Image Calibration Research Articles

Radiographic calibration for pubic diastasis assessment in bladder exstrophy-epispadias complex: a phantom study

BackgroundThe assessment of pubic diastasis is important for the surgical planning of patients with bladder exstrophy-epispadias complex. Understanding how the diastasis changes during surgical follow-up may help predict patient morbidity. Radiography can follow diastasis but may be affected by patient and technical imaging factors including body size, imaging protocol, and equipment. Using imaging calibration and anatomic ratios may mitigate differences due to these aspects.ObjectiveUse imaging phantoms to assess the effect of radiographic calibration on measurements of pubic diastasis and an internal anatomic ratio as a child grows.Materials and methodsRadiographic images were obtained of three different sizes of computed tomography phantoms (older child, child, and infant) using three imaging techniques that include the osseous pelvis in children. All phantoms were imaged with abdomen and pelvis techniques. The infant phantom was additionally imaged using a thoracoabdominal technique. These exposures were all repeated with systems from three manufacturers. Linear measurements were made between radiographic markers placed to simulate pubic diastasis and sacral width. A ratio was also created between these distances. Measurements with and without image calibration were made by two pediatric radiologists using rulers placed at the time of image acquisition.ResultsThere was excellent interrater agreement for measurements, ICC >0.99. Anterior distances were more affected by magnification than posterior ones with a significant difference between uncalibrated versus calibrated anterior distances (p=0.04) and not for posterior ones (p=0.65). There was no difference between radiographic equipment manufacturers without or with calibration (p values 0.66 to 0.99). There was a significant difference in simulated pubic distance between thoracoabdominal and abdomen (p=0.04) as well as pelvic (p=0.04) techniques which resolved with calibration, each p=0.6. The ratio between the simulated pubic diastasis and sacral width differed by phantom size (all p<0.01) and imaging technique (p values 0.01 to 0.03) with or without calibration. However, the numerical differences may not be clinically significant.ConclusionImage calibration results in more uniform measurements that are more accurate than uncalibrated ones across patient size, imaging techniques, and equipment. Image calibration is necessary for accurate measurement of inter-pubic distances on all projection imaging. Small differences in the pelvic ratio likely are not clinically significant, but until there is a better understanding, image calibration may be prudent.Graphical abstract

Lizard Body Temperature Acquisition and Lizard Recognition Using Artificial Intelligence.

The acquisition of the body temperature of animals kept in captivity in biology laboratories is crucial for several studies in the field of animal biology. Traditionally, the acquisition process was carried out manually, which does not guarantee much accuracy or consistency in the acquired data and was painful for the animal. The process was then switched to a semi-manual process using a thermal camera, but it still involved manually clicking on each part of the animal's body every 20 s of the video to obtain temperature values, making it a time-consuming, non-automatic, and difficult process. This project aims to automate this acquisition process through the automatic recognition of parts of a lizard's body, reading the temperature in these parts based on a video taken with two cameras simultaneously: an RGB camera and a thermal camera. The first camera detects the location of the lizard's various body parts using artificial intelligence techniques, and the second camera allows reading of the respective temperature of each part. Due to the lack of lizard datasets, either in the biology laboratory or online, a dataset had to be created from scratch, containing the identification of the lizard and six of its body parts. YOLOv5 was used to detect the lizard and its body parts in RGB images, achieving a precision of 90.00% and a recall of 98.80%. After initial calibration, the RGB and thermal camera images are properly localised, making it possible to know the lizard's position, even when the lizard is at the same temperature as its surrounding environment, through a coordinate conversion from the RGB image to the thermal image. The thermal image has a colour temperature scale with the respective maximum and minimum temperature values, which is used to read each pixel of the thermal image, thus allowing the correct temperature to be read in each part of the lizard.

Modeling and compensation of phase error in projection moiré

The phase error caused by the non-sinusoidal characteristics of the projector and reference grating seriously reduces the measurement accuracy of projection moiré, yet relevant research is lacking. This work presents a phase error model and a phase compensation method to address this issue. The model is established by considering the multiple error sources and phase-solving principles. By employing the phase error model difference, the method accurately fits the model parameters from the non-ideal phase error. With these parameters, the phase error is iteratively extracted and eliminated from the measured phase. Due to its comprehensive consideration of the impacting factors, the model can clarify the phase error mechanism. The method not only significantly enhances the measurement accuracy but also requires only relatively few images for calibration. Experimental results demonstrate an improvement in the morphology measurement accuracy by up to 50%.

Spatial calibration of high-density absorption imaging

The accurate determination of atom numbers is an ubiquitous problem in the field of ultracold atoms. For modest atom numbers, absolute calibration techniques are available, however, for large numbers and high densities, the available techniques neglect many-body scattering processes. Here, a spatial calibration technique for time-of-flight absorption images of ultracold atomic clouds is presented. The calibration is obtained from radially averaged absorption images and we provide a practical guide to the calibration process. It is shown that the calibration coefficient scales linearly with optical density and depends on the absorbed photon number for the experimental conditions explored here. This allows for the direct inclusion of a spatially dependent calibration in the image analysis. For typical ultracold atom clouds the spatial calibration technique leads to corrections in the detected atom number up to ≈12% and temperature up to ≈14% in comparison to previous calibration techniques. The technique presented here addresses a major difficulty in absorption imaging of ultracold atomic clouds and prompts further theoretical work to understand the scattering processes in ultracold dense clouds of atoms for accurate atom number calibration.

The PAU survey: photometric calibration of narrow band images

ABSTRACT The physics of the accelerating Universe (PAU) camera is an optical narrow band and broad band imaging instrument mounted at the prime focus of the William Herschel Telescope. We describe the image calibration procedure of the PAU survey data. We rely on an external photometric catalogue to calibrate our narrow band data using stars that have been observed by both data sets. We fit stellar templates to the stellar broad-band photometry of the Sloan Digital Sky Survey and synthesize narrow band photometry that we compare to the PAUS narrow band data to determine their calibration. Consequently, the PAUS data are in the AB system as inherited from its reference calibrator. We do several tests to check the performance of the calibration. We find it self-consistent when comparing repeated observations of the same objects, with a good overall accuracy to the AB system which we estimate to be at the 2 per cent precision level and no significant trends as a function of narrow band filter or wavelength. Repeated observations allow us to build a spatial map of the illumination pattern of the system. We also check the wavelength dependence of the calibration comparing to stellar spectra. We find that using only blue stars reduces the effects of variations in the stellar template fitting to broad-band colours, improving the overall precision of the calibration to around 1 per cent and its wavelength uniformity. The photometric redshift performance obtained with the PAUS data attests to the validity of our calibration to reach the PAUS science goals.

Evaluating Linear Coral Growth Estimation Using Photogrammetry and Alternative Point Cloud Comparison Methods

Abstract. Corals are critical reef-building organisms, providing essential habitat and ecosystem services. Tracking coral growth over time indicates coral reef health, which can be measured using various established techniques. Several coral growth-related studies have successfully applied photogrammetry to a particular coral of various types. While the focus of previous work was on standardised data processing and, to a certain degree, on the assessment of different point cloud comparison methods (Lange et al. 2022), little attention has been given to the impact of camera calibration. This study measured the annual linear extension of five Acropora spp. colonies using photogrammetry and evaluated all stages of imagery processing. A high focus was given to the analysis of the camera calibration method and the validation of camera parameters derived using an in-situ calibration of coral images with scale bars placed in the camera's field of view. We demonstrate that this method is as reliable as the calibration using a calibration frame. This study also examined the impact of the different point cloud comparison methods for Acropora spp. More specifically, the derived point clouds are compared by applying the point-to-point and point-to-model methods and manually selecting 12 coral branch tips. Histograms derived from the comparison methods were analysed and deemed a suitable and efficient alternative approach for measuring the maximum growth rate of mature colonies over shorter time periods (1 year or less).

An improved vision-based tactile skin with imaging adjustment system to reduce defocusing caused by contact depth changes

Imaging robustness of the vision-based tactile sensor (VTS) depends on coating wear resistance and imaging stability. With the miniaturization of sensors, the limited imaging space cannot afford a buffer against imaging deviations caused by contact depth changes. This paper proposes an improved vision-based thin tactile sensor (VTTS, also named vision-based tactile skin), integrating a focus-adjustable camera and an imaging adjustment system (IAS) to improve imaging robustness. The IAS contains an imaging calibration system and a focusing system. Since visuo-tactile sensing is performed in a relatively stable and closed imaging environment, the image-based focusing method is naturally matched with tactile imaging. Accordingly, an imaging calibration system based on a global search is designed to determine the focusing interval and shorten the focusing range. Three-point-fitting-based depth from focus (TPF-DFF) and deep-learning-based depth from defocus (DL-DFD) are proposed for low-frequency (simple)/high-frequency (complex) imaging adjustment. The experimental results demonstrate that the IAS-VTTS has close to 100% adjustment accuracy, and the fastest focusing time is up to 40 ms. Consequently, the IAS-VTTS can adapt to different deformation depths and support high-quality tactile imaging.

Quantitative SPECT/CT imaging of actinium-225 for targeted alpha therapy of glioblastomas

BackgroundA new, alternative option for patients with recurrent glioblastoma is targeted alpha therapy (TAT), in the form of a local administration of substance P (neurokinin type 1 receptor ligand, NK-1) labelled with 225Ac. The purpose of the study was to confirm the feasibility of quantitative SPECT imaging of 225Ac, in a model reproducing specific conditions of TAT. In particular, to present the SPECT calibration methodology used, as well as the results of validation measurements and their accuracy. Additionally, to discuss the specific problems related to high noise in the presented case.Materials and methodsAll SPECT/CT scans were conducted using the Symbia T6 equipped with HE collimators, and acquired with multiple energy windows (three main windows: 440 keV, 218 keV, and 78 keV, with three lower scatter energy windows). A Jaszczak phantom with fillable cylindrical sources of various sizes was used to investigate quantitative SPECT/CT imaging characteristics. The planar sensitivity of the camera, an imaging calibration factor, and recovery coefficients were determined. Additionally, the 3D printed model of the glioblastoma tumour was developed and imaged to evaluate the accuracy of the proposed protocol.ResultsUsing the imaging calibration factor and recovery coefficients obtained with the Jaszczak phantom, we were able to quantify the activity in a 3D-printed model of a glioblastoma tumour with uncertainty of no more than 10% and satisfying accuracy.ConclusionsIt is feasible to perform quantitative 225Ac SPECT/CT imaging. However, there are still many more challenges that should be considered for further research on this topic (among others: accurate determination of ICF in the case of high background noise, better method of background estimation for recovery coefficient calculations, other methods for scatter correction than the dual-energy window scatter-compensation method used in this study).

Accelerated 3D multi-echo spin-echo sequence with a subspace constrained reconstruction for whole mouse brain mapping.

To accelerate whole-brain quantitative mapping in preclinical imaging setting. A three-dimensional (3D) multi-echo spin echo sequence was highly undersampled with a variable density Poisson distribution to reduce the acquisition time. Advanced iterative reconstruction based on linear subspace constraints was employed to recover high-quality raw images. Different subspaces, generated using exponential or extended-phase graph (EPG) simulations or from low-resolution calibration images, were compared. The subspace dimension was investigated in terms of precision. The method was validated on a phantom containing a wide range of and was then applied to monitor metastasis growth in the mouse brain at 4.7T. Image quality and estimation were assessed for 3 acceleration factors (6/8/10). The EPG-based dictionary gave robust estimations of a large range of . A subspace dimension of 6 was the best compromise between precision and image quality. Combining the subspace constrained reconstruction with a highly undersampled dataset enabled the acquisition of whole-brain maps, the detection and the monitoring of metastasis growth of less than 500 . Subspace-based reconstruction is suitable for 3D mapping. This method can be used to reach an acceleration factor up to 8, corresponding to an acquisition time of 25 min for an isotropic 3D acquisition of 156 m on the mouse brain, used here for monitoring metastases growth.

Spot scanning imaging calibration method based on deviation model for wafer inspection

The spot scanning defect detection system has good detection performance for wafer defects. However, due to the existence of system assembly errors, the wafer defect inspection system could not scan following the planned trace, which resulted in distortion of the reconstructed image and had a greater impact on defect feature extraction and accurate defect location. The system deviation calibration and adjustment can further facilitate the accuracy of the system position and image reconstruction. To address this issue, the causes and shapes of imaging distortion were analyzed and simulated, and a simple and effective calibration method based on a scanning deviation model for the spot scanning defect detection system was proposed. First, a scanning deviation model was established following the characteristics of the spot scanning system, and different degrees of reconstructed image distortion were simulated and analyzed as the system deviation changes. Then, an 8-inch wafer with a grid was used as the calibrator and scanned using the developed spot scanning system. Serveral grid corners in the reconstructed image are selected and used as the calibration feature points. The constraint function is constructed using the equidistant features between adjacent grid points, and then the quasi-Newton method is used to optimize the parameters of the function to obtain the best deviation estimation of the spot scanning system. To prove the validity of the estimated deviation parameters, another patterned wafer was scanned and the distortion image was used for reconstruction. The experiment demonstrated that an accurate and distortion-free wafer image can be reconstructed using the calibrated deviation parameters, and the root mean square error of the measured distance between two points of interest was only 0.61 pixels, which proved the effectiveness of the proposed calibration method.

Multi-camera joint calibration algorithm for guiding machining robot positioning considering ambient light and error uncertainty

Multi-camera guided robotic machining is widely used in the removal of flash and burrs on the complex automotive castings and forgings. The multi-camera calibration process, however, is easily affected by the ambient light and uncertain error, thereby resulting in the undesired robot positioning accuracy. To overcome the challenging problem, this paper proposes a multi-camera joint calibration (MCJC) algorithm by considering both the ambient light and error uncertainty to optimize and compensate the positioning error of the image-guided machining robot. The high dynamic range imaging is used at first to obtain stable calibration image data for countering the effects of ambient light changes. Then the uncertainty error in camera self-calibration process is removed by the confidence threshold filtering. The hand-eye calibration matrix is corrected finally using the deviation feedback optimization for positioning accuracy enhancement of the image-guided machining robot. Taking the automotive flywheel shell as the experimental object, the results demonstrate that the average absolute positioning error is controlled within 1.3 mm, the optimum can reach 0.43 mm, and the average relative positioning error is smaller than 0.5 mm. Compared with the existing state-of-the-art algorithms, the proposed algorithm shows high positioning accuracy and strong algorithm robustness, and can meet the requirements for the subsequent image-guided robotic machining.

Self-Adaptive Colour Calibration of Deep Underwater Images Using FNN and SfM-MVS-Generated Depth Maps

The task of colour restoration on datasets acquired in deep waters with simple equipment such as a camera with strobes is not an easy task. This is due to the lack of a lot of information, such as the water environmental conditions, the geometric setup of the strobes and the camera, and in general, the lack of precisely calibrated setups. It is for these reasons that this study proposes a self-adaptive colour calibration method for underwater (UW) images captured in deep waters with a simple camera and strobe setup. The proposed methodology utilises the scene’s 3D geometry in the form of Structure from Motion and MultiView Stereo (SfM-MVS)-generated depth maps, the well-lit areas of certain images, and a Feedforward Neural Network (FNN) to predict and restore the actual colours of the scene in a UW image dataset.

Distortion Correction and Denoising of Light Sheet Fluorescence Images.

Light Sheet Fluorescence Microscopy (LSFM) has emerged as a valuable tool for neurobiologists, enabling the rapid and high-quality volumetric imaging of mice brains. However, inherent artifacts and distortions introduced during the imaging process necessitate careful enhancement of LSFM images for optimal 3D reconstructions. This work aims to correct images slice by slice before reconstructing 3D volumes. Our approach involves a three-step process: firstly, the implementation of a deblurring algorithm using the work of K. Becker; secondly, an automatic contrast enhancement; and thirdly, the development of a convolutional denoising auto-encoder featuring skip connections to effectively address noise introduced by contrast enhancement, particularly excelling in handling mixed Poisson-Gaussian noise. Additionally, we tackle the challenge of axial distortion in LSFM by introducing an approach based on an auto-encoder trained on bead calibration images. The proposed pipeline demonstrates a complete solution, presenting promising results that surpass existing methods in denoising LSFM images. These advancements hold potential to significantly improve the interpretation of biological data.

CROP HEIGHT ESTIMATION OF WHEAT USING SENTINEL-1 SATELLITE IMAGERY: PRELIMINARY RESULTS

Abstract. Wheat is one of the primary crop productions for half of the global production. It is the most exported cereal, which reached up to 40% according to the records of the Food and Agriculture Organization of the United Nations (FAO) in 2020. The relationship between the crop parameters and remote sensing tools has become essential for the temporal monitoring and estimations of crop growth. In this study, we investigate the relationship between crop height and Synthetic Aperture Radar (SAR) backscatter. Plant height was collected twice, in the early (13 April 2023) and late stages (21 June 2023) of wheat, for a total of 70 samples. Sentinel-1 SAR data was also attempted to be synchronized with ground measurements. For this purpose, 8 images were obtained, 4 in both ascending (8 and 15 April, 16 and 26 June) and descending (9 and 14 April, 20 and 25 June) directions. The basic steps of image pre-processing, calibration, filtering, and topography correction, were applied to each image. By evaluating the correlation coefficients between plant height and images, data with low correlations were excluded and the first three data, dated 8 April and 19 June, were used for plant height estimation. For prediction purposes, Linear Regression (LR) and Random Forest (RF) methods were evaluated with three different training data sets. The highest correlation and minimum error value were achieved by VH polarization with random forest (r = 0.868, RMSE = 5.377 cm) in the early stage and LR (r = 0.616, RMSE = 14.451 cm) in the late stage. In general, higher training (80%) and lower test data (20%) produced better results.

Temperature-Automated Calibration Methods for a Large-Area Blackbody Radiation Source.

High-precision temperature control of large-area blackbodies has a pivotal role in temperature calibration and thermal imaging correction. Meanwhile, it is necessary to correct the temperature difference between the radiating (surface of use) and back surfaces (where the temperature sensor is installed) of the blackbody during the testing phase. Moreover, large-area blackbodies are usually composed of multiple temperature control channels, and manual correction in this scenario is error-prone and inefficient. At present, there is no method that can achieve temperature-automated calibration for a large-area blackbody radiation source. Therefore, this article is dedicated to achieving temperature-automated calibration for a large-area blackbody radiation source. First, utilizing two calibrated infrared thermometers, the optimal temperature measurement location was determined using a focusing algorithm. Then, a three-axis movement system was used to obtain the true temperature at the same measurement location on a large-area blackbody surface from different channels. This temperature was subtracted from the blackbody's back surface. The temperature difference was calculated employing a weighted algorithm to derive the parameters for calibration. Finally, regarding experimental verification, the consistency error of the temperature measurement point was reduced by 85.4%, the temperature uniformity of the surface source was improved by 40.4%, and the average temperature measurement deviation decreased by 43.8%. In addition, this system demonstrated the characteristics of strong environmental adaptability that was able to perform temperature calibration under the working conditions of a blackbody surface temperature from 100 K to 573 K, which decreased the calibration time by 9.82 times.

Experimental verification and rapid estimation of uncalibrated cable force via video-based and vibration-based measurements.

The stayed-cable is an important component of cable-stayed bridges, with cable force being a focal point during construction and bridge operation. The advancement of camera and image processing technology has facilitated the integration of computer vision technology in structural inspection and monitoring. This paper focuses on enhancing cable force measurement methods and addressing the limitations of traditional testing techniques by conducting experimental research on cable force estimation using video recording. The proposed approach involves capturing video footage of the target on the cable with a smartphone. Subsequently, a combination of techniques such as the background subtraction method, image morphology processing, and Hough transform image processing technology are employed to detect the precise center coordinates and ultimately obtain the accurate displacement-time curve of the cable's vibration. In addition, the graphic Circularity Coefficient (CC) has been introduced to assess its effectiveness in post-motion-blur image processing for circular targets. The fundamental frequency of the cable is determined by the fast Fourier transformation, and the relationship between the cable force and the fundamental frequency is used to estimate the cable force. The experimental results are compared with data from accelerometers and force gauges, demonstrating that the frequency measurement error is below 1.2% and the cable force test error is less than 3%. In the process of acquiring the cable's fundamental frequency, the test directly employs the pixel as the displacement unit, eliminating the need for image calibration. The innovative use of the CC in processing motion-blurred targets ensured accurate recognition of target coordinates. The experimental findings highlight the method's simplicity, speed, and accuracy.

Quantitative pharmacokinetic and biodistribution studies for fluorescent imaging agents.

Pharmacokinetics and biodistribution studies are essential for characterizing fluorescent agents in vivo. However, few simple methods based on fluorescence imaging are available that account for tissue optical properties and sample volume differences. We describe a method for simultaneously quantifying mean fluorescence intensity of whole blood and homogenized tissues in glass capillary tubes for two fluorescent agents, ABY-029 and IRDye 680LT, using wide-field imaging and tissue-specific calibration curves. All calibration curves demonstrated a high degree of linearity with mean R2 = 0.99 ± 0.01 and RMSE = 0.12 ± 0.04. However, differences between linear regressions indicate that tissue-specific calibration curves are required for accurate concentration recovery. The lower limit of quantification (LLOQ) for all samples tested was determined to be < 0.3 nM for ABY-029 and < 0.4 nM for IRDye 680LT.

Managing the remediation strategy of contaminated megasites using field-scale calibration of geo-electrical imaging with chemical monitoring

Groundwater contamination is a threat to drinking water resources and ecosystems. Remediation by injection of chemical reagents into the aquifer may be preferred to excavation to reduce cost and environmental footprint. Yet, successful remediation requires complete contact between contamination and reagents. Subsurface heterogeneities are often responsible for diffusion into low-permeable zones, which may inhibit this contact. Monitoring the spatial distribution of injected reagents over time is crucial to achieve complete interaction. Source zone contamination at megasites is particularly challenging to remediate and monitor due to the massive scale and mixture of contaminants. Source zone remediation at Kærgård Plantation megasite (Denmark) is monitored here, with a new methodology, using high-resolution cross-borehole electrical resistivity tomography (XB-ERT) imaging calibrated by chemical analyses on groundwater samples. At this site, high levels of toxic non-aqueous phase liquids (NAPL) are targeted by in-situ chemical oxidation using activated persulfate. It may take numerous injection points with extensive injection campaigns to distribute reagents, which requires an understanding of how reagent may transport within the aquifer. A geophysical (XB-ERT) monitoring network of unprecedented size was installed to identify untreated zones and help manage the remediation strategy. The combination of spatially continuous geophysical information with discrete but precise chemical information, allowed detailed monitoring of sulfate distribution, produced during persulfate activation. Untreated zones identified in the first remediation campaign were resolved in the second campaign. The monitoring allowed adjusting the number of injection screens and the injection strategy from one campaign to the next, which resulted in better persulfate distribution and contaminant degradation in the second campaign. Furthermore, geophysical transects repeated over the timespan of a remediation campaign allowed high-resolution time-lapse imaging of reagent transport, which could in the future improve the predictability of transport models, compared to only using on a-priori assumptions of the hydraulic conductivity field.

Two-edge-resolved three-dimensional non-line-of-sight imaging with an ordinary camera

We introduce an approach for three-dimensional full-colour non-line-of-sight imaging with an ordinary camera that relies on a complementary combination of a new measurement acquisition strategy, scene representation model, and tailored reconstruction method. From an ordinary photograph of a matte line-of-sight surface illuminated by the hidden scene, our approach reconstructs a three-dimensional image of the scene hidden behind an occluding structure by exploiting two orthogonal edges of the structure for transverse resolution along azimuth and elevation angles and an information orthogonal scene representation for accurate range resolution. Prior demonstrations beyond two-dimensional reconstructions used expensive, specialized optical systems to gather information about the hidden scene. Here, we achieve accurate three-dimensional imaging using inexpensive, and ubiquitous hardware, without requiring a calibration image. Thus, our system may find use in indoor situations like reconnaissance and search-and-rescue.

A phase-angle inspired calibration strategy based on MEMS projector for 3D reconstruction with markedly reduced calibration images and parameters

3D reconstruction system based on MEMS (micro-electro-mechanical system) mirror has an advantage of small-size and low-cost. However, those existing calibration methods for such systems generally require many calibration images and parameters, which is time-consuming. To address this issue, we propose a phase-angle inspired calibration strategy with reduced calibration images and parameters. This method selects samples of isophase planes by sampling and coordinate transformation, and establishes an isophase planes model that will have deflection angle changes with phase changes, considering possible misalignment deviations. During reconstruction, it determines the equations of the current isophase plane and the back-projection rays to complete the geometric constraints. The experiments verified that the proposed method achieved planeness error of <0.06 mm and sphere error of <0.05 mm with only two calibration positions.