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An automatic focusing system is a crucial component of automated microscopes, adjusting the lens-to-object distance to find the optimal focus by maximizing the focus measure (FM) value. This study develops reliable autofocus methods for hyperspectral imaging microscope systems, essential for extracting accurate chemical and spatial information from hyperspectral datacubes. Since FMs are domain- and application-specific, commonly, their performance is evaluated using verified focus positions. For example, in optical microscopy, the sharpness/contrast of visual peculiarities of a sample under testing typically guides as an anchor to determine the best focus position, but this approach is challenging in hyperspectral imaging systems (HSISs), where instant two-dimensional hyperspectral images do not always possess human-comprehensible visual information. To address this, a principal component analysis (PCA) was used to define the optimal ("ideal") optical focus position in HSIS, providing a benchmark for assessing 22 FMs commonly used in other imaging fields. Evaluations utilized hyperspectral images from visible (400-1100 nm) and near-infrared (900-1700 nm) bands across four different HSIS setups with varying magnifications. Results indicate that gradient-based FMs are the fastest and most reliable operators in this context.

In the field of autofocus for optical systems, although passive focusing methods are widely used due to their cost-effectiveness, fixed focusing windows and evaluation functions in certain scenarios can still lead to focusing failures. Additionally, the lack of datasets limits the extensive research of deep learning methods. In this work, we propose a neural network autofocus method with the capability of dynamically selecting the region of interest (ROI). Our main work is as follows: first, we construct a dataset for automatic focusing of grayscale images; second, we transform the autofocus issue into an ordinal regression problem and propose two focusing strategies: full-stack search and single-frame prediction; and third, we construct a MobileViT network with a linear self-attention mechanism to achieve automatic focusing on dynamic regions of interest. The effectiveness of the proposed focusing method is verified through experiments, and the results show that the focusing MAE of the full-stack search can be as low as 0.094, with a focusing time of 27.8 ms, and the focusing MAE of the single-frame prediction can be as low as 0.142, with a focusing time of 27.5 ms.

Active vision systems (AVSs) have been widely used to obtain high-resolution images of objects of interest. However, tracking small objects in high-magnification scenes is challenging due to shallow depth of field (DoF) and narrow field of view (FoV). To address this, we introduce a novel high-speed AVS with a continuous autofocus (C-AF) approach based on dynamic-range focal sweep and a high-frame-rate (HFR) frame-by-frame tracking pipeline. Our AVS leverages an ultra-fast pan-tilt mechanism based on a Galvano mirror, enabling high-frequency view direction adjustment. Specifically, the proposed C-AF approach uses a 500 fps high-speed camera and a focus-tunable liquid lens operating at a sine wave, providing a 50 Hz focal sweep around the object's optimal focus. During each focal sweep, 10 images with varying focuses are captured, and the one with the highest focus value is selected, resulting in a stable output of well-focused images at 50 fps. Simultaneously, the object's depth is measured using the depth-from-focus (DFF) technique, allowing dynamic adjustment of the focal sweep range. Importantly, because the remaining images are only slightly less focused, all 500 fps images can be utilized for object tracking. The proposed tracking pipeline combines deep-learning-based object detection, K-means color clustering, and HFR tracking based on color filtering, achieving 500 fps frame-by-frame tracking. Experimental results demonstrate the effectiveness of the proposed C-AF approach and the advanced capabilities of the high-speed AVS for magnified object tracking.

'openFrame' is a modular, low-cost, open-hardware microscopy platform that can be configured or adapted to most light microscopy techniques and is easily upgradeable or expandable to multiple modalities. The ability to freely mix and interchange both open-source and proprietary hardware components or software enables low-cost, yet research-grade instruments to be assembled and maintained. It also enables rapid prototyping of advanced or novel microscope systems. For long-term time-lapse image data acquisition, slide-scanning or high content analysis, we have developed a novel optical autofocus incorporating orthogonal cylindrical optics to provide robust single-shot closed-loop focus lock, which we have demonstrated to accommodate defocus up to ±37 μm with <200 nm accuracy, and a two-step autofocus mode which we have shown can operate with defocus up to ±68 μm. We have used this to implement automated single molecule localisation microscopy (SMLM) in a relatively low-cost openFrame-based instrument using multimode diode lasers for excitation and cooled CMOS cameras., (© 2023 The Authors. Journal of Microscopy published by John Wiley & Sons Ltd on behalf of Royal Microscopical Society.)

Hyperspectral imaging (HSI) captures a greater level of spectral detail than traditional optical imaging, making it a potentially valuable intraoperative tool when precise tissue differentiation is essential. Hardware limitations of current optical systems used for handheld realtime video HSI result in a limited focal depth, thereby posing usability issues for integration of the technology into the operating room. This work integrates a focus-tunable liquid lens into a video HSI exoscope, and proposes novel video autofocusing methods based on deep reinforcement learning. A first-of-its-kind robotic focal-time scan was performed to create a realistic and reproducible testing dataset. We benchmarked our proposed autofocus algorithm against traditional policies, and found our novel approach to perform significantly ( p < 0.05) better than traditional techniques (0.070 ±.098 mean absolute focal error compared to 0.146 ±.148). In addition, we performed a blinded usability trial by having two neurosurgeons compare the system with different autofocus policies, and found our novel approach to be the most favourable, making our system a desirable addition for intraoperative HSI.

Piezoelectric objective driver positioners are increasingly used in the field of microscopy. They have the advantages of high dynamic and fast response. This paper presents a fast autofocus algorithm for highly interactive microscope system. First, the Tenengrad gradient of the down-sampled image is used to calculate the image sharpness, and Brent search method is adopted to quickly converge to the correct focal length. At the same time, the input shaping method is used to eliminate the displacement vibration of the piezoelectric objective lens driver and further accelerate the image acquisition speed. Experimental results show that the proposed scheme can improve the speed of the automatic focusing task of the piezoelectric objective driver and improve the real-time focus of the automatic microscopic system. HIGHLIGHTS: A high real-time autofocus strategy. A vibration control method suitable for a piezoelectric objective driver., (© 2023 Wiley Periodicals LLC.)

An algorithm for a sharpness evaluation of microscopic images based on non-subsampled shearlet wave transform (NSST) and variance is proposed in the present study for the purpose of improving the noise immunity and accuracy of a microscope's image autofocus. First, images are decomposed with the NSST algorithm; then, the decomposed sub-band images are subjected to variance to obtain the energy of the sub-band coefficients; and finally, the evaluation value is obtained from the ratio of the energy of the high- and low-frequency sub-band coefficients. The experimental results show that the proposed algorithm delivers better noise immunity performance than other methods reviewed by this study while maintaining high sensitivity.

In optoacoustic (photoacoustic) tomography, several parameters related to tissue and detector features are needed for image formation, but they may not be known a priori. An autofocus (AF) algorithm is generally used to estimate these parameters. However, the algorithm works iteratively and is therefore impractical for clinical imaging with planar geometry systems due to the long reconstruction times. We have developed a fast autofocus (FAF) algorithm for 3D optoacoustic systems with planar geometry. Such an algorithm exploits the symmetries of the planar geometry and a virtual source concept to reduce the dimensionality of the parameter estimation problem. The dimensionality reduction makes FAF much simpler computationally than the conventional AF algorithm. We show that the FAF algorithm required about 5 s to provide accurate estimates of the speed of sound in simulated data and experimental data obtained using an imaging system that is poised to enter the clinic. The applicability of FAF for estimating other image formation parameters is discussed. We expect the FAF algorithm to contribute decisively to the clinical use of optoacoustic tomography systems with planar geometry., (© 2022 The Authors. Journal of Biophotonics published by Wiley-VCH GmbH.)

Microscopic hyperspectral imaging technology has been widely used to acquire pathological information of tissue sections. Autofocus is one of the most important steps in microscopic hyperspectral imaging systems to capture large scale or even whole slide images of pathological slides with high quality and high speed. However, there are quite few autofocus algorithm put forward for the microscopic hyperspectral imaging system. Therefore, this article proposes a Laplace operator based autofocus algorithm for microscopic hyperspectral imaging system which takes the influence of wavelength changes into consideration. Through the proposed algorithm, the focal length for each wavelength can be adjusted automatically to ensure that each single band image can be autofocused precisely with adaptive image sharpness evaluation method. In addition, to increase the capture speed, the relationship of wavelength and focal length is derived and the focal offsets among different single band images are calculated for pre-focusing. We have employed the proposed method on our own datasets and the experimental results show that it can capture large-scale microscopic hyperspectral pathology images with high precise., (© 2022 Wiley-VCH GmbH.)

For many parasitic diseases, the microscopic examination of clinical samples such as urine and stool still serves as the diagnostic reference standard, primarily because microscopes are accessible and cost-effective. However, conventional microscopy is laborious, requires highly skilled personnel, and is highly subjective. Requirements for skilled operators, coupled with the cost and maintenance needs of the microscopes, which is hardly done in endemic countries, presents grossly limited access to the diagnosis of parasitic diseases in resource-limited settings. The urgent requirement for the management of tropical diseases such as schistosomiasis, which is now focused on elimination, has underscored the critical need for the creation of access to easy-to-use diagnosis for case detection, community mapping, and surveillance. In this paper, we present a low-cost automated digital microscope-the Schistoscope-which is capable of automatic focusing and scanning regions of interest in prepared microscope slides, and automatic detection of Schistosoma haematobium eggs in captured images. The device was developed using widely accessible distributed manufacturing methods and off-the-shelf components to enable local manufacturability and ease of maintenance. For proof of principle, we created a Schistosoma haematobium egg dataset of over 5000 images captured from spiked and clinical urine samples from field settings and demonstrated the automatic detection of Schistosoma haematobium eggs using a trained deep neural network model. The experiments and results presented in this paper collectively illustrate the robustness, stability, and optical performance of the device, making it suitable for use in the monitoring and evaluation of schistosomiasis control programs in endemic settings.

Purpose: Respiratory motion-corrected coronary MR angiography (CMRA) has shown promise for assessing coronary disease. By incorporating coronal 2D image navigators (iNAVs), respiratory motion can be corrected for in a beat-to-beat basis using translational correction in the foot-head (FH) and right-left (RL) directions and in a bin-to-bin basis using non-rigid motion correction addressing the remaining FH, RL and anterior-posterior (AP) motion. However, with this approach beat-to-beat AP motion is not corrected for. In this work we investigate the effect of remaining beat-to-beat AP motion and propose a virtual 3D iNAV that exploits autofocus motion correction to enable beat-to-beat AP and improved RL intra-bin motion correction., Methods: Free-breathing 3D whole-heart CMRA was acquired using a 3-fold undersampled variable-density Cartesian trajectory. Beat-to-beat 3D translational respiratory motion was estimated from the 2D iNAVs in FH and RL directions, and in AP direction with autofocus assuming a linear relationship between FH and AP movement of the heart. Furthermore, motion in RL was also refined using autofocus. This virtual 3D (v3D) iNAV was incorporated in a non-rigid motion correction (NRMC) framework. The proposed approach was tested in 12 cardiac patients, and visible vessel length and vessel sharpness for the right (RCA) and left (LAD) coronary arteries were compared against 2D iNAV-based NRMC., Results: Average vessel sharpness and length in v3D iNAV NRMC was improved compared to 2D iNAV NRMC (vessel sharpness: RCA: 56 ± 1% vs 52 ± 11%, LAD: 49 ± 8% vs 49 ± 7%; visible vessel length: RCA: 5.98 ± 1.37 cm vs 5.81 ± 1.62 cm, LAD: 5.95 ± 1.85 cm vs 4.83 ± 1.56 cm), however these improvements were not statistically significant., Conclusion: The proposed virtual 3D iNAV NRMC reconstruction further improved NRMC CMRA image quality by reducing artefacts arising from residual AP motion, however the level of improvement was subject-dependent., (Copyright © 2022. Published by Elsevier Inc.)

Multilayer polymer composite structures have been playing important roles in various fields, but the voids inside are not allowed in most scenarios. Ultrasonic technology has been widely used to inspect voids in concrete and metal structures. However, the application of ultrasonic imaging in polymer composite structures is severely blocked by the coating and lamination structures and unstable manufacturing induced sound speed variations. In this paper, a method to autofocus imaging of internal voids in multilayer polymer composite structures with ultrasonic phased array is firstly proposed. The method processes the full matrix capture (FMC) and focuses all voids in the multilayer structure automatically without the prior information of the speed of sound (SOS). The method utilizes the focus criterions to evaluate the focusing quality and then estimates the SOS with differential evolution layer by layer from surface to deep, which improves the robustness and computational efficiency. The method was examined with simulation data from three multilayer structures and well-focused all voids with position error less than 0.6 mm and SOS error less than 6 %. Moreover, the method was verified with the experimental data and focused voids with position error less than 1 mm., (Copyright © 2021 Elsevier B.V. All rights reserved.)

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., (© 2021 The Authors. Journal of Microscopy published by John Wiley & Sons Ltd on behalf of Royal Microscopical Society.)

Trypan blue dye exclusion-based cell viability measurements are highly dependent upon image quality and consistency. In order to make measurements repeatable, one must be able to reliably capture images at a consistent focal plane, and with signal-to-noise ratio within appropriate limits to support proper execution of image analysis routines. Imaging chambers and imaging systems used for trypan blue analysis can be inconsistent or can drift over time, leading to a need to assure the acquisition of images prior to automated image analysis. Although cell-based autofocus techniques can be applied, the heterogeneity and complexity of the cell samples can make it difficult to assure the effectiveness, repeatability and accuracy of the routine for each measurement. Instead of auto-focusing on cells in our images, we add control beads to the images, and use them to repeatedly return to a reference focal plane. We use bead image features that have stable profiles across a wide range of focal values and exposure levels. We created a predictive model based on image quality features computed over reference datasets. Because the beads have little variation, we can determine the reference plane from bead image features computed over a single-shot image and can reproducibly return to that reference plane with each sample. The achieved accuracy (over 95%) is within the limits of the actuator repeatability. We demonstrate that a small number of beads (less than 3 beads per image) is needed to achieve this accuracy. We have also developed an open-source Graphical User Interface called Bead Benchmarking-Focus And Intensity Tool (BB-FAIT) to implement these methods for a semi-automated cell viability analyser., (© 2021 Royal Microscopical Society. This article has been contributed to by US Government employees and their work is in the public domain in the USA.)

Wearable Video See-Through (VST) devices for Augmented Reality (AR) and for obtaining a Magnified View are taking hold in the medical and surgical fields. However, these devices are not yet usable in daily clinical practice, due to focusing problems and a limited depth of field. This study investigates the use of liquid-lens optics to create an autofocus system for wearable VST visors. The autofocus system is based on a Time of Flight (TOF) distance sensor and an active autofocus control system. The integrated autofocus system in the wearable VST viewers showed good potential in terms of providing rapid focus at various distances and a magnified view., Competing Interests: The authors declare no conflict of interest.

Autofocus is an essential technique for airborne synthetic aperture radar (SAR) imaging to correct phase errors mainly due to unexpected motion error. There are several well-known conventional autofocus methods such as phase gradient autofocus (PGA) and minimum entropy (ME). Although these methods are still widely used for various SAR applications, each method has drawbacks such as limited bandwidth of estimation, low convergence rate, huge computation burden, etc. In this paper, feature preserving autofocus (FPA) algorithm is newly proposed. The algorithm is based on the minimization of the cost function containing a regularization term. The algorithm is designed for postprocessing purpose, which is different from the existing regularization-based algorithms such as sparsity-driven autofocus (SDA). This difference makes the proposed method far more straightforward and efficient than those existing algorithms. The experimental results show that the proposed algorithm achieves better performance, convergence, and robustness than the existing postprocessing autofocus algorithms.

In this paper, we present an infrared microscopy based approach for structures' location in integrated circuits, to automate their secure characterization. The use of an infrared sensor is the key device for internal integrated circuit inspection. Two main issues are addressed. The first concerns the scan of integrated circuits using a motorized optical system composed of an infrared uncooled camera combined with an optical microscope. An automated system is required to focus the conductive tracks under the silicon layer. It is solved by an autofocus system analyzing the infrared images through a discrete polynomial image transform which allows an accurate features detection to build a focus metric robust against specific image degradation inherent to the acquisition context. The second issue concerns the location of structures to be characterized on the conductive tracks. Dealing with a large amount of redundancy and noise, a graph-matching method is presented-discriminating graph labels are developed to overcome the redundancy, while a flexible assignment optimizer solves the inexact matching arising from noises on graphs. The resulting automated location system brings reproducibility for secure characterization of integrated systems, besides accuracy and time speed increase.

During the process of whole slide imaging, it is necessary to focus thousands of fields of view to obtain a high-quality image. To make the focusing procedure efficient and effective, we propose a novel autofocus algorithm for whole slide imaging. It is based on convolution and recurrent neural networks to predict the out-of-focus distance and subsequently update the focus location of the camera lens in an iterative manner. More specifically, we train a convolution neural network to extract focus information in the form of a focus feature vector. In order to make the prediction more accurate, we apply a recurrent neural network to combine focus information from previous search iteration and current search iteration to form a feature aggregation vector. This vector contains more focus information than the previous one and is subsequently used to predict the out-of-focus distance. Our experiments indicate that our proposed autofocus algorithm is able to rapidly determine the optimal in-focus image. The code is available at https://github.com/hezhujun/autofocus-rnn., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Well-focused and accurately scaled high-resolution inverse synthetic aperture radar (ISAR) images provide a sound basis for feature extraction and target recognition. This paper proposes a novel high-resolution ISAR imaging algorithm, namely modified joint range spatial-variant autofocus and azimuth scaling algorithm (MJAAS). After motion compensation, the shift of the equivalent rotational center (ERC) of the target destroys the linear relationship between the azimuth chirp rates (ACR) of echo signals and the range coordinates of scattering points, thereby leading to the failure of azimuth scaling. Accordingly, a new joint equivalent rotational center position and effective rotational velocity (JERCP-ERV) signal model is established, serving as the basis of MJAAS. By recourse to the Davidon-Fletcher-Powell (DFP) algorithm, MJAAS can jointly estimate the ERCP and ERV by solving a minimum entropy optimization problem, so as to simultaneously achieve accurate azimuth scaling and range spatial-variant autofocus, which further improves the image focusing performance. MJAAS is not restricted by the modes of motion errors (coherent or non-coherent) and the motion compensation methods, so it can be widely applied to real data with the advantages of strong practicality and high accuracy. Extensive experimental results based on both simulated and real data are provided to corroborate the effectiveness of the proposed algorithm.

Micromanipulation is an interdisciplinary technology that integrates advanced knowledge of microscale/nanoscale science, mechanical engineering, electronic engineering, and control engineering. Over the past two decades, it has been widely applied in the fields of MEMS (microelectromechanical systems), bioengineering, and microdevice integration and manufacturing. Microvision servoing is the basic tool for enabling the automatic and precise micromanipulation of microscale/nanoscale entities. However, there are still many problems surrounding microvision servoing in theory and the application of this technology's micromanipulation processes. This paper summarizes the research, development status, and practical applications of critical components of microvision servoing for micromanipulation, including geometric calibration, autofocus techniques, depth information, and visual servoing control. Suggestions for guiding future innovation and development in this field are also provided in this review., Competing Interests: The authors declare no conflicts of interest.

As a powerful signal processing tool for imaging moving targets, placing radar on a non-stationary platform (such as an aerostat) is a future direction of Inverse Synthetic Aperture Radar (ISAR) systems. However, more phase errors are introduced into the received signal due to the instability of the radar platform, making it difficult for popular algorithms to accurately perform motion compensation, which leads to severe effects in the resultant ISAR images. Moreover, maneuvering targets may have complex motion whose motion parameters are unknown to radar systems. To overcome the issue of non-stationary platform ISAR autofocus imaging, a high-resolution imaging method based on the phase retrieval principle is proposed in this paper. Firstly, based on the spatial geometric and echo models of the ISAR maneuvering target, we can deduce that the radial motion of the radar platform or the vibration does not affect the modulus of the ISAR echo signal, which provides a theoretical basis for the phase recovery theory for the ISAR imaging. Then, we propose an oversampling smoothness (OSS) phase retrieval algorithm with prior information, namely, the phase of the blurred image obtained by the classical imaging algorithm replaces the initial random phase in the original OSS algorithm. In addition, the size of the support domain of the OSS algorithm is set with respect to the blurred target image. Experimental simulation shows that compared with classical imaging methods, the proposed method can obtain the resultant motion-compensated ISAR image without estimating the radar platform and maneuvering target motion parameters, wherein the fictitious target is perfectly focused.

Aberration correctors offer greatly enhanced resolution in electron microscopes, however can require dramatically more complicated adjustments. A method of computer adjustment of a probe forming aberration corrector in a Scanning Transmission Electron Microscope (STEM) is proposed and analyzed using image simulation. This method works directly with the image and should work well with crystalline specimens. It does not have a significant dependence on post specimen lens aberrations., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Since the inception of commercialized automated high content screening (HCS) imaging devices in the mid to late 1990s, the adoption of media vessels typically used to house and contain biological specimens for interrogation has transitioned from microscope slides and petri dishes into multi-well microtiter plates called microplates. The early 96- and 384-well microplates commonly used in other high-throughput screening (HTS) technology applications were often not designed for optical imaging. Since then, modifications and the use of next-generation materials with improved optical clarity have enhanced the quality of captured images, reduced autofocusing failures, and empowered the use of higher power magnification objectives to resolve fine detailed measurements at the subcellular pixel level. The plethora of microplates and their applications requires practitioners of high content imaging (HCI) to be especially diligent in the selection and adoption of the best plates for running longitudinal studies or larger screening campaigns. While the highest priority in experimental design is the selection of the biological model, the choice of microplate can alter the biological response and ultimately may change the experimental outcome. This chapter will provide readers with background, troubleshooting guidelines, and considerations for choosing an appropriate microplate.

Exact focusing is essential for any automatic image capturing system. Performances of focus measure functions (FMFs) used for autofocusing are sensitive to image contents and imaging systems. Therefore, identification of universal FMF assumes a lot of significance. Eight FMFs were hybridized in pairs of two and implemented simultaneously on a single stack to calculate the hybrid focus measure. In total, 28 hybrid FMFs (HFMFs) and eight FMFs were implemented on stacks of images from three different imaging modalities. Performance of FMFs was found to the best at 50% region sampling. Accuracy, focus error, and false maxima were calculated to evaluate the performance of each FMF. Nineteen HFMFs provided >90% accuracy. Image distortion (noise, contrast, saturation, illumination, etc.) was performed to evaluate robustness of HFMFs. Hybrid of tenengrad variance and steerable filter-based (VGRnSFB) FMFs was identified as the most robust and accurate function with an accuracy of ≥90% and a relatively lower focus error and false maxima rate. Sharpness of focus curve of VGRnSFB along with eight individual FMFs was also computed to determine the efficacy of HFMF for the optimization process. VGRnSFB HFMF may be implemented for automated capturing of an image for any imaging system., ((2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).)

The sharpness of the images coming from a Scanning Electron Microscope (SEM) is a very important property for many computer vision applications at micro- and nanoscale. It represents how much object details are distinctive in the images: the object may be perceived sharp or blurred. Image sharpness highly depends on the value of focal distance, or working distance in the case of the SEM. Autofocus is the technique allowing to automatically adjust the working distance to maximize the sharpness. Most of the existing algorithms allows working only with a static object which is enough for the tasks of visualization, manual microanalysis or microcharacterization. These applications work with a low frame rate, less than 1 Hz, that guarantees a low level of noise. However, static autofocus can not be used for samples performing continuous 3D motion, which is the case of robotic applications where it is required to carry out a continuous 3D position measurement, e.g., nano-assembly or nanomanipulation. Moreover, in addition to constantly keeping object in focus while it is moving, it is required to perform the operation at high frame rate. The approach offering both these possibilities is presented in this paper and is referred as dynamic autofocus. The presented solution is based on stochastic optimization techniques. It allows tracking the maximum of the sharpness of the images without sweep and without training. It works under uncertainty conditions: presence of noise in images, unknown maximal sharpness and unknown 3D motion of the specimen. The experiments, that were performed with noisy images at high frame rate (5 Hz), were conducted on a Carl Zeiss Auriga 60 FE-SEM. They prove the robustness of the algorithm with respect to the variation of optimization parameters, object speed and magnification. Moreover, it is invariant to the object structure and its variation in time., (Copyright © 2017 Elsevier B.V. All rights reserved.)

For the microvision system, a new autofocus evaluation function based on the Robert function is proposed by increasing the threshold value. Compared with the traditional evaluation function, the new focus function reduces the local extreme value and increases the steepness of the focusing curve. According to the characteristics of the focusing evaluation function, the focus curve can be divided into two stages: the gentle area and the steep area. In the gentle area, there will be set a large step-length to realize the fast search. In the steep area, the data will be fitted by Gauss method, and on the basis of the fitting results, the motor of microvision system was directly driven to achieve the focal plane and this method has been improved in real-time and accuracy., (© 2017 Wiley Periodicals, Inc.)

Capturing of the best-focused image using focus measure function (FMF) from a stack of images acquired at different focus distances is a crucial step in automatic microscopy development. Detection of bacilli present in Ziehl-Neelsen (ZN) stained sputum smears conventional microscope (CM) images is critical to disease diagnosis. Studies have revealed that the performances of FMFs are sensitive to image contents and background noise. In this article, 24 diverse FMFs were implemented on 31 stacks of CM's field of view images acquired from three different microscopes to determine the best-focused one. Seven FMFs achieved the accuracies of greater than 90%. Accuracy, focus error, and false maxima were calculated for each FMF, and overall score and ranking were also calculated for better interpretation. Preprocessing techniques such as filtering and image distortions (noise, contrast, saturation, illumination, etc.) were performed to evaluate the robustness of every FMF. Gaussian derivative, steerable filters, Tenengrad, and Hemli and Scherer's mean FMFs were identified as the most robust and accurate functions with the accuracy >90%. These FMFs have a relatively less focus error and false maxima rate. Full widths at half maximum of these four FMFs were also computed to determine their efficacy for the optimization process. These four FMFs can be implemented for automated capturing of the image from ZN-stained sputum smear slide. Gaussian derivative FMF can also be used effectively for both CM and fluorescence microscope's field of view image stacks to determine the best-focused one from each stack. © 2017 International Society for Advancement of Cytometry., (© 2017 International Society for Advancement of Cytometry.)

Autofocus is an important issue in electron microscopy, particularly at high magnification. It consists in searching for sharp image of a specimen, that is corresponding to the peak of focus. The paper presents a machine learning solution to this issue. From seven focus measures, support vector machines fitting is used to compute the peak with an initial guess obtained from a gradient ascent search, that is search in the direction of higher gradient of focus. The solution is implemented on a Carl Zeiss Auriga FE-SEM with a three benchmark specimen and magnification ranging from x300 to x160 000. Based on regularized nonlinear least squares optimization, the solution overtakes the literature nonregularized search and Fibonacci search methods: accuracy improvement ranges from 1.25 to 8 times, fidelity improvement ranges from 1.6 to 28 times, and speed improvement ranges from 1.5 to 4 times. Moreover, the solution is practical by requiring only an off-line easy automatic train with cross-validation of the support vector machines., (© 2016 The Authors Journal of Microscopy © 2016 Royal Microscopical Society.)

Automated optical inspection is a very important technique. For this reason, this study proposes an optical non-contact slanting surface measuring system. The essential features of the measurement system are obtained through simulations using the optical design software Zemax. The actual propagation of laser beams within the measurement system is traced by using a homogeneous transformation matrix (HTM), the skew-ray tracing method, and a first-order Taylor series expansion. Additionally, a complete mathematical model that describes the variations in light spots on photoelectric sensors and the corresponding changes in the sample orientation and distance was established. Finally, a laboratory prototype system was constructed on an optical bench to verify experimentally the proposed system. This measurement system can simultaneously detect the slanting angles (x, z) in the x and z directions of the sample and the distance (y) between the biconvex lens and the flat sample surface.

Background: Virtual microscopy and automated processing of cytological slides are more challenging compared to histological slides. Since cytological slides exhibit a three-dimensional surface and the required microscope objectives with high resolution have a low depth of field, these cannot capture all objects of a single field of view in focus. One solution would be to scan multiple focal planes; however, the increase in processing time and storage requirements are often prohibitive for clinical routine., Materials and Methods: In this paper, we show that it is a reasonable trade-off to scan a single focal plane and automatically reject defocused objects from the analysis. To this end, we have developed machine learning solutions for the automated identification of defocused objects. Our approach includes creating novel features, systematically optimizing their parameters, selecting adequate classifier algorithms, and identifying the correct decision boundary between focused and defocused objects. We validated our approach for computer-assisted DNA image cytometry., Results and Conclusions: We reach an overall sensitivity of 96.08% and a specificity of 99.63% for identifying defocused objects. Applied on ninety cytological slides, the developed classifiers automatically removed 2.50% of the objects acquired during scanning, which otherwise would have interfered the examination. Even if not all objects are acquired in focus, computer-assisted DNA image cytometry still identified more diagnostically or prognostically relevant objects compared to manual DNA image cytometry. At the same time, the workload for the expert is reduced dramatically.

This study introduces a passive autofocus method based on image analysis calculating the Bayes spectral entropy (BSE). The method is applied to optical microscopy and together with the specific construction of the opto-mechanical unit, it allows the analysis of large samples with complicated surfaces without subsampling. This paper will provide a short overview of the relevant theory of calculating the normalized discrete cosine transform when analyzing obtained images, in order to find the BSE measure. Furthermore, it will be shown that the BSE measure is a strong indicator, helping to determine the focal position of the optical microscope. To demonstrate the strength and robustness of the microscope system, tests have been performed using a 1951 USAF test pattern resolution chart determining the in focus position of the microscope. Finally, this method and the optical microscope system is applied to analyze an optical grating (100 lines/mm) demonstrating the detection of the focal position. The paper concludes with an outlook of potential applications of the presented system within quality control and surface analysis.

Purpose: To develop and assess motion correction techniques for high-resolution pediatric abdominal volumetric magnetic resonance images acquired free-breathing with high scan efficiency., Materials and Methods: First, variable-density sampling and radial-like phase-encode ordering were incorporated into the 3D Cartesian acquisition. Second, intrinsic multichannel butterfly navigators were used to measure respiratory motion. Lastly, these estimates are applied for both motion-weighted data-consistency in a compressed sensing and parallel imaging reconstruction, and for nonrigid motion correction using a localized autofocusing framework. With Institutional Review Board approval and informed consent/assent, studies were performed on 22 consecutive pediatric patients. Two radiologists independently scored the images for overall image quality, degree of motion artifacts, and sharpness of hepatic vessels and the diaphragm. The results were assessed using paired Wilcoxon test and weighted kappa coefficient for interobserver agreements., Results: The complete procedure yielded significantly better overall image quality (mean score of 4.7 out of 5) when compared to using no correction (mean score of 3.4, P < 0.05) and to using motion-weighted accelerated imaging (mean score of 3.9, P < 0.05). With an average scan time of 28 seconds, the proposed method resulted in comparable image quality to conventional prospective respiratory-triggered acquisitions with an average scan time of 91 seconds (mean score of 4.5)., Conclusion: With the proposed methods, diagnosable high-resolution abdominal volumetric scans can be obtained from free-breathing data acquisitions., (© 2014 Wiley Periodicals, Inc.)

Optical coherence tomography (OCT) provides structural information of laryngeal tissue which is comparable to histopathological analysis of biopsies taken under general anesthesia. In awake patients, movements impede clinically useful OCT acquisition. Therefore, an automatic compensation of movements was implemented into a swept source OCT-laryngoscope. Video and OCT beam path were combined in one tube of 10-mm diameter. Segmented OCT images served as distance sensor and a feedback control adjusted the working distance between 33 and 70 mm by synchronously translating the reference mirror and focusing lens. With this motion compensation, the tissue was properly visible in up to 88% of the acquisition time. During quiet respiration, OCT contrasted epithelium and lamina propria. Mean epithelial thickness was measured to be 109 and [Formula: see text] in female and male, respectively. Furthermore, OCT of mucosal wave movements during phonation enabled estimation of the oscillation frequency and amplitude. Regarding clinical issues, the OCT-laryngoscope with automated working distance adjustment may support the estimation of the depth extent of epithelial lesions and contribute to establish an indication for a biopsy. Moreover, OCT of the vibrating vocal folds provides functional information, possibly giving further insight into mucosal behavior during the vibratory cycle.

Purpose: To implement a nonrigid autofocus motion correction technique to improve respiratory motion correction of free-breathing whole-heart coronary magnetic resonance angiography acquisitions using an image-navigated 3D cones sequence., Methods: 2D image navigators acquired every heartbeat are used to measure superior-inferior, anterior-posterior, and right-left translation of the heart during a free-breathing coronary magnetic resonance angiography scan using a 3D cones readout trajectory. Various tidal respiratory motion patterns are modeled by independently scaling the three measured displacement trajectories. These scaled motion trajectories are used for 3D translational compensation of the acquired data, and a bank of motion-compensated images is reconstructed. From this bank, a gradient entropy focusing metric is used to generate a nonrigid motion-corrected image on a pixel-by-pixel basis. The performance of the autofocus motion correction technique is compared with rigid-body translational correction and no correction in phantom, volunteer, and patient studies., Results: Nonrigid autofocus motion correction yields improved image quality compared to rigid-body-corrected images and uncorrected images. Quantitative vessel sharpness measurements indicate superiority of the proposed technique in 14 out of 15 coronary segments from three patient and two volunteer studies., Conclusion: The proposed technique corrects nonrigid motion artifacts in free-breathing 3D cones acquisitions, improving image quality compared to rigid-body motion correction., (Copyright © 2013 Wiley Periodicals, Inc.)

An accurate method using image sharpness to determine the best focusing is proposed for ultra-high-voltage electron microscopy. This method maximizes image sharpness for adjusting the focus. Five images with different defocus values are used to calculate the image sharpness. To obtain the best focus value that produces greatest image sharpness, fitting the quasi-Gaussian function to five image sharpness is a suitable alternative. This method, which maximizes image sharpness, gives better accuracy than the wobbler method for the ultra-high-voltage electron microscope. The focusing area can be selected without moving the field of view, because the focusing area can be selected at almost any area in the image.

Context: Whole slide imaging (WSI) for digital pathology involves the rapid automated acquisition of multiple high-power fields from a microscope slide containing a tissue specimen. Capturing each field in the correct focal plane is essential to create high-quality digital images. Others have described a novel focusing method which reduces the number of focal planes required to generate accurate focus. However, this method was not applied dynamically in an automated WSI system under continuous motion., Aims: This report measures the accuracy of this method when applied in a rapid continuous scan mode using a dual sensor WSI system with interleaved acquisition of images., Methods: We acquired over 400 tiles in a "stop and go" scan mode, surveying the entire z depth in each tile and used this as ground truth. We compared this ground truth focal height to the focal height determined using a rapid 3-point focus algorithm applied dynamically in a continuous scanning mode., Results: Our data showed the average focal height error of 0.30 (±0.27) μm compared to ground truth, which is well within the system's depth of field. On a tile by tile assessment, approximately 95% of the tiles were within the system's depth of field. Further, this method was six times faster than acquiring tiles compared to the same method in a non-continuous scan mode., Conclusions: The data indicates that the method employed can yield a significant improvement in scan speed while maintaining highly accurate autofocusing.

Accurate focusing is a critical challenge of whole slide imaging, primarily due to inherent tissue topography variability. Traditional line scanning and tile-based scanning systems are limited in their ability to acquire a high degree of focus points while still maintaining high throughput. This review examines limitations with first-generation whole slide scanning systems and explores a novel approach that employs continuous autofocus, referred to as independent dual sensor scanning. This "second-generation" concept decouples image acquisition from focusing, allowing for rapid scanning while maintaining continuous accurate focus. The technical concepts, merits, and limitations of this method are explained and compared to that of a traditional whole slide scanning system.