Automated Image Analysis Techniques Research Articles

Microvascular Metrics on Diabetic Retinopathy Severity: Analysis of Diabetic Eye Images from Real-World Data

Objective: To quantify microvascular lesions in a large real-world data (RWD) set, based on single central retinal fundus images of diabetic eyes from different origins, with the aim of validating its use as a precision tool for classifying diabetic retinopathy (DR) severity. Design: Retrospective meta-analysis across multiple fundus image datasets. Sample size: The study analyzed 2445 retinal fundus images from diabetic patients across four diverse RWD international datasets, including populations from Spain, India, China and the US. Intervention: The quantification of specific microvascular lesions: microaneurysms (MAs), hemorrhages (Hmas) and hard exudates (HEs) using advanced automated image analysis techniques on central retinal images to validate reliable metrics for DR severity assessment. The images were pre-classified in the DR severity levels as defined by the International Clinical Diabetic Retinopathy (ICDR) scale. Main Outcome Measures: The primary variables measured were the number of MAs, Hmas, red lesions (RLs) and HEs. These counts were related with DR severity levels using statistical methods to validate the relationship between lesion counts and disease severity. Results: The analysis revealed a robust and statistically significant increase (p < 0.001) in the number of microvascular lesions and the DR severity across all datasets. Tight data distributions were reported for MAs, Hmas and RLs, supporting the reliability of lesion quantification for accurately assessing DR severity. HEs also followed a similar pattern, but with a broader dispersion of data. Data used in this study are consistent with the definition of the DR severity levels established by the ICDR guidelines. Conclusions: The statistically significant increase in the number of microvascular lesions across DR severity validate the use of lesion quantification in a single central retinal field as a key biomarker for disease classification and assessment. This quantification method demonstrates an improvement over traditional assessment scales, providing a quantitative microvascular metric that enhances the precision of disease classification and patient monitoring. The inclusion of a numerical component allows for the detection of subtle variations within the same severity level, offering a deeper understanding of disease progression. The consistency of results across diverse datasets not only confirms the method’s reliability but also its applicability in a global healthcare setting.

Development of a novel automated algorithm for patient dosimetry in computed tomography: a step towards the facilitation of size-specific dose estimates and organs dosimetry estimations in a busy clinical workflow

Abstract Accurate dosimetry in computed tomography (CT) is essential for patient safety and effective radiation management. This study presents the development of an automated algorithm designed to enhance patient dosimetry by facilitating size-specific dose estimates (SSDE) and organ dose estimations. Utilizing a Python-based script, the proposed method integrates advanced image preprocessing, contour detection, and mathematical calculations to quantify key metrics from CT images. This automated approach addresses the limitations of manual measurement techniques. A retrospective analysis was conducted on CT axial images from examinations acquired with an 80-detector scanner. The algorithm processes DICOM images, converts pixel values to Hounsfield Units, applies Gaussian smoothing, windowing, and thresholding, followed by morphological operations to refine segmentation. It measures the water equivalent diameter (Dw) and estimates both region SSDE and organ doses, incorporating tissue attenuation. Validation was performed using an adult anthropomorphic ATOM phantom, with organ doses measured by optically stimulated luminescence dosimeters. The results demonstrated the algorithm’s potential in estimating SSDE and organ doses. Validation of the automated method revealed strong correlations for Dw and SSDE between the proposed method and manual measurements of five expert reviewers ranging from 0.86 to 0.99 for determination coefficient. Comparative analysis of organ doses showed close agreement between results from experimental setup against the proposed algorithm. The automated algorithm estimated brain dose with a mean of 21.8 mGy, while measurements from the ATOM phantom and CT Expo indicated 19.74 mGy and 23.05 mGy, respectively. For lung doses, the automated algorithm estimated 12.5 mGy compared to 11.0 mGy from the ATOM phantom and 13.1 mGy from CT Expo. Liver doses were measured at 12.7 mGy by the automated method, versus 12.1 mGy from the ATOM phantom and 11.1 mGy from CT Expo. This study shows the potential of automated image analysis techniques in enhancing dosimetry accuracy in CT examinations.

Detection and counting of Leishmania intracellular parasites in microscopy images.

Leishmaniasis is a disease caused by protozoan parasites of the genus Leishmania and has a high prevalence and impact on global health. Currently, the available drugs for its treatment have drawbacks, such as high toxicity, resistance of the parasite, and high cost. Therefore, the search for new, more effective, and safe drugs is a priority. The effectiveness of an anti-leishmanial drug is analyzed through in vitro studies in which a technician manually counts the intracellular form of the parasite (amastigote) within macrophages, which is slow, laborious, and prone to errors. To develop a computational system that facilitates the detection and counting of amastigotes in microscopy images obtained from in vitro studies using image processing techniques. Segmentation of objects in the microscope image that might be Leishmania amastigotes was performed using the multilevel Otsu method on the saturation component of the hue, saturation, and intensity color model. In addition, morphological operations and the watershed transform combined with the weighted external distance transform were used to separate clustered objects. Then positive (amastigote) objects were detected (and consequently counted) using a classifier algorithm, the selection of which as well as the definition of the features to be used were also part of this research. MATLAB was used for the development of the system. The results were evaluated in terms of sensitivity, precision, and the F-measure and suggested a favorable effectiveness of the proposed method. This system can help researchers by allowing large volumes of images of amastigotes to be counted using an automatic image analysis technique.

A deep-learning automated image recognition method for measuring pore patterns in closely related bolivinids and calibration for quantitative nitrate paleo-reconstructions

Eutrophication is accelerating the recent expansion of oxygen-depleted coastal marine environments. Several bolivinid foraminifera are abundant in these oxygen-depleted settings, and take up nitrate through the pores in their shells for denitrification. This makes their pore density a possible nitrate proxy. This study documents three aspects related to the porosity of bolivinids. 1. A new automated image analysis technique to determine the number of pores in bolivinids is tested. 2. The pore patterns of Bolivina spissa from five different ocean settings are analysed. The relationship between porosity, pore density and mean pore size significantly differs between the studied locations. Their porosity is mainly controlled by the size of the pores at the Gulf of Guayaquil (Peru), but by the number of pores at other studied locations. This might be related to the presence of a different cryptic Bolivina species in the Gulf of Guayaquil. 3. The pore densities of closely related bolivinids in core-top samples are calibrated as a bottom-water nitrate proxy. Bolivina spissa and Bolivina subadvena showed the same correlation between pore density and bottom-water nitrate concentrations, while the pore density of Bolivina argentea and Bolivina subadvena accumeata is much higher.

T1- Weighted MRI Image Segmentation

Growing evidence in recent years indicates that interest in the development of automated image analysis techniques for medical imaging, especially with regard to the discipline of magnetic resonance imaging. T1-weighted MRI scans are often used for both diagnosis and monitoring various neurological disorders, making accurate segmentation of these images crucial for effective treatment planning. In this work, we offer a new method for T1-weighted MRI image segmentation using patch densenet, an image segmentation-specific deep learning architecture. Our method aims to improve the accuracy and efficiency of segmentation, while also addressing some of the challenges associated with traditional segmentation methods. Traditional segmentation methods typically rely on features that are handcrafted and may struggle to accurately capture the intricate details present in MRI images. By utilizing patch densenet, our method automatically learn and extract relevant features from the T1-weighted MRI images and further enhance the accuracy and specificity of the segmentation results. Ultimately, we believe that our proposed approach can greatly improve diagnosis and treatment planning process for neurological disorders.

Calibration phantom-based prediction of CT lung nodule volume measurement performance.

A calibration phantom-based method has been developed for predicting small lung nodule volume measurement bias and precision that is specific to a particular computed tomography (CT) scanner and acquisition protocol. The approach involves CT scanning a simple reference object with a specific acquisition protocol, analyzing the scan to estimate the fundamental imaging properties of the CT acquisition system, generating numerous simulated images of a target geometry using the fundamental imaging properties, measuring the simulated images with a standard nodule volume segmentation algorithm, and calculating bias and precision performance statistics from the resulting volume measurements. We evaluated the ability of this approach to predict volume measurement bias and precision of Teflon spheres (diameters =4.76, 6.36, and 7.94 mm) placed within an anthropomorphic chest phantom when using 3M Scotch Magic™ tape as the reference object. CT scanning of the spheres was performed with 0.625, 1.25, and 2.5 mm slice thickness and spacing. The study demonstrated good agreement between predicted volumetric performance and observed volume measurement performance for both volumetric measurement bias and precision. The predicted and observed volume mean for all slice thicknesses was found to be 28% and 13% lower on average than the manufactured sphere volume, respectively. When restricted to 0.625 and 1.25 mm slice thickness scans, which are recommended for small lung nodule volume measurement, we found that the difference between predicted and observed volume coefficient of variation was less than 1.0 %. The approach also showed a resilience to varying CT image acquisition protocols, a critical capability when deploying in a real-world clinical setting. This is the first report of a calibration phantom-based method's ability to predict both small lung nodule volume measurement bias and precision. Volume measurement bias and precision for small lung nodules can be predicted using simple low-cost reference objects to estimate fundamental CT image characteristics and modeling and simulation techniques. The approach demonstrates an improved method for predicting task specific, clinically relevant measurement performance using advanced and fully automated image analysis techniques and low-cost reference objects.

Whole-Slide Cytometric Feature Mapping for Distinguishing Tumor Genomic Subtypes in Head and Neck Squamous Cell Carcinoma Whole-Slide Images

Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous disease where, in advanced stages, clinical and pathologic stages do not correlate with outcome. Molecular and genomic biomarkers for HNSCC classification have shown promise for prognostic and therapeutic applications. This study utilized automated image analysis techniques in whole-slide images of HNSCC tumors to identify relationships between cytometric features and genomic phenotypes. Hematoxylin and eosin-stained slides of HNSCC tumors (N=49) were obtained from The Cancer Imaging Archive, along with accompanying clinical, pathologic, genomic, and proteomic reports. Automated nuclear detection was performed across the entirety of slides, and cytometric feature maps were generated. Forty-one cytometric features were evaluated for associations with tumor grade, tumor stage, tumor subsite, and integrated genomic subtype. Thirty-two features demonstrated significant association with integrated genomic subtype when corrected for multiple comparisons. In particular, the basal subtype was visually distinguishable from the chromosomal instability and immune subtypes based on cytometric feature measurements. No features were significantly associated with tumor grade, stage, or subsite. This study provides preliminary evidence that features derived from tissue pathology slides could provide insights into genomic phenotypes of HNSCC.

A high‐speed unsupervised hardware architecture for rapid diagnosis of COVID‐19

SummaryIn the diagnosis of COVID‐19, investigation, analysis, and automatic counting of blood cell clusters are the most essential steps. Currently employed methods for cell segmentation, identification, and counting are time‐consuming and sometimes performed manually from sampled blood smears, which is hard and needs the support of an expert laboratory technician. The conventional method for the blood‐count‐test is by automatic hematology analyzer which is quite expensive and slow. Moreover, most of the unsupervised learning techniques currently available presume the medical practitioner to have a prior knowledge regarding the number and action of possible segments within the image before applying recognition. This assumption fails most often as the severity of the disease gets increased like the advanced stages of COVID‐19, lung cancer etc. In this manuscript, a simplified automatic histopathological image analysis technique and its hardware architecture suited for blind segmentation, cell counting, and retrieving the cell parameters like radii, area, and perimeter has been identified not only to speed up but also to ease the process of diagnosis as well as prognosis of COVID‐19. This is achieved by combining three algorithms: the K‐means algorithm, a novel statistical analysis technique‐HIST (histogram separation technique), and an islanding method an improved version of CCA algorithm/blob detection technique. The proposed method is applied to 15 chronic respiratory disease cases of COVID‐19 taken from high profile hospital databases. The output in terms of quantitative parameters like PSNR, SSIM, and qualitative analysis clearly reveals the usefulness of this technique in quick cytological evaluation. The proposed high‐speed and low‐cost architecture gives promising results in terms of performance of 190 MHz clock frequency, which is two times faster than its software implementation.

Integrated approach to analyse benthic images from an autonomous underwater vehicle deployed at Pemba Island, Tanzania

Manual analysis of large amounts of benthic images is time consuming and costly. This challenge has led to the development of automated image analysis techniques such as CoralNet. The technique combines an online repository and machine learning to completely or partially automate classification of benthic images. Here, the integration of Coral Point Count with Excel Extensions (CPCe) and CoralNet is showcased. CPCe was applied to manually annotate images captured by an autonomous underwater vehicle (AUV) deployed at Pemba Island, Tanzania and then to train and build confidence in CoralNet to automatically annotate more images. Further, possible outputs that can be derived from assessing the relationships between the cover of benthic variables and depth are demonstrated.

Automated analysis of platelet microstructures using a feature length orientation space

The ability to measure elongated structures such as platelets and colonies, is an important step in the microstructural analysis of many materials. Widely used techniques and standards require extensive manual interaction making them slow, laborious, difficult to repeat and prone to human error. Automated approaches have been proposed but often fail when analysing complex microstructures. This paper addresses these challenges by proposing a new, automated image analysis technique, to reliably assess platelet microstructure. Tools from Mathematical Morphology are designed to probe the image and map the response onto a new feature-length orientation space (FLOS). This enables automated measurement of key microstructural features such as platelet width, orientation, globular volume fraction, and colony size. The method has a wide field of view, low dependency on input parameters, and does not require prior thresholding, common in other automated analysis techniques. Multiple datasets of complex Titanium alloys were used to evaluate the new techniques which are shown to match measurements from expert materials scientists using recognized standards, while drastically reducing measurement time and ensuring repeatability. The per-pixel measurement style of the technique also allows for the generation of useful colourmaps, that aid further analysis and provide evidence to increase user confidence in the quantitative measurements.

Comparative study on methods for obtaining voids, fibre and resin percentages in composite materials

Manufacturing quality in composite laminates is one of the key aspects to have in consideration when producing aerostructures. For composite structures quality evaluation, an iterative process of qualification of the first items manufactured is often followed. Thus, the manufacturing process parameters can be tuned until achieving an incremented maturity level that ensures the structure mechanical properties present values within the defined quality standards. Volume fraction of voids (pores), resin and fibre is part of the set of properties that must be verified, especially in novel and/or low cost manufacturing processess that can affect the structural behaviour. The ability to obtain results depending on the technique used for testing becomes very important at this point. This study proposes a comparative analysis of the measurement of voids, fibre and resin percentage on carbon fibre reinforced polymers by automated image analysis technique and acid digestion method, varying parameters of the test procedure with the purpose of evaluating their influence in the correct determination of volume fractions. The use of different methods and parameters allows to analyze comparatively the different results obtained and shows advantages and disadvantages of both methods as well as provides valuable information on the best practices for correct test execution following each procedure.

Ice Core Science Meets Computer Vision: Challenges and Perspectives

Polar ice cores play a central role in studies of the earth’s climate system through natural archives. A pressing issue is the analysis of the oldest, highly thinned ice core sections, where the identification of paleoclimate signals is particularly challenging. For this, state-of-the-art imaging by laser-ablation inductively-coupled plasma mass spectrometry (LA-ICP-MS) has the potential to be revolutionary due to its combination of micron-scale 2D chemical information with visual features. However, the quantitative study of record preservation in chemical images raises new questions that call for the expertise of the computer vision community. To illustrate this new inter-disciplinary frontier, we describe a selected set of key questions. One critical task is to assess the paleoclimate significance of single line profiles along the main core axis, which we show is a scale-dependent problem for which advanced image analysis methods are critical. Another important issue is the evaluation of post-depositional layer changes, for which the chemical images provide rich information. Accordingly, the time is ripe to begin an intensified exchange between the two scientific communities of computer vision and ice core science. The collaborative building of a new framework for investigating high-resolution chemical images with automated image analysis techniques will also benefit the already wide-spread application of laser-ablation inductively-coupled plasma mass spectrometry chemical imaging in the geosciences.

Classification of COVID 19 in Chest CT Images using Convolutional Neural Network

The single-celled organisms called a virus are the root cause of many harmful diseases that affects both animals and humans. Coronavirus is one type of virus that is usually found in animals but if transmitted to humans can cause a wide range of respiratory diseases. One such disease caused is the COVID-19 diseases which caused a worldwide epidemic since its origin in 2019. The disease which is said to have originated in China had caused more than 252000 deaths worldwide in a few months. The test for the COVID-19 involves analysing the throat swab sample which may take days if not a week and by the time the results come the infection would have spread. Hence there is a need to improve the testing procedure for COVID-19. In this paper, we have come up with an automated Image Analysis technique to diagnose COVID-19 using the chest Computed Tomography images of the chest that uses a Convolutional Neural Network. This developed method has shown very good accuracy and efficiency in recognizing the COVID-19 infected CT images.

Deep learning-based oriented object detection for in situ image monitoring and analysis: A process analytical technology (PAT) application for taurine crystallization

Image analysis enables the estimation of critical process properties such as crystal size, morphology, and crystallization kinetics. Despite the rich image information, the lack of a robust image analysis technique has been an obstacle to promote its applications. In this work, an automated image analysis technique that combines the state-of-the-art oriented object detection model, S2A-Net, was developed for in situ estimation of the two-dimensional crystal size distribution (CSD) and the crystal counts. The model was trained to detect and classify both crystals and clusters to enable quantification of the extent of agglomeration and exclude unreliable detections. The effectiveness and robustness of extracting size and aspect ratio at various image complexities were verified by comparing with the focused beam reflective measurement (FBRM) and manually analyzed images in the experimental studies for taurine batch cooling crystallization with different seed loadings. An on-line calibration strategy for solute concentration measurement with Raman spectroscopy was introduced to eliminate the dedicated calibration experiments. The secondary nucleation and growth rate kinetics were evaluated from the on-line measurements and validated by numerical simulation. The proposed method provides a novel PAT strategy that enables accurate two-dimensional size measurement and shape characterization for online monitoring and control of a solution crystallization process.

Deep learning architecture using transfer learning for classification of skin lesions

Skin cancer is one of the most dangerous health problems in many countries the development in automatic medical image analysis technique leads to accurate classification of diseases. Deep learning is a recent technology which solves the complexity in diagnosing the skin cancer. Due to high dissimilarity of skin lesion, it is challenging to classify the Image automatically. By using Convolutional Neural Network the classification of skin lesions can be done with high accuracy. The proposed models was developed by transfer learning concept with three popular architectures: inception V3, VGG16 and VGG19. Our models were trained by ISIC dataset that contain 2487 train images and 604 test images of seven skin lesion classes. Our models give the best performance of test data with 74%, 77% and 76% accuracy for Inception V3, VGG16 and VGG19.

Astrocyte regional heterogeneity revealed through machine learning-based glial neuroanatomical assays.

Evaluation of reactive astrogliosis by neuroanatomical assays represents a common experimental outcome for neuroanatomists. The literature demonstrates several conflicting results as to the accuracy of such measures. We posited that the diverging results within the neuroanatomy literature were due to suboptimal analytical workflows in addition to astrocyte regional heterogeneity. We therefore generated an automated segmentation workflow to extract features of glial fibrillary acidic protein (GFAP) and aldehyde dehydrogenase family 1, member L1 (ALDH1L1) labeled astrocytes with and without neuroinflammation. We achieved this by capturing multiplexed immunofluorescent confocal images of mouse brains treated with either vehicle or lipopolysaccharide (LPS) followed by implementation of our workflows. Using classical image analysis techniques focused on pixel intensity only, we were unable to identify differences between vehicle-treated and LPS-treated animals. However, when utilizing machine learning-based algorithms, we were able to (1) accurately predict which objects were derived from GFAP or ALDH1L1-stained images indicating that GFAP and ALDH1L1 highlight distinct morphological aspects of astrocytes, (2) we could predict which neuroanatomical region the segmented GFAP or ALDH1L1 object had been derived from, indicating that morphological features of astrocytes change as a function of neuroanatomical location. (3) We discovered a statistically significant, albeit not highly accurate, prediction of which objects had come from LPS versus vehicle-treated animals, indicating that although features exist capable of distinguishing LPS-treated versus vehicle-treated GFAP and ALDH1L1-segmented objects, that significant overlap between morphologies exists. We further determined that for most classification scenarios, nonlinear models were required for improved treatment class designations. We propose that unbiased automated image analysis techniques coupled with well-validated machine learning tools represent highly useful models capable of providing insights into neuroanatomical assays.

Automated Analysis of Multiparametric Magnetic Resonance Imaging/Magnetic Resonance Elastography Exams for Prediction of Nonalcoholic Steatohepatitis.

Nonalcoholic fatty liver disease (NAFLD) affects 25% of the global population. The standard of diagnosis, biopsy, is invasive and affected by sampling error and inter-reader variability. We hypothesized that widely available rapid MRI techniques could be used to predict nonalcoholic steatohepatitis (NASH) noninvasively by measuring liver stiffness, with magnetic resonance elastography (MRE), and liver fat, with chemical shift-encoded (CSE) MRI. Besides, we validate an automated image analysis technique to maximize the utility of these methods. To implement and test an automated system for analyzing CSE-MRI and MRE data coupled with model-based prediction of NASH. Prospective. Eighty-three patients with suspected NAFLD. A 1.5 T using a flow-compensated motion-encoded gradient echo MRE sequence and a multiecho CSE-MRI sequence. The MRE and CSE-MRI data were analyzed by two readers (5+ and 1 years of experience) and an automated algorithm. A logistic regression model to predict pathology-diagnosed NASH was trained based on stiffness and proton density fat fraction, and the area under the receiver operating characteristic curve (AUROC) was calculated using 10-fold cross validation for models based on both automated and manual measurements. A separate model was trained to predict the NASH severity score (NAS). Pearson's correlation, Bland-Altman, AUROC, C-statistic. The agreement between automated measurements and the more experienced reader (R2 =0.87 for stiffness and R2 =0.99 for proton density fat fraction [PDFF]) was slightly better than the agreement between readers (R2 =0.85 and 0.98). The model for predicting biopsy-diagnosed NASH had an AUROC of 0.87. The NAS-prediction model had a C-statistic of 0.85. We demonstrated a workflow that used a limited MRI acquisition protocol and fully automated analysis to predict NASH with high accuracy. These methods show promise to provide a reliable noninvasive alternative to biopsy for NASH-screening in populations with NAFLD. 2 TECHNICAL EFFICACY STAGE: 2.

Immunohistochemical analysis of oral cancer tissue images using support vector machine

This paper describes an automatic image analysis technique for p53 immunostained tissue sections of oral cancer. The tissue images are segmented using the entropy thresholding and clustered cells are resolved by selectively applying watershed transform. Each cell nuclei of tissue images is classified as positive or negative according to the staining intensity using support vector machine, and then, tissue score is determined as per J-scoring protocol. The performance of the feature and also scoring technique has been evaluated separately by an individual dataset. According to the experimental result, the feature extracted from the blue component has attained the highest classification accuracy of 98.01% with sensitivity and specificity of 98.86% & 94.74% respectively. The outcome of automatic technique based on the blue component has a strong agreement with the manual score. Therefore, automatic tissue scoring has high potential in the field of modern cancer diagnosis and specific therapy design for the patients.

Radiological characterization of gilthead seabream (Sparus aurata) by X-ray computed tomography.

In recent years, the increasing use of fish as new animal models in scientific research and the growth of fish farming (mainly for human consumption) have highlighted the need for advanced technology to deepen our knowledge of fish biology. Hence, the present study was carried out to radiologically analyse the whole body of gilthead seabream (Sparus aurata) specimens using X-ray computed tomography (CT). Images were acquired in an Albira SPECT/PET/CT tri-modal preclinical-scanner. Segmentation, measurements and three-dimensional reconstruction were made using the Carestream Molecular imaging Albira CT system in conjunction with Pmod, AMIDE and Amira software packages. The results showed that the density values of gilthead seabream are in the range -700 to +2500 HU for the whole body. We also determined the density ranges that topographically coincide with the swim bladder, soft tissues, fat, skin and skeleton. This work describes, validates and demonstrates the application of a fully automated image analysis technique to study and quantify fish body composition, whether segmented or as a whole. In addition, the basis for applying this image technique in other in vivo studies is established.

An efficient protocol and data set for automated otolith image analysis

AbstractInformation on fish age constitutes one of the most important biological variables for a fish stock, and an accurate estimation of the age structure of the fish populations is essential for the reliable management of these natural resources. The age of individual cod (Gadus morhua) is determined by manually examining the layered structure of otoliths, a calcium carbonate structure of the inner ear. Image‐based methods to age otoliths have been investigated for over 4 decades with varying results, but recent developments in automatic image analysis techniques are promising. The objective of this paper is to describe a method to efficiently image a manually broken otolith (avoiding the time‐consuming embedding and cross‐sectioning process) and to describe the organization and acquisition of imaged broken otolith images with associated metadata for a collection of north‐east Arctic cod otoliths. A single‐lens reflex camera was used for capturing photographs of the broken otoliths. A total of six images were acquired for each subject, consisting of three images in the first position with three different light exposures and three images in the second position with three different light exposures. This results in a simple and efficient procedure for capturing clear, satisfactory, and reproducible images of broken fish otoliths, and a more straightforward and less labour‐intensive alternative to the commonly used methods that involve embedding and cross‐sectioning of the otolith.