Camera-based Method Research Articles

Error Analysis and Visibility Classification of Camera-Based Visiometer Using SVM under Nonstandard Conditions

A camera-based visiometer is a promising atmospheric visibility measurement tool because it can meet some specific demands such as the need for visibility monitoring in a strong way, whereas traditional instruments, such as forward scatter-type sensors and transmissometers, can hardly be widely utilized due to their high cost. The camera-based method is used to measure visibility by recording the luminance contrast of the objects in an image. However, lacking standard conditions, they can hardly obtain absolute measurements even with blackbody objects. In this paper, the errors caused by nonstandard conditions in camera-based visiometers with two artificial black bodies are analyzed. The results show that the luminance contrasts of the two blackbodies are highly dependent on the environmental radiance distribution. The nonuniform sky illuminance can cause a large error in the blackbody contrast estimations, leading to substantial visibility measurement errors. A method based on a support vector machine (SVM) is proposed to classify the visibility under nonstandard conditions to ensure the reliability of the camera-based visiometer. A classification accuracy of 96.77% was achieved for the data containing images depicting different illumination conditions (e.g., a clear sky, cloudy sky, and overcast). The results show that the classifier based on the SVM is an effective and reliable method to estimate visibility under complex conditions.

IMouse: Augmentative Communication with Patients Having Neuro-Locomotor Disabilities Using Simplified Morse Code

Patients with amyotrophic lateral sclerosis (ALS), also known as Lou Gehrig’s disease, an incurable disease in which motor neurons are selectively destroyed, gradually lose their mobility as organ dysfunction begins, and eventually, patients find it challenging to make even minor movements and simple communications. To communicate with patients with quadriplegia, researchers have focused on movements of the eye, the only moving organ for patients with ALS, and they have investigated the detection of eyeblinks using brainwaves or cameras or other ways to select letters on a screen via eyeball movements based on eye-tracking cameras. However, brainwave-based techniques, which use the electrical signals of eye movements to determine patient’s intentions, are sensitive to noise, often resulting in the inaccurate identification of intent. Alternatively, a camera-based method that uses letter selection detects the movement of eye feature-points, and this method makes it easy to identify a patient’s intentions using a predefined decision-making process. However, it has long processing time and is prone to inaccuracy due to errors in either the Morse code implementation assigned to all alphabets or the sequential selection methods. Therefore, we have proposed iMouse-sMc, a simplified Morse code-based user interface model using an eye mouse for faster and easier communication with such patients. Furthermore, we improved the detection performance of the eye mouse by applying image contrast techniques to enable communication with patients even at night. To verify the excellent performance of the proposed eye mouse for a user interface, we conducted comparative experiments with existing camera-based communication models based on various words. The results revealed that the time of communication was reduced to 83 s and the intention recognition accuracy was improved by ~28.16%. Additionally, even in low-light environments, where existing models are unable to communicate with the patients due to difficulties with eye detection, the proposed model demonstrated its eye detection capability and proved that it can be used universally for communication with patients during the day and at night.

Automatic Post-Stroke Severity Assessment Using Novel Unsupervised Consensus Learning for Wearable and Camera-Based Sensor Datasets

Stroke survivors often suffer from movement impairments that significantly affect their daily activities. The advancements in sensor technology and IoT have provided opportunities to automate the assessment and rehabilitation process for stroke survivors. This paper aims to provide a smart post-stroke severity assessment using AI-driven models. With the absence of labelled data and expert assessment, there is a research gap in providing virtual assessment, especially for unlabeled data. Inspired by the advances in consensus learning, in this paper, we propose a consensus clustering algorithm, PSA-NMF, that combines various clusterings into one united clustering, i.e., cluster consensus, to produce more stable and robust results compared to individual clustering. This paper is the first to investigate severity level using unsupervised learning and trunk displacement features in the frequency domain for post-stroke smart assessment. Two different methods of data collection from the U-limb datasets—the camera-based method (Vicon) and wearable sensor-based technology (Xsens)—were used. The trunk displacement method labelled each cluster based on the compensatory movements that stroke survivors employed for their daily activities. The proposed method uses the position and acceleration data in the frequency domain. Experimental results have demonstrated that the proposed clustering method that uses the post-stroke assessment approach increased the evaluation metrics such as accuracy and F-score. These findings can lead to a more effective and automated stroke rehabilitation process that is suitable for clinical settings, thus improving the quality of life for stroke survivors.

First Demonstration of Compton Camera Used for X-Ray Fluorescence Imaging.

X-ray fluorescence computed tomography (XFCT) is a promising approach used for obtaining the distribution of high-Z elements in the target object. The characteristic energy of X-ray fluorescence (XRF) photons makes XFCT have higher sensitivity and contrast ratio. Conventional XFCT systems usually require mechanical collimators, which leads to a huge loss of incident photons and reduce photon collection efficiency. The Compton camera is an imaging modality that uses electronic collimation to obtain the incident direction of the photons, which brings advantages in the detection area and X-ray photon collection efficiency. Compton cameras can also achieve three-dimensional (3D) imaging with one or several views of scanning without rotation. Therefore, it is a good idea to realize XRF imaging by using Compton cameras, which may provide more potential applications and imaging possibilities, such as handheld XRF imaging or image-guided interventional operation. In this work, we demonstrate the first Compton camera platform which is used for XRF imaging. The proposed X-ray fluorescence Compton camera (XFCC) mainly consists of a conventional X-ray tube (150kVp) and a Timepix3 photon-counting detector (PCD). A PMMA phantom with insertions containing different concentrations of 4%, 6%, 8%, and 10% (weight/volume) Gd solution is scanned by a fan-beam X-ray. Besides, a Compton camera-based X-ray fluorescence imaging reconstruction method (CCFIRM) is developed to solve specific problems in XFCC reconstruction. The reconstruction images and the quantitative analyses of the contrast-to-noise ratio (CNR) are presented. The results indicate that the detectability limit of the proposed XFCC platform is 3.5139 wt.% (CNR =4 ).

Camera-based PHM method in rotating machinery equipment micro-action scenarios

The health operation of rotating machinery guarantees safety of the project. To ensure a good operating environment, current subway equipment inspections frequency is high, resulting in a waste of resources. Small abnormal changes in mechanical equipment will also contribute to the development of mechanical component defects, which will ultimately lead to the failure of the equipment. Therefore, mechanical equipment defects should be detected and diagnosed as soon as possible. Through the use of graphic processing and deep learning, this paper proposes Rmcad Framework with three aspects: condition monitoring, anomaly detection, defect early warning. Using a network algorithm, this paper proposes an improved model that has the characteristics of two-stream and multi-loss functions, which improves the accuracy of detection. Additionally, a defect warning method is constructed to improve the perception ability of equipment before failure occurs and reduce the frequency of frequent maintenance by detecting anomalies according to the degree of opening.

Detection method of rice blast based on 4D light field refocusing depth information fusion

Rice blast is one of the most widely distributed rice diseases in the world. Its rapid transmission and strong harm have seriously affected the rice yield. The traditional detection methods for rice blast are time-consuming and laborious. Therefore, rapid and accurate detection of rice blast in its early stage has become a priority. In this study, a rice blast detection method based on 4D light field refocusing depth information fusion is studied, which realizes the decoding of rice blast light field images, light field refocusing and image detection after refocusing. The four-dimensional light information recorded by the light field is used to quickly lock the best focal plane of the rice blast image. Then analyze the characteristics of rice blast and carry out subsequent image processing. The experimental results show that the average accuracy of the method used in the study is 96.08%, which has a slight advantage over other image detection methods. The average detection time is 12.13 s, which is a big improvement compared to the manual detection method (38.91 s) and the traditional camera-based detection method (16.68 s). The rice blast detection method based on 4D light field refocusing depth information fusion has certain advantages compared with other image detection and artificial detection methods and can be used for the detection of rice blast.

Reliability and applicability of a low-cost, camera-based gait evaluation method for clinical use

Background There is the need for the development of reliable and easy to use in clinical setting gait assessment tools. An open-access video analysis software that administers the calculation of kinematical and spatio-temporal characteristics of human movement is Kinovea® however, its repeatability as a gait analysis tool has not been well addressed. The purpose of the study was to examine the applicability and reliability of an objective, quantitative, low-cost and easy to use in the clinical setting, gait evaluation method, using Kinovea® software. Methods Data collected from 44 healthy subjects recording gait in sagittal and frontal plane using two smartphones. Time consumption of the procedure was captured. Kinovea® software was used to calculate kinematical and spatial parameters. Results Intra- and inter-rater reliability of the video processing as well as intra-rater reliability of the measurement procedure represented good to excellent and there were less random measurement error. There was no measurement error due to random variation for the most of the calculated parameters, except of the pelvis position. Conclusions The results suggest that excepting low accuracy in calculation of pelvis position, gait evaluation using Kinovea® software is objective, quantitative, low-cost, reliable and easy to use in the clinical setting.

Occupants’ Interaction with Building Services: Development of a Camera-Based Method for Detailed Monitoring of Windows, Shadings, And Lights

Occupants’ Interaction with Building Services: Development of a Camera-Based Method for Detailed Monitoring of Windows, Shadings, And Lights

An improved cascade R-CNN and RGB-D camera-based method for dynamic cotton top bud recognition and localization in the field

Cotton top cutting is an indispensable part of cotton planting. Cotton top bud detection and localization are highly challenging tasks because of tiny targets, dense growth and varying illumination. To achieve the automatic cutting of cotton buds in the field, a cotton top bud recognition and location algorithm adapted to a moving platform in the field based on images acquired by a red, green, blue and depth (RGB-D) camera was developed. In this study, an improved Cascade R-CNN network was proposed to detect cotton top bud regions on RGB images, and three-dimensional (3D) coordinates of targets were obtained by combining color images and depth images from RGB-D cameras. The 3D spatial position of the target in the world coordinate system was affected by the time of the cotton top bud recognition algorithm, the forward speed of the mobile platform and the time consumption of the manipulator moving to the target position. A dynamic compensation method of target coordinates in the moving direction was proposed to ensure the identification and positioning accuracy of cotton top buds in the moving process. To verify the effectiveness of the proposed algorithm, cotton top bud recognition and localization experiments were conducted in the field. The average precision with the proposed improved Cascade R-CNN model was 97.5 %, and the FPS was 13.3 frames per second, which was suitable for different period of a day. The average error of the positioning accuracy in the platform forwarding direction was 4.2 mm, 6.6 mm and 9.6 mm at speeds of 0.1 m/s, 0.2 m/s and 0.3 m/s, respectively. All the results demonstrate that the proposed method could be used for robotic cotton top cutting.

RGB camera-based simultaneous measurements of percutaneous arterial oxygen saturation, tissue oxygen saturation, pulse rate, and respiratory rate.

We propose a method to perform simultaneous measurements of percutaneous arterial oxygen saturation (SpO 2), tissue oxygen saturation (StO 2), pulse rate (PR), and respiratory rate (RR) in real-time, using a digital red–green–blue (RGB) camera. Concentrations of oxygenated hemoglobin (C HbO), deoxygenated hemoglobin (C HbR), total hemoglobin (C HbT), and StO 2 were estimated from videos of the human face using a method based on a tissue-like light transport model of the skin. The photoplethysmogram (PPG) signals are extracted from the temporal fluctuations in C HbO, C HbR, and C HbT using a finite impulse response (FIR) filter (low and high cut-off frequencies of 0.7 and 3 Hz, respectively). The PR is calculated from the PPG signal for C HbT. The ratio of pulse wave amplitude for C HbO and that for C HbR are associated with the reference value of SpO 2 measured by a commercially available pulse oximeter, which provides an empirical formula to estimate SpO 2 from videos. The respiration-dependent oscillation in C HbT was extracted from another FIR filter (low and high cut-off frequencies of 0.05 and 0.5 Hz, respectively) and used to calculate the RR. In vivo experiments with human volunteers while varying the fraction of inspired oxygen were performed to evaluate the comparability of the proposed method with commercially available devices. The Bland–Altman analysis showed that the mean bias for PR, RR, SpO 2, and StO 2 were -1.4 (bpm), -1.2(rpm), 0.5 (%), and -3.0 (%), respectively. The precisions for PR, RR, Sp O 2, and StO 2 were ±3.1 (bpm), ±3.5 (rpm), ±4.3 (%), and ±4.8 (%), respectively. The resulting precision and RMSE for StO 2 were pretty close to the clinical accuracy requirement. The accuracy of the RR is considered a little less accurate than clinical requirements. This is the first demonstration of a low-cost RGB camera-based method for contactless simultaneous measurements of the heart rate, percutaneous arterial oxygen saturation, and tissue oxygen saturation in real-time.

Highly Concentrated Solar Flux of Large Fresnel Lens Using CCD Camera-Based Method

Fresnel lens is a kind of lens that can concentrate sunlight up to a level of thousands of suns with small space occupation which is widely used in the research of sunlight concentration and transmission systems via optical fiber. Most studies on the concentrated flux of lenses use experimental methods to measure the flux distribution on the receiver of parabolic trough solar concentrators, solar power towers, and parabolic dish concentrators, while for Fresnel lenses, especially large-aperture Fresnel lenses such as the one in this manuscript, the simulation approach was mostly used. In response to this problem, this study has developed an experimental system for measuring the concentrated flux density of Fresnel lenses. A charge-coupled device (CCD) camera was used to capture the image of spot of large-aperture (968 mm) Fresnel lenses in the CCD camera-based method, and a heat flow meter was used to calibrate the spot brightness image obtained by the CCD camera. Experimental data show that the peak flux of concentrated spot can reach 4.06 MW/m2. This method confirms the simulation results of previous studies that using the rays tracing method, that is, the flux level of the Fresnel lenses can reach 5000 suns. The experimental results demonstrated the CCD camera-based method combined with a heat flow meter is competent in measuring the intensity of flux with a level of 5000 suns.

High speed ultraviolet digital image correlation (UV-DIC) for dynamic strains at extreme temperatures.

Digital Image Correlation (DIC) is a camera-based method of measuring full-field displacements and strains from the surface of a deforming object. It can be applied at any length scale (determined by the lenses) and any time scale (determined by the camera), and because it is non-contacting, it can also be used at temperatures much higher than can be withstood by bonded strain gauges. At extreme temperatures, materials emit light in the form of blackbody radiation, which can saturate the camera sensor. It has previously been shown that the emitted light can be effectively screened by using ultraviolet (UV) cameras, lenses, and filters; however, commercially available UV cameras are relatively slow, which limits the speed of combined UV-DIC measurements. In this study, a UV intensifier was paired with a high-speed camera, and its ability to perform UV-DIC at high temperature and high speed was investigated. The system was compared over three different experiments: (A) a quasi-static thermal expansion test at high temperature, (B) a vibration test at room temperature, and (C) the same vibration test repeated at high temperature. The system successfully performed DIC up to at least 1600 °C at frame rates of 5000fps, which is more than 100 times faster than other examples of UV-DIC in the literature. In all cases, measurements made using the UV intensifier were much noisier than those made without the intensifier, but the intensifier enabled measurements at temperatures well above those which an unfiltered high-speed camera otherwise saturates.

A note of quantitative diagnosis of compound errors in trough concentration system

In practice, there are multiple optical errors existing in parabolic trough concentrator, which respectively have a unique effect on the flux distribution on the wall of the absorber, causing it to deviate from the ideal operating condition. Diagnosis for multiple errors is difficult. The effect of combined optical errors on the flux distribution can be modelled using convolution operations. In this study, the flux patterns under multiple types of optical errors are traversed and combined, and the actual flux distribution profiles of the parabolic trough concentrator measured by a camera-based method are compared to identify the Gaussian slope error, gravity deformation error and alignment error of the concentrator. This indicates that it is feasible to invert the flux profile on the absorber wall and diagnose the types and values of the corresponding optical errors of the concentrator.

The Effect of Bit Depth on High-Temperature Digital Image Correlation Measurements

Digital Image Correlation (DIC) is a camera-based method of measuring displacement and strain. High-temperature DIC is challenging due to light emitted from the sample which can saturate the image. This effect can be mitigated using optical bandpass filters, but the maximum sample temperature range of DIC remains dependent on the camera and camera settings. Among camera settings, bit depth, also referred to as color depth or number of bits, has received insufficient attention in high-temperature DIC literature. In this work, the effect of bit depth on DIC measurements is investigated both analytically and experimentally. It is shown that if image noise is sufficiently low, then increasing bit depth reduces DIC random error. A new metric, the effective number of bits, is presented to determine the appropriate number of bits for DIC images. Using increased bit depth, reduced exposure time, and low-noise images, the maximum sample temperature for DIC measurements was shown to increase without negatively impacting random error.

Remote investigation and assessment of vital signs (RIA-VS)—proof of concept for contactless estimation of blood pressure, pulse, respiratory rate, and oxygen saturation in patients with suspicion of COVID-19

Background Vital signs are critical in assessing the severity and prognosis of infections, for example, COVID-19, influenza, sepsis, and pneumonia. This study aimed to evaluate a new method for rapid camera-based non-contact measurement of heart rate, blood oxygen saturation, respiratory rate, and blood pressure. Methods Consecutive adult patients attending a hospital emergency department for suspected COVID-19 infection were invited to participate. Vital signs measured with a new camera-based method were compared to the corresponding standard reference methods. The camera device observed the patient's face for 30 s from ∼1 m. Results Between 1 April and 1 October 2020, 214 subjects were included in the trial, 131 female (61%) and 83 male (39%). The mean age was 44 years (range 18–81 years). The new camera-based device’s vital signs measurements were, on average, very close to the gold standard but the random variation was larger than the reference methods. Conclusions The principle of contactless measurement of blood pressure, pulse, respiratory rate, and oxygen saturation works, which is very promising. However, technical improvements to the equipment used in this study to reduce its random variability is required before clinical implementation. This will likely be a game changer once this is sorted out. Clinical trial registration Universal Trial Number (UTN) U1111-1251-4114 and the ClinicalTrials.gov Identifier NCT04383457.

Validation of an Automated Wearable Camera-Based Image-Assisted Recall Method and the 24-h Recall Method for Assessing Women's Time Allocation in a Nutritionally Vulnerable Population: The Case of Rural Uganda.

Accurate data are essential for investigating relationships between maternal time-use patterns and nutritional outcomes. The 24 h recall (24HR) has traditionally been used to collect time-use data, however, automated wearable cameras (AWCs) with an image-assisted recall (IAR) may reduce recall bias. This study aimed to evaluate their concurrent criterion validity for assessing women’s time use in rural Eastern Ugandan. Women’s (n = 211) time allocations estimated via the AWC-IAR and 24HR methods were compared with direct observation (criterion method) using the Bland–Altman limits of agreement (LOA) method of analysis and Cronbach’s coefficient alpha (time allocation) or Cohen’s κ (concurrent activities). Systematic bias varied from 1 min (domestic chores) to 226 min (caregiving) for 24HR and 1 min (own production) to 109 min (socializing) for AWC-IAR. The LOAs were within 2 h for employment, own production, and self-care for 24HR and AWC-IAR but exceeded 11 h (24HR) and 9 h (AWC-IAR) for caregiving and socializing. The LOAs were within four concurrent activities for 24HR (−1.1 to 3.7) and AWC-IAR (−3.2 to 3.2). Cronbach’s alpha for time allocation ranged from 0.1728 (socializing) to 0.8056 (own production) for 24HR and 0.2270 (socializing) to 0.7938 (own production) for AWC-IAR. For assessing women’s time allocations at the population level, the 24HR and AWC-IAR methods are accurate and reliable for employment, own production, and domestic chores but poor for caregiving and socializing. The results of this study suggest the need to revisit previously published research investigating the associations between women’s time allocations and nutrition outcomes.

A Camera-Based Method for Collecting Rapid Vegetation Data to Support Remote-Sensing Studies of Shrubland Biodiversity

The decline in biodiversity in Mediterranean-type ecosystems (MTEs) and other shrublands underscores the importance of understanding the trends in species loss through consistent vegetation mapping over broad spatial and temporal ranges, which is increasingly accomplished with optical remote sensing (imaging spectroscopy). Airborne missions planned by the National Aeronautics and Space Administration (NASA) and other groups (e.g., US National Ecological Observatory Network, NEON) are essential for improving high-quality maps of vegetation and plant species. These surveys require robust and efficient ground calibration/validation data; however, barriers to ground-data collection exist, such as steep terrain, which is a common feature of Mediterranean-type ecosystems. We developed and tested a method for rapidly collecting ground-truth data for shrubland plant communities across steep topographic gradients in southern California. Our method utilizes semi-aerial photos taken with a high-resolution digital camera mounted on a telescoping pole to capture groundcover, and a point-intercept image-classification program (Photogrid) that allows efficient sub-sampling of field images to derive vegetation percent-cover estimates while reducing human bias. Here, we assessed the quality of data collection using the image-based method compared to a traditional point-intercept ground survey and performed time trials to compare the efficiency of various survey efforts. The results showed no significant difference in estimates of percent cover and Simpson’s diversity derived from the point-intercept and those derived using the image-based method; however, there was lower correspondence in estimates of species richness and evenness. The image-based method was overall more efficient than the point-intercept surveys, reducing the total survey time by 13 to 46 min per plot depending on sampling effort. The difference in survey time between the two methods became increasingly greater when the vegetation height was above 1 m. Due to the high correspondence between estimates of species percent cover derived from the image-based compared to the point-intercept method, we recommend this type of survey for the verification of remote-sensing datasets featuring percent cover of individual species or closely related plant groups, for use in classifying UAS imagery, and especially for use in MTEs that have steep, rugged terrain and other situations such as tall, dense-growing shrubs where traditional field methods are dangerous or burdensome.

Indoor Localization of Hand-Held OCT Probe Using Visual Odometry and Real-Time Segmentation Using Deep Learning.

Optical coherence tomography (OCT) is an established medical imaging modality that has found widespread use due to its ability to visualize tissue structures at a high resolution. Currently, OCT hand-held imaging probes lack positional information, making it difficult or even impossible to link a specific image to the location it was originally obtained. In this study, we propose a camera-based localization method to track and record the scanner position in real-time, as well as providing a deep learning-based segmentation method. We used camera-based visual odometry (VO) and simultaneous mapping and localization (SLAM) to compute and visualize the location of a hand-held OCT imaging probe. A deep convolutional neural network (CNN) was used for kidney tubule lumens segmentation. The mean absolute error (MAE) and the standard deviation (STD) for 1D translation were found to be 0.15 mm and 0.26mm respectively. For 2D translation, the MAE and STD were found to be 0.85 mm and 0.50 mm, respectively. The dice coefficient of the segmentation method was 0.7. The t-statistic of the T-test between predicted and actual average densities and predicted and actual average diameters were 7.7547e-13 and 2.2288e-15 respectively. We also experimented on a preserved kidney utilizing our localization method with automatic segmentation. Comparisons of the average density maps and average diameter maps were made between the 3D comprehensive scan and VO system scan. Our results demonstrate that VO can track the probe location at high accuracy, and provides a user-friendly visualization tool to review OCT 2D images in 3D space. It also indicates that deep learning can provide high accuracy and high speed for segmentation. The proposed methods can be potentially used to predict delayed graft function (DGF) in kidney transplantation.

EmbeddedPigCount: Pig Counting with Video Object Detection and Tracking on an Embedded Board.

Knowing the number of pigs on a large-scale pig farm is an important issue for efficient farm management. However, counting the number of pigs accurately is difficult for humans because pigs do not obediently stop or slow down for counting. In this study, we propose a camera-based automatic method to count the number of pigs passing through a counting zone. That is, using a camera in a hallway, our deep-learning-based video object detection and tracking method analyzes video streams and counts the number of pigs passing through the counting zone. Furthermore, to execute the counting method in real time on a low-cost embedded board, we consider the tradeoff between accuracy and execution time, which has not yet been reported for pig counting. Our experimental results on an NVIDIA Jetson Nano embedded board show that this “light-weight” method is effective for counting the passing-through pigs, in terms of both accuracy (i.e., 99.44%) and execution time (i.e., real-time execution), even when some pigs pass through the counting zone back and forth.

Clinical Comparison of the Glomerular Filtration Rate Calculated from Different Renal Depths and Formulae

A camera-based method using Technetium-99m diethylenetriaminepentaacetic acid (Tc-99m DTPA) is commonly used to calculate glomerular filtration rate (GFR), especially, as it can easily calculate split renal function. Renal depth is the main factor affecting the measurement of GFR accuracy. This study aimed to compare the difference of renal depths between three formulae and a CT scan, and, additionally, to calculate the GFRs by four methods. We retrospectively reviewed the medical records of patients receiving a renal dynamic scan. All patients underwent a laboratory test within one month, and a computed tomography (CT) scan within two months, before or after the renal dynamic scan. The GFRs were calculated by employing a renal dynamic scan using renal depth measured in three formulae (Tonnesen’s, Itoh K’s, and Taylor’s), and a CT scan. The renal depths measured by the above four methods were compared, and the GFRs were compared to the modified estimated GFR (eGFR). Fifty-one patients were enrolled in the study. The mean modified eGFR was 60.5 ± 42.7 mL/min. The mean GFRs calculated by three formulae and CT were 45.3 ± 23.3, 54.7 ± 27.5, 56.5 ± 26.3, and 63.7 ± 30.0, respectively. All of them correlated well with the modified eGFR (r = 0.87, 0.87, 0.87, and 0.84, respectively). The Bland–Altman plot revealed good consistency between the calculated GFR by Tonnesen’s and the modified eGFR. The renal depths measured using the three formulae were smaller than those measured using the CT scan, and the right renal depth was always larger than the left. In patients with modified eGFR > 60 mL/min, the GFR calculated by CT was the closest to the modified eGFR. The Renal depth measured by CT scan is deeper than that using formula, and it influences the GFR calculated by Gate’s method. The GFR calculated by CT is more closely related to modified eGFR when modified eGFR > 60 mL/min.