Image Acquisition Approach Research Articles

A low-cost close-range photogrammetric surface scanner

IntroductionA low-cost, close-range photogrammetric surface scanner is proposed, made from Computer Numerical Control (CNC) components and an off-the-shelf, consumer-grade macro camera.MethodsTo achieve micrometer resolution in reconstruction, accurate and photorealistic surface digitization, and retain low manufacturing cost, an image acquisition approach and a reconstruction method are proposed. The image acquisition approach uses the CNC to systematically move the camera and acquire images in a grid tessellation and at multiple distances from the target surface. A relatively large number of images is required to cover the scanned surface. The reconstruction method tracks keypoint features to robustify correspondence matching and uses far-range images to anchor the accumulation of errors across a large number of images utilized.Results and discussionQualitative and quantitative evaluation demonstrate the efficacy and accuracy of this approach.

Aflatoxins detection in almonds via fluorescence imaging and deep neural network approach

The detection of aflatoxin B in raw food materials represents a topic of great interest worldwide because of the huge health and economic impact of aflatoxin contamination. In this paper, we present an original approach to aflatoxin detection, using a portable instrument to acquire fluorescence images, among other spectral responses. The acquired images are processed by combining a color space conversion from the RGB scale to Y′CbCr, and a neural network approach to encode a vector of features. After a feature reduction using a Receiving Operating Curve method, two-class and three-class classification tasks of contaminated vs non-contaminated samples are accomplished. This procedure has been applied to artificially contaminated grained almond samples in the range of 0–320.2 ng/g, achieving an overall classification accuracy between 84.7% and 93.0%, depending on the number of classes. Thus, in this setting, we show that good classification performance can be achieved using only an image acquisition and analysis approach. The proposed procedure can represent a cheap, rapid, non-destructive yet sensitive method for the assessment of aflatoxin B contamination in food matrices, and its monitoring and tracing throughout the food chain.

Biodistribution Profile of Magnetic Nanoparticles in Cirrhosis-Associated Hepatocarcinogenesis in Rats by AC Biosusceptometry

Since magnetic nanoparticles (MNPs) have been used as multifunctional probes to diagnose and treat liver diseases in recent years, this study aimed to assess how the condition of cirrhosis-associated hepatocarcinogenesis alters the biodistribution of hepatic MNPs. Using a real-time image acquisition approach, the distribution profile of MNPs after intravenous administration was monitored using an AC biosusceptometry (ACB) assay. We assessed the biodistribution profile based on the ACB images obtained through selected regions of interest (ROIs) in the heart and liver position according to the anatomical references previously selected. The signals obtained allowed for the quantification of pharmacokinetic parameters, indicating that the uptake of hepatic MNPs is compromised during liver cirrhosis, since scar tissue reduces blood flow through the liver and slows its processing function. Since liver monocytes/macrophages remained constant during the cirrhotic stage, the increased intrahepatic vascular resistance associated with impaired hepatic sinusoidal circulation was considered the potential reason for the change in the distribution of MNPs.

PREVALENCE OF POTENTIAL MRI-BASED EXCLUSIONARY FINDINGS IN OSTEOARTHRITIS CLINICAL TRIALS: DATA FROM THE OSTEOARTHRITIS INITIATIVE (OAI)

PREVALENCE OF POTENTIAL MRI-BASED EXCLUSIONARY FINDINGS IN OSTEOARTHRITIS CLINICAL TRIALS: DATA FROM THE OSTEOARTHRITIS INITIATIVE (OAI)

Detection and Segmentation of Mature Green Tomatoes Based on Mask R-CNN with Automatic Image Acquisition Approach.

Since the mature green tomatoes have color similar to branches and leaves, some are shaded by branches and leaves, and overlapped by other tomatoes, the accurate detection and location of these tomatoes is rather difficult. This paper proposes to use the Mask R-CNN algorithm for the detection and segmentation of mature green tomatoes. A mobile robot is designed to collect images round-the-clock and with different conditions in the whole greenhouse, thus, to make sure the captured dataset are not only objects with the interest of users. After the training process, RestNet50-FPN is selected as the backbone network. Then, the feature map is trained through the region proposal network to generate the region of interest (ROI), and the ROIAlign bilinear interpolation is used to calculate the target region, such that the corresponding region in the feature map is pooled to a fixed size based on the position coordinates of the preselection box. Finally, the detection and segmentation of mature green tomatoes is realized by the parallel actions of ROI target categories, bounding box regression and mask. When the Intersection over Union is equal to 0.5, the performance of the trained model is the best. The experimental results show that the F1-Score of bounding box and mask region all achieve 92.0%. The image acquisition processes are fully unobservable, without any user preselection, which are a highly heterogenic mix, the selected Mask R-CNN algorithm could also accurately detect mature green tomatoes. The performance of this proposed model in a real greenhouse harvesting environment is also evaluated, thus facilitating the direct application in a tomato harvesting robot.

Proposed Smart Monitoring System for the Detection of Bee Swarming

This paper presents a bee-condition-monitoring system incorporated with a deep-learning process to detect bee swarming. This system includes easy-to-use image acquisition and various end node approaches for either on-site or cloud-based mechanisms. This system also incorporates a new smart CNN engine called Swarm-engine for detecting bees and the issue of notifications in cases of bee swarming conditions to the apiarists. First, this paper presents the authors’ proposed implementation system architecture and end node versions that put it to the test. Then, several pre-trained networks of the authors’ proposed CNN Swarm-engine were also validated to detect bee-clustering events that may lead to swarming. Finally, their accuracy and performance towards detection were evaluated using both cloud cores and embedded ARM devices on parts of the system’s different end-node implementations.

X-ray bi-prism interferometry-A design study of proposed novel hardware.

Advances in X-ray phase-contrast imaging can obtain excellent soft-tissue contrast of phase-shift, attenuation, and small-angle scatter. Here, we present fringe patterns for different design parameters of X-ray bi-prism interferometry imaging systems. Our aim is to develop bi-prism interferometry imaging systems with excellent polychromatic performance that produce high-contrast fringes with spatially incoherent X-ray illumination. We also introduce a novel X-ray tube design that uses temporal multiplexing to provide simultaneous virtual "electronic phase stepping" that replace "mechanical phase stepping" popular with grating-based interferometry setups. In our investigation, we develop expressions for the irradiance distribution pattern of a bi-prism interferometer composed of multiple point sources and multiple bi-prisms. These expressions are used to plot fringe patterns for X-ray design parameters, including size of point source, number of point sources, and point source separation, and bi-prism design parameters including material, angle, number of bi-prisms, period, and bi-prism array to X-ray source and detector distances. Results show that the fringe patterns for a bi-prism interferometry system are not longitudinally periodic as with grating interferometers that produce a Talbot-Lau carpet. It is also shown that at 59keV X-rays the bi-prism material should be something comparable to nickel to obtain reasonable fringe visibility. The irradiance distribution pattern demonstrates that bi-prism interferometry may provide comparable or improved fringe visibility to that of gratings. Special care is given to present our findings within the context of previous advancements. A single-shot image acquisition approach using a temporal multiplexed, high-power X-ray source provides virtual electronic phase stepping without focal spot sweeping. This provides multiple images, each at the same exposure and the same projection view, from different fringe locations that allow one to derive the attenuation, phase, and dark-field images of the sample without mechanical phase stepping of a grating.

A Low-Cost Contactless Overhead Micrometer Surface Scanner

The design and implementation of a contactless scanner and its software are proposed. The scanner regards the photographic digitization of planar and approximately planar surfaces and is proposed as a cost-efficient alternative to off-the-shelf solutions. The result is 19.8 Kppi micrometer scans, in the service of several applications. Accurate surface mosaics are obtained based on a novel image acquisition and image registration approach that actively seeks registration cues by acquiring auxiliary images and fusing proprioceptive data in correspondence and registration tasks. The device and operating software are explained, provided as an open prototype, and evaluated qualitatively and quantitatively.

Standardized User-Independent Confocal Microscopy Image Acquisition and Analysis for Thickness Measurements of Microscale Collagen Scaffolds.

The ability to accurately and precisely measure the thickness of biomaterial constructs is critical for characterizing both specific dimensional features and related mechanical properties. However, in the absence of a standardized approach for thickness measurements, a variety of imaging modalities have been employed, which have been associated with varying limits of accuracy, particularly for ultrathin hydrated structures. Electron microscopy (EM), a commonly used modality, yields thickness values for extensively processed and nonhydrated constructs, potentially resulting in overestimated mechanical properties, including elastic modulus and ultimate tensile strength. Confocal laser scanning microscopy (CLSM) has often been used as a nondestructive imaging alternative. However, published CLSM-derived image analysis protocols use arbitrary signal intensity cutoffs and provide minimal information regarding thickness variability across imaged surfaces. To address the aforementioned limitations, we present a standardized, user-independent CLSM image acquisition and analysis approach developed as a custom ImageJ macro and validated with collagen-based scaffolds. In the process, we also quantify thickness discrepancies in collagen-based scaffolds between CLSM and EM techniques, further illustrating the need for improved strategies. Employing the same image acquisition protocol, we also demonstrate that this approach can be used to estimate the surface roughness of the same scaffolds without the use of specialized instrumentation.

Digital colorimetric analysis for estimation of iron in water with smartphone-assisted microfluidic paper-based analytical devices

ABSTRACT Microfluidic paper-based analytical devices are emerging as promising options for on-the-spot detection of chemical contaminants in water. The coupling of these devices with digital imaging technology has attracted immense interest in developing portable sensor applications. However, the potential of digital techniques still remains to be fully explored. In this work, we integrate digital imaging with microfluidic paper-based analytical devices to develop a portable colorimetric assay for iron. The experimental conditions are optimised using image analysis and the effects of imaging equipment and colorimetric analysis methods on the readout of devices are studied using numerical, graphical and statistical techniques. The experimental results are obtained for the estimation of iron contamination in water samples using a colorimetric assay based on iron (III)-thiocyanate reaction. The digital image acquisition approach using smartphone as imaging equipment is adopted for on-the-spot colorimetric data collection. Image processing techniques are employed to estimate the iron concentration in water from digital images. Greyscale colour intensity obtained from a digital image is identified as a readout of the analytical device and employed for quantitative assessment. The limit of detection for iron is estimated to be 0.26 ppm. The microfluidic approach offers good recovery and repeatability with relative standard deviation of 1.4%. The maximum percentage difference for intensities between the image analysis methods is 4.94% and that between the imaging equipment is 9.96%. The gradients of regression lines for pairwise equipment and method outputs with zero intercept range from 0.9931 to 1.0162 with high values of coefficients of determination. The correlation coefficient values for various paired comparisons (>0.99) suggest strong agreement. Statistical test results suggest no significant differences between methods and between imaging equipment (significance level 0.05). The findings suggest the efficacy of digital imaging and colorimetric analysis for paper-based analytical devices.

High-Throughput Phenotyping of Morphological Seed and Fruit Characteristics Using X-Ray Computed Tomography.

Traditional seed and fruit phenotyping are mainly accomplished by manual measurement or extraction of morphological properties from two-dimensional images. These methods are not only in low-throughput but also unable to collect their three-dimensional (3D) characteristics and internal morphology. X-ray computed tomography (CT) scanning, which provides a convenient means of non-destructively recording the external and internal 3D structures of seeds and fruits, offers a potential to overcome these limitations. However, the current CT equipment cannot be adopted to scan seeds and fruits with high throughput. And there is no specialized software for automatic extraction of phenotypes from CT images. Here, we introduced a high-throughput image acquisition approach by mounting a specially designed seed-fruit container onto the scanning bed. The corresponding 3D image analysis software, 3DPheno-Seed&Fruit, was created for automatic segmentation and rapid quantification of eight morphological phenotypes of internal and external compartments of seeds and fruits. 3DPheno-Seed&Fruit is a graphical user interface design and user-friendly software with an excellent phenotype result visualization function. We described the software in detail and benchmarked it based upon CT image analyses in seeds of soybean, wheat, peanut, pine nut, pistachio nut and dwarf Russian almond fruit. R2 values between the extracted and manual measurements of seed length, width, thickness, and radius ranged from 0.80 to 0.96 for soybean and wheat. High correlations were found between the 2D (length, width, thickness, and radius) and 3D (volume and surface area) phenotypes for soybean. Overall, our methods provide robust and novel tools for phenotyping the morphological seed and fruit traits of various plant species, which could benefit crop breeding and functional genomics.

Biomedical Imaging and Analysis in the Age of Big Data and Deep Learning [Scanning the Issue

Imaging of the human body using a number of different modalities has revolutionized the field of medicine over the past several decades and continues to grow at a rapid pace <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> . More than ever, previously unknown information about biology and disease is being unveiled at a range of spatiotemporal scales. Although results and clinical adoption of strategies related to the computational and quantitative analysis of the images have lagged behind development of image acquisition approaches, there has been a noticeable increase of effort and interest in these areas in recent years <xref ref-type="bibr" rid="ref6" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[6]</xref> . This special issue aims to define and highlight some of the “hot” newer ideas that are in biomedical imaging and analysis, intending to shine a light on where the field might move in the next several decades, and focuses on emphasizing where electrical engineers have been involved and could potentially have the most impact. These areas include image acquisition physics, image/signal processing, and image analysis, including pattern recognition and machine learning. This issue focuses on two themes common in much of this effort: first, engineers and computer scientists have found that the information contained in medical images, when viewed through image-based vector spaces, is generally quite sparse. This observation has been transformative in many ways and is quite pervasive in the articles we include here. Second, medical imaging is one of the largest producers of “big data,” and, data-driven machinelearning techniques (e.g., deep learning) are gaining significant attention because improved performance over previous approaches. Thus, data-driven techniques, e.g., formation via image reconstruction <xref ref-type="bibr" rid="ref11" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[11]</xref> and image analysis via deep learning <xref ref-type="bibr" rid="ref8" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[8]</xref> , <xref ref-type="bibr" rid="ref9" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[9]</xref> , are gaining momentum in their development.

3D dosimetry for proton therapy

We have been developing novel 3-dimensional (3D) detector systems using organic plastic and liquid scintillators to measure and image the dose distribution from proton therapy beams in near-real time. Proof-of-concept and initial feasibility studies using a single charge-coupled device camera have already been conducted. Our recent studies focused on the characterization of scanning proton beams used for patient treatments using a 3D liquid scintillator-based detector system with a set of scientific-complementary metal-oxide-semiconductor (sCMOS) cameras. The basic concept consists of using a large volume of a solid or liquid scintillator to measure or image the dose distributions from proton beams in 3D. We recently developed a large liquid scintillator-based detector system consisting of a 20 × 20 × 20cm transparent acrylic tank filled with a water-equivalent, commercially available liquid scintillator that generates scintillation light when irradiated with protons. To track rapid spatial and dose variations in spot-scanned proton beams, we used 3 high-speed sCMOS cameras to image the scintillation light signals from 3 orthogonal projections in cine mode. Furthermore, we developed a new image acquisition approach that synchronized camera imaging times with dynamic pencil-beam deliveries to efficiently capture the dose and therefore enable accurate dosimetric calculations. This system was fully developed and characterized at the Proton Therapy Center at The University of Texas MD Anderson Cancer Center. We show that such systems can provide fast and accurate measurements of the range, lateral profile, and lateral position of scanning proton beams with excellent spatial resolution (0.21 mm). We also demonstrate that such detectors can rapidly measure proton beam characteristics and intensities at multiple energies, which makes them an ideal tool for scanned proton-beam systems, beam quality assurance studies, and verification of patient treatment delivery.

Mapping of River Terraces with Low-Cost UAS Based Structure-from-Motion Photogrammetry in a Complex Terrain Setting

River terraces are the principal geomorphic features for unraveling tectonics, sea level, and climate conditions during the evolutionary history of a river. The increasing availability of high-resolution topography data generated by low-cost Unmanned Aerial Systems (UAS) and modern photogrammetry offer an opportunity to identify and characterize these features. In this paper, we assessed the capabilities of UAS-based Structure-from-Motion (SfM) photogrammetry, coupled with a river terrace detection algorithm for mapping of river terraces over a 1.9 km2 valley of complex terrain setting, with a focus on the performance of this latest technology over such complex terrains. With the proposed image acquisition approach and SfM photogrammetry, we constructed a 3.8 cm resolution orthomosaic and digital surface model (DSM). The vertical accuracy of DSM was assessed against 196 independent checkpoints measured with a real-time kinematic (RTK) GPS. The results indicated that the root mean square error (RMSE) and mean absolute error (MAE) were 3.1 cm and 2.9 cm, respectively. These encouraging results suggest that this low-cost, logistically simple method can deliver high-quality terrain datasets even in the complex terrain, competitive with those obtained using more expensive laser scanning. A simple algorithm was then employed to detect river terraces from the generated DSM. The results showed that three levels of river terraces and a high-level floodplain were identified. Most of the detected river terraces were confirmed by field observations. Despite the highly erosive nature of fluvial systems, this work obtained good results, allowing fast analysis of fluvial valleys and their comparison. Overall, our results demonstrated that the low-cost UAS-based SfM technique could yield highly accurate ultrahigh-resolution topography data over complex terrain settings, making it particularly suitable for quick and cost-effective mapping of micro to medium-sized geomorphic features under such terrains in remote or poorly accessible areas. Methods discussed in this paper can also be applied to produce highly accurate digital terrain data over large spatial extents for some other places of complex terrains.

Automatic visual inspection of thermoelectric metal pipes

This paper presents the main aspects of the design of an image acquisition and processing approach that can be inserted into thermoelectric metal pipe systems and travel inside the pipes to capture images from the inner surface of such pipes for further analysis. After the image capture, a preprocessing is applied based on iris recognition, which transforms the image from a Cartesian coordinate system to a polar coordinate system, which allows a better texture analysis of the internal surface of the pipe. The extracted information is used to train a classifier capable of automatically identifying segments that present some type of corrosion or defects. The experimental results in a dataset of 6150 images using two textural features have shown that the proposed classification approach can achieve accuracy between 96 and 98% in the test set.

Exposure-fusion-based dot-grid image acquisition and recognition for sheet metal strain analysis.

Dot-grid images are usually captured for grid strain analysis during sheet metal forming. Due to the strong reflective characteristic of the metallic surfaces, the recorded dot-grid images often have poor quality, low positioning accuracy, and low recognition rate. Therefore, an exposure-fusion-based dot-grid image acquisition and recognition approach is proposed. First, multiple dot-grid images are captured at different exposure levels. Subsequently, the recorded multi-exposure dot-grid images are fused into a new high-quality dot-grid image based on exposure fusion technology. Finally, a dot-grid image recognition procedure is developed to detect the dot-grids in the new dot-grid image. Both synthetic and real dot-grid images were tested to verify the performance of the novel approach. When synthetic dot-grid images were tested, the maximum positioning error was up to 6.044 pixels if they were recognized in the traditional way, whereas the maximum positioning error was reduced to 0.132 pixels if the novel approach was adopted. When real dot-grid images were tested, the lowest recognition rate is only 50.52% if they were recognized in the traditional way. Nevertheless, the recognition rate can reach about 91% if the novel approach was employed. These experimental results show the superiorities of the novel approach.

Whole heart detailed and quantitative anatomy, myofibre structure and vasculature from X-ray phase-contrast synchrotron radiation-based micro computed tomography.

While individual cardiac myocytes only have a limited ability to shorten, the heart efficiently pumps a large volume-fraction thanks to a cell organization in a complex 3D fibre structure. Subclinical subtle cardiac structural remodelling is often present before symptoms arise. Understanding and early detection of these subtle changes is crucial for diagnosis and prevention. Additionally, personalized computational modelling requires knowledge on the multi-scale structure of the whole heart and vessels. We developed a rapid acquisition together with visualization and quantification methods of the integrated microstructure of whole in-vitro rodents hearts using synchrotron based X-ray phase-contrast tomography. These images are formed not only by X-ray absorption by the tissue but also by wave propagation phenomena, enhancing structural information, thus allowing to raise tissue contrast to an unprecedented level. We used a (ex-vivo) normal rat heart and fetal rabbit hearts suffering intrauterine growth restriction as a model of subclinical cardiac remodelling to illustrate the strengths and potential of the technique. For comparison, histology and diffusion tensor magnetic resonance imaging was performed. We have developed a novel, high resolution, image acquisition, and quantification approach to study a whole in-vitro heart at myofibre resolution, providing integrated 3D structural information at microscopic level without any need of tissue slicing and processing. This superior imaging approach opens up new possibilities for a systems approach towards analysing cardiac structure and function, providing rapid acquisition of quantitative microstructure of the heart in a near native state.

Evaluating the performance of the newly-launched Landsat 8 sensor in detecting and mapping the spatial configuration of water hyacinth (Eichhornia crassipes) in inland lakes, Zimbabwe

The remote sensing of freshwater resources is increasingly becoming important, due to increased patterns of water use and the current or projected impacts of climate change and the rapid invasion by lethal water weeds. This study therefore sought to explore the potential of the recently-launched Landsat 8 OLI/TIRS sensor in mapping invasive species in inland lakes. Specifically, the study compares the performance of the newly-launched Landsat 8 sensor, with more advanced sensor design and image acquisition approach to the traditional Landsat-7 ETM+ in detecting and mapping the water hyacinth (Eichhornia crassipes) invasive species across Lake Chivero, in Zimbabwe. The analysis of variance test was used to identify windows of spectral separability between water hyacinth and other land cover types. The results showed that portions of the visible (B3), NIR (B4), as well as the shortwave bands (Band 8, 9 and 10) of both Landsat 8 OLI and Landsat 7 ETM, exhibited windows of separability between water hyacinth and other land cover types. It was also observed that on the use of Landsat 8 OLI produced high overall classification accuracy of 72%, when compared Landsat 7 ETM, which yielded lower accuracy of 57%. Water hyacinth had optimal accuracies (i.e. 92%), when compared to other land cover types, based on Landsat 8 OLI data. However, when using Landsat 7 ETM data, classification accuracies of water hyacinth were relatively lower (i.e. 67%), when compared to other land cover types (i.e. water with accuracy of 100%). Spectral curves of the old, intermediate and the young water hyacinth in Lake Chivero based on: (a) Landsat 8 OLI, and (b) Landsat 7 ETM were derived. Overall, the findings of this study underscores the relevance of the new generation multispectral sensors in providing primary data-source required for mapping the spatial distribution, and even configuration of water weeds at lower or no cost over time and space.

SU‐G‐IeP1‐02: Automated SNR Analysis of MR Images Using Model‐Based Noise Determination

Purpose:To develop a robust automated method of signal‐to‐noise (SNR) analysis from MR images using a single image acquisition approach and model‐based noise determination.Methods:Three consecutive MRI phantom scans were performed according to the SNR test procedures in the 2015 ACR MRI Quality Control Manual. This was repeated for several different RF coils and on MRI scanners from two different vendors. The three images from each RF coil were analyzed by the single‐image (SNR‐ACR1) and two‐image (SNR‐ACR2) SNR analysis methods also described in the ACR QC Manual. In addition the images were analyzed by the automated method (SNR‐AUTO). The SNR‐AUTO method utilizes an ImageJ macro file that was developed to 1) segment the phantom from the background region by means of a simple histogram‐based thresholding algorithm, 2) measure the signal from a ROI encompassing 80% of the phantom area, and 3) determine noise by fitting the histogram of the background region (devoid of the phase encode direction) to a noise distribution model established in the literature.Results:The accuracy of the SNR‐AUTO analysis method is demonstrated by it yielding SNR results that on average were −0.8% ± 6.7% of the SNR‐ACR1 method and 7.5% ± 8.6% of the SNR‐ACR2 method. The SNR‐AUTO method demonstrated a higher level of reproducibility (average COV = 0.32%) than that of the other two methods (average COV = 1.01% and 1.69% for SNR‐ACR1 and SNRACR2 respectively) both of which require manual placement of ROIs. An additional finding is that the model‐based noise determination is relatively insensitive to the presence of minor artifacts in the background region.Conclusion:The SNR‐AUTO method proved to be an accurate, highly reproducible, time‐efficient method for analysis of SNR in MR images and is insensitive to minor artifacts in the noise‐measuring region.

Bright-Field Imaging and Optical Coherence Tomography of the Mouse Posterior Eye.

Noninvasive live imaging has been used extensively for ocular phenotyping in mouse vision research. Bright-field imaging and optical coherence tomography (OCT) are two methods that are particularly useful for assessing the posterior mouse eye (fundus), including the retina, retinal pigment epithelium, and choroid, and are widely applied due to the commercial availability of sophisticated instruments and software. Here, we provide a guide to using these approaches with an emphasis on post-acquisition image processing using Fiji, a bundled version of the Java-based public domain software ImageJ. A bright-field fundus imaging protocol is described for acquisition of multi-frame videos, followed by image registration to reduce motion artifacts, averaging to reduce noise, shading correction to compensate for uneven illumination, filtering to improve image detail, and rotation to adjust orientation. An OCT imaging protocol is described for acquiring replicate volume scans, with subsequent registration and averaging to yield three-dimensional datasets that show reduced motion artifacts and enhanced detail. The Fiji algorithms used in these protocols are designed for batch processing and are freely available. The image acquisition and processing approaches described here may facilitate quantitative phenotyping of the mouse eye in drug discovery, mutagenesis screening, and the functional cataloging of mouse genes by individual laboratories and large-scale projects, such as the Knockout Mouse Phenotyping Project and International Mouse Phenotyping Consortium.