91.5% has been achieved, which shows that the K-NN classifier efficiently accomplishes automatic chromosome classification from relatively few samples. CONCLUSIONS: The feature extraction method proposed here and K-NN classifiers offer a promising computerized intelligent system for automatic karyotyping of CGH human chromosomes.     

Fully automatic input function determination program for simple noninvasive 123I-IMP microsphere cerebral blood flow quantification method

We recently developed a simple noninvasive ¹²³I-IMP microsphere (SIMS) method using chest dynamic planar images and brain single photon emission computed tomography. The SIMS method is an automatic analysis method, except for the process of setting the region of interest (ROI) of the input function. If a fully automatic ROI setting algorithm can be developed to determine the input function for the SIMS method, repeatability and reproducibility of the analysis of regional cerebral blood flow (rCBF) of the SIMS method can be guaranteed. The purpose of this study is to develop a fully automatic input function determination program for the SIMS method and to confirm the clinical usefulness of this program.The automatic input function determination program consists of two ROI setting programs for the PA and lung regions, and it is developed using the image phase analysis of a chest RI angiogram. To confirm the clinical usefulness of this program, the rCBF in 34 patients measured using the automatic method were compared with the values obtained through the manual setting method.Input functions by the automatic and manual methods were approximately equal. A good correlation was observed between the rCBF values obtained by the automatic method and those obtained by the manual setting method (r = 0.96, p < 0.01).Further, the total time taken for the automatic SIMS analysis is 1–2 min as compared to 20–30 min for the current analysis, and therefore, this technique contributes to the improvement of the throughput of nuclear medical examinations.