Resolution Camera Research Articles

An Experimental Study on Deep Learning Technique Implemented on Low Specification OpenMV Cam H7 Device

This research aims to identify and recognize the OpenMV Camera H7. In this research, all tests were carried out using Deep Machine Learning and applied to several functions, including Face Recognition, Facial Expression Recognition, Detection and Calculation of the Number of Objects, and Object Depth Estimation. Face Expression Recognition was used in the Convolutional Neural Network to recognize five facial expressions: angry, happy, neutral, sad, and surprised. This allowed the use of a primary dataset with a 48MP resolution camera. Some scenarios are prepared to meet environment variability in the implementation, such as indoor and outdoor environments, with different lighting and distance. Most pre-trained models in each identification or recognition used mobileNetV2 since this model allows low computation cost and matches with low hardware specifications. The object detection and counting module compared two methods: the conventional Haar Cascade and the Deep Learning MobileNetV2 model. The training and validation process is not recommended to be carried out on OpenMV devices but on computers with high specifications. This research was trained and validated using selected primary and secondary data, with 1500 image data. The computing time required is around 5 minutes for ten epochs. On average, recognition results on OpenMV devices take around 0.3 - 2 seconds for each frame. The accuracy of the recognition results varies depending on the pre-trained model and the dataset used, but overall, the accuracy levels achieved tend to be very high, exceeding 96.6%.

Application of Image Restoration and Object Detection Techniques in Fruit-Picking Robots

This paper focuses on the problem that existing apple picking robots are unable to accurately recognize targets in complex and unstructured orchard environments. The target detection algorithm of the robot is optimized based on YOLOv5 and ResNet neural network model. First, considering the low resolution of the robot's own binocular camera, this paper adopts the RealSR model to process the image with super-resolution. Then, the YOLOv5 model is chosen as the optimized target detection algorithm, which can obtain the number, location, ripeness and quality information of apples in the image through training. Secondly, by comparing the VGG and ResNet models, which are widely used in the field of image classification, the ResNet model is finally chosen as the main body for recognizing and classifying different fruits, and its correct rate can reach more than 97%.

Comprehensive study on the development of an automatic helmet violator detection system (AHVDS) using advanced machine learning techniques

The number of motorcycle accidents has increased rapidly due to different causes. Helmet is a one of the major safety equipment that helps to reduce the fatal accidents even though many of the motorcyclists are not used that. Currently many researchers have focused on traffic violator detection but they are yet to identify the violated person details as it requires high resolution cameras and expensive sensors for number plate extraction. This research article is focused on comprehensive study on the development of an automated helmet violation detection with number plate extraction in real time. This study divides the dataset into rider with helmet, rider without helmet, passenger with helmet and passenger without helmet classes, and four training model was created to detect helmet in rider and passengers. The proposed model was constructed in three stages to attain high accuracy with a vast amount of training dataset. In the first stage, the helmet was detected in the input image using the deep learning object-detection model .In the second stage, if the helmet identification confirmed in the input image then the entire motorcyclist image is segmented based on object segmentation model. In the third stage, number plate training model is deployed into the same object detection and object segmentation model to identify and segment the license plates in the motorcyclist image. This study primarily incorporates the Mask Region based Convolution Neural Networks (Mask R-CNN) for object detection and segmentation Further, it incorporates the three different deep neural networks like Dense block Network (DenseNet-121), Residual Network (ResNet-101) and Inception Residual Network architecture V2layers (Inception Res Net V2) to create accurate helmet violator detections with more extensive information. The experimental results indicates that this comprehensive helmet violator detection study improves the helmet detection accuracy by 6.2 %.In addition, the above three comprehensive proposed models are evaluated under Wilcoxon rank test for better sensitive information. Further, the less object detection loss (2 %) and significant computational time variation (69 %) indicates the performance of the comprehensive helmet violator detection system was good compared to existing study.

PallorMetrics: Software for Automatically Quantifying Optic Disc Pallor in Fundus Photographs, and Associations With Peripapillary RNFL Thickness.

We sough to develop an automatic method of quantifying optic disc pallor in fundus photographs and determine associations with peripapillary retinal nerve fiber layer (pRNFL) thickness. We used deep learning to segment the optic disc, fovea, and vessels in fundus photographs, and measured pallor. We assessed the relationship between pallor and pRNFL thickness derived from optical coherence tomography scans in 118 participants. Separately, we used images diagnosed by clinical inspection as pale (n = 45) and assessed how measurements compared with healthy controls (n = 46). We also developed automatic rejection thresholds and tested the software for robustness to camera type, image format, and resolution. We developed software that automatically quantified disc pallor across several zones in fundus photographs. Pallor was associated with pRNFL thickness globally (β = -9.81; standard error [SE] = 3.16; P < 0.05), in the temporal inferior zone (β = -29.78; SE = 8.32; P < 0.01), with the nasal/temporal ratio (β = 0.88; SE = 0.34; P < 0.05), and in the whole disc (β = -8.22; SE = 2.92; P < 0.05). Furthermore, pallor was significantly higher in the patient group. Last, we demonstrate the analysis to be robust to camera type, image format, and resolution. We developed software that automatically locates and quantifies disc pallor in fundus photographs and found associations between pallor measurements and pRNFL thickness. We think our method will be useful for the identification, monitoring, and progression of diseases characterized by disc pallor and optic atrophy, including glaucoma, compression, and potentially in neurodegenerative disorders.

RMSRGAN: A Real Multispectral Imagery Super-Resolution Reconstruction for Enhancing Ginkgo Biloba Yield Prediction

Multispectral remote sensing data with abundant spectral information can be used to compute vegetation indices to improve the accuracy of Ginkgo biloba yield prediction. The limited spatial resolution of multispectral cameras restricts the detail capture over wide farmland, but super-resolution (SR) reconstruction methods can enhance image quality. However, most existing SR models have been trained on images processed from downsampled high-resolution (HR) images, making them less effective in reconstructing real low-resolution (LR) images. This study proposes a GAN-based super-resolution reconstruction method (RMSRGAN) for multispectral remote sensing images of Ginkgo biloba trees in real scenes. A U-Net-based network is employed instead of the traditional discriminator. Convolutional block attention modules (CBAMs) are incorporated into the Residual-in-Residual Dense Blocks (RRDBs) of the generator and the U-Net of the discriminator to preserve image details and texture features. An unmanned aerial vehicle (UAV) equipped with a multispectral camera was employed to capture field multispectral remote sensing images of Ginkgo biloba trees at different spatial resolutions. Four matching HR and LR datasets were created from these images to train RMSRGAN. The proposed model outperforms the traditional models by achieving superior results in both quantitative evaluation metrics (peak signal-to-noise ratio (PSNR) is 32.490, 31.085, 27.084, 26.819, and structural similarity index (SSIM) is 0.894, 0.881, 0.832, 0.818, respectively) and qualitative evaluation visualization. Furthermore, the efficiency of our proposed method was tested by generating individual vegetation indices (VIs) from images taken before and after reconstruction to predict the yield of Ginkgo biloba. The results show that the SR images exhibit better R2 and RMSE values than LR images. These findings show that RMSRGAN can improve the spatial resolution of real multispectral images, increasing the accuracy of Ginkgo biloba yield prediction and providing more effective and accurate data support for crop management.

Estimation with Ultimate Quantum Precision of the Transverse Displacement between Two Photons via Two-Photon Interference Sampling Measurements.

We present a quantum sensing scheme achieving the ultimate quantum sensitivity in the estimation of the transverse displacement between two photons interfering at a balanced beam splitter, based on transverse-momentum sampling measurements at the output. This scheme can possibly lead to enhanced high-precision nanoscopic techniques, such as superresolved single-molecule localization microscopy with quantum dots, by circumventing the requirements in standard direct imaging of camera resolution at the diffraction limit, and of highly magnifying objectives. Interestingly, we show that our interferometric technique achieves the ultimate spatial precision in nature irrespectively of the overlap of the two displaced photonic wave packets, while its precision is only reduced of a constant factor for photons differing in any nonspatial degrees of freedom. This opens a new research paradigm based on the interface between spatially resolved quantum interference and quantum-enhanced spatial sensitivity.

Object Detection Model for Web-Based Physical Distancing Detector Using Deep Learning

The pandemic has changed the way people interact with each other in the public setting. As a result, social distancing has been implemented in public society to reduce the virus’s spread. Automatically detecting social distancing is paramount in reducing menial manual tasks. There are several methods to detect social distance in public, and one is through a surveillance camera. However, detecting social distance through a camera is not an easy task. Problems, such as lighting, occlusion, and camera resolution, can occur during detection. The research aims to develop a physical distancing detector system that is adjusted to work with Indonesian rules and conditions, especially in Jakarta, using deep learning (i.e., YOLOv4 architecture with the Darknet framework) and the CrowdHuman dataset. The detection is done by reading the source video, detecting the distance between individuals, and determining the crowd of individuals close to each other. In order to accomplish the detection, the training is done with CSPDarknet53 and VGG16 backbone in YOLOv4 and YOLOv4 Tiny architecture using various hyperparameters in the training process. Several explorations are made in the research to find the best combination of architectures and fine-tune them. The research successfully detects crowds at the 16th training, with mAP50 of 71.59% (74.04% AP50) and 16.2 Frame per Second (FPS) displayed on the web. The input size is essential for determining the model’s accuracy and speed. The model can be implemented in a web-based application.

Design of a Multimodal Detection System Tested on Tea Impurity Detection

A multimodal detection system with complementary capabilities for efficient detection was developed for impurity detection. The system consisted of a visible light camera, a multispectral camera, image correction and registration algorithms. It can obtain spectral features and color features at the same time and has higher spatial resolution than a single spectral camera. This system was applied to detect impurities in Pu’er tea to verify its high efficiency. The spectral and color features of each pixel in the images of Pu’er tea were obtained by this system and used for pixel classification. The experimental results showed that the accuracy of a support vector machine (SVM) model based on combined features was 93%, which was 7% higher than that based on spectral features only. By applying a median filtering algorithm and a contour detection algorithm to the label matrix extracted from pixel-classified images, except hair, eight impurities were detected successfully. Moreover, taking advantage of the high resolution of a visible light camera, small impurities could be clearly imaged. By comparing the segmented color image with the pixel-classified image, small impurities such as hair could be detected successfully. Finally, it was proved that the system could obtain multiple images to allow a more detailed and comprehensive understanding of the detected items and had an excellent ability to detect small impurities.

Local analysis of micro mixing for the Villermaux-Dushman protocol by using the imaging UV-Vis spectroscopy

The Villermaux-Dushman protocol with UV-Vis analytics is an established tool to characterize the global micro mixing performance in process equipment. The local mixing process is described by a micro mixing model (incorporation model), originally designed for turbulent flows. The novel imaging UV-Vis spectroscopy used in this work uncovers locally resolved micro mixing phenomena in a laminar split-and-recombine (SAR) mixer unit manufactured from selective laser-induced etching (SLE). The local absorbance is recorded with a high spatial resolution camera through a telecentric lens and post-processed into local concentration fields of components. The method unveils discrepancies of the micro mixing time determined from the conventional incorporation model in laminar flow and the locally recorded mixing process. The micro mixing time rather needs to be seen as a mean micro mixing time instead. Furthermore, the spatial information obtained by the imaging UV-Vis spectroscopy gives insight into local micro mixing and selectivity, yielding new approaches to equipment optimization.

Selection based on the phenomenic approach and agronomic ideotic of white oat

AbstractThis work aimed to propose multivariate selection strategies reconciling the agronomic ideotype and vegetation indices to obtain genotypes with high tillering, early cycle, tolerant to lodging, and foliar diseases. The sowing of white oat lines was carried out on April 27, 2023, allocated in an experimental design of blocks augmented with interspersed controls. The regular treatments correspond to 593 lines of white oat and the common treatments were represented by the cultivars URS Olada, URS Altaneira, and IPR Artêmis arranged in four replications. The measurements of variables of agronomic interest took place in each experimental unit as follows: percentage of tillering, days to flowering, percentage of lodging, percentage of leaf rust, percentage of stem rust, and percentage of leaf spots. The phenomic analyses took place through flights carried out with a specialized drone, equipped with a 20 MP resolution camera and standard height (80 m). The best proposed indices were index for reducing the percentage of lodging with normalized green (NG), green–red ratio (IGR), soil color index (SCI), green leaf index (GLI), visible atmospheric resistant index (VARI), green leaf index 2 (GLI2), red–blue (RB), blue green pigment (BGI), normalized red–blue difference index (NRBDI), green–blue ratio, BIM, normalized green–blue difference index (NGBDI), and ExGR; index for reducing days to flowering with brightness index (BI), NRBDI, days to flowering (DF), gray (GRAY), IGR, average intensity (L), normalized red (NR), RGRI, SCI, BGI, BIM, GLI, NGBDI, and NG; and index for reducing the percentage of stem rust with NG, BI, Gray2, GLI2, RGRI, GLI, VARI, BGI, L, NR, RB, and coloration index. Only two lines were selected concomitantly with multivariate indices, line L167 (M5, SA) and L426 (M6, ETMS), that combine lodging tolerance and leaf rust genes.

Camera sheath with transformable head for minimally invasive surgical instruments

Introduction This paper presents a camera sheath that can be assembled to various minimally invasive surgical instruments and provide the localized view of the instrument tip. Material and methods The advanced transformable head structure (ATHS) that overcomes the trade-off between the camera resolution and the instrument size is designed for the sheath. Design solutions to maintain the alignment between the camera’s line of sight and the instrument tip direction during the transformation of the ATHS are derived and applied to the prototype of the sheath. Results The design solution ensured proper alignment between the line of sight and the tip direction. The prototype was used with the curved micro-debrider blades in simulated functional endoscopic sinus surgery (FESS). Deep regions of the sinus that were not observable with the conventional endoscopes was accessed and observed using the prototype. Conclusions The presented camera sheath allows the delivery of the instrument and camera to the surgical site with minimal increase in port size. It may be applied to various surgeries to reduce invasiveness and provide additional visual information to the surgeons.

Statistical Evaluation of the Accuracy of Consumer Drone Photogrammetry at a Romanesque Church in Eastern Bavaria (Germany)

For several years, photogrammetric surveys are quite often applied for surveying cultural heritage. For a detailed and accurate result, it is indispensable that the calculated point cloud represents the original geometry of the building in high quality, with vertical and horizontal structures in the same manner. Whereas many published papers in the field of digital heritage documentation are dealing with creating such models or using photogrammetric data for damage detection, only a few papers are investigating the crucial fact of accuracy and how it can be influenced by the used camera resolution. We want to present a case study, in which we compare the photogrammetric point cloud of a normal consumer drone with a high-end device. Beside the actual survey results showing the details of a Romanesque church, a statistical calculation of deviation is shown. It becomes evident that there is only a difference within some millimetres between the corresponding point clouds in most parts of the monument. Hence, it can be stated that, for most applications in heritage protection, affordable consumer unmanned aerial vehicles (UAVs) are sufficient. Only if faint details have to be resolved will a better resolution of the mounted camera be required.

Event-Based Background-Oriented Schlieren.

Schlieren imaging is an optical technique to observe the flow of transparent media, such as air or water, without any particle seeding. However, conventional frame-based techniques require both high spatial and temporal resolution cameras, which impose bright illumination and expensive computation limitations. Event cameras offer potential advantages (high dynamic range, high temporal resolution, and data efficiency) to overcome such limitations due to their bio-inspired sensing principle. This article presents a novel technique for perceiving air convection using events and frames by providing the first theoretical analysis that connects event data and schlieren. We formulate the problem as a variational optimization one combining the linearized event generation model with a physically-motivated parameterization that estimates the temporal derivative of the air density. The experiments with accurately aligned frame- and event camera data reveal that the proposed method enables event cameras to obtain on par results with existing frame-based optical flow techniques. Moreover, the proposed method works under dark conditions where frame-based schlieren fails, and also enables slow-motion analysis by leveraging the event camera's advantages. Our work pioneers and opens a new stack of event camera applications, as we publish the source code as well as the first schlieren dataset with high-quality frame and event data.

Dynamic measurement of a long‐distance moving object using multi‐binocular high‐speed videogrammetry with adaptive‐weighting bundle adjustment

AbstractThe dynamic measurement of position and attitude information of a long‐distance moving object is a common demand in ground testing of aerospace engineering. Due to the movement from far to near and the limitations of camera resolution, it is necessary to use multi‐binocular cameras for segmented observation at different distances. However, achieving accurate and continuous position and attitude estimation is a challenging task. Therefore, this paper proposes a dynamic monitoring technique for long‐distance movement based on a multi‐binocular videogrammetric system. Aiming to solve the problem that the scale in images changes constantly during the moving process, a scale‐adaptive tracking method of circular targets is presented. Bundle adjustment (BA) with joint segments using an adaptive‐weighting least‐squares strategy is developed to enhance the measurement accuracy. The feasibility and reliability of the proposed technique are validated by a ground testing of relative measurement for spacecraft rendezvous and docking. The experimental results indicate that the proposed technique can obtain the actual motion state of the moving object, with a positioning accuracy of 3.2 mm (root mean square error), which can provide a reliable third‐party verification for on‐orbit measurement systems in ground testing. Compared with the results of BA with individual segments and vision measurement software PhotoModeler, the accuracy is improved by 45% and 30%, respectively.

Research on Image Stitching Technology for Road Surface Cracks

Image stitching refers to the combination of multiple images with a certain over lappingarea rate acquired by the image acquisition device into one image, which has a full picture of each image. Image stitching technology is an important part in the field of digital image processing. With the increasing requirements for image quality and the limited resolution of ordinary cameras, it is impossible to obtain large angle of view and clear image at the same time. In this paper, the key technologies involved in pavement crack image stitching are intensively studied, including image feature point detection and matching, internal and external camera parameter estimation and optimization, and image fusion. The work done in this paper and the main innovations include: (1)Two main feature detection and description algorithms, SIFT algorithm and ORBalgorithm, are studied. In view of the fact that the SIFT algorithm has redundancy when the Gaussian pyramid is established, this paper appropriately reduces the number of groups and maintains the number and stability of feature point detection. Combined with the advantages and disadvantages of the improved SIFT algorithm and ORB algorithm, and the performance of the two algorithms in the detection of pavement cracks, the improvedSIF'T algorithm is used as the feature point detection algorithm, and rBRlEF algorithm for describing the feature points in the ORB algorithm is used as the feature point description algorithm. The description algorithm is called the BSIF'T algorithm. Experiments show that the robustness of BSIFT algorithm is similar to that of SIFT algorithm, which is better than ORB algorithm and faster than SIF'T algorithm. (2)Image fiusion algorithms are studied, including best seam-line search and image fusion. Aiming at the ghost phenomenon that may occur in overlapping regions, this paper proposes a best seam-line search algorithm based on distance transform.

Assessment of the axial resolution of a compact gamma camera with coded aperture collimator

PurposeHandheld gamma cameras with coded aperture collimators are under investigation for intraoperative imaging in nuclear medicine. Coded apertures are a promising collimation technique for applications such as lymph node localization due to their high sensitivity and the possibility of 3D imaging. We evaluated the axial resolution and computational performance of two reconstruction methods.MethodsAn experimental gamma camera was set up consisting of the pixelated semiconductor detector Timepix3 and MURA mask of rank 31 with round holes of 0.08 mm in diameter in a 0.11 mm thick Tungsten sheet. A set of measurements was taken where a point-like gamma source was placed centrally at 21 different positions within the range of 12–100 mm. For each source position, the detector image was reconstructed in 0.5 mm steps around the true source position, resulting in an image stack. The axial resolution was assessed by the full width at half maximum (FWHM) of the contrast-to-noise ratio (CNR) profile along the z-axis of the stack. Two reconstruction methods were compared: MURA Decoding and a 3D maximum likelihood expectation maximization algorithm (3D-MLEM).ResultsWhile taking 4400 times longer in computation, 3D-MLEM yielded a smaller axial FWHM and a higher CNR. The axial resolution degraded from 5.3 mm and 1.8 mm at 12 mm to 42.2 mm and 13.5 mm at 100 mm for MURA Decoding and 3D-MLEM respectively.ConclusionOur results show that the coded aperture enables the depth estimation of single point-like sources in the near field. Here, 3D-MLEM offered a better axial resolution but was computationally much slower than MURA Decoding, whose reconstruction time is compatible with real-time imaging.

Dynamical Behavior of a Stacked Blue Laser Diode Consisting of Two Active Regions with Wide InGaN Quantum Wells and Two Tunnel Junctions

In nitride‐based laser diodes, the poor conductivity of the p‐type region, higher light absorption, and the difficult processing of an ohmic p‐contact with low resistance are major challenges. Recently interband tunnel junctions (TJs) have been used to replace much of the hole transport layers by electron transport layers, and also to allow for two n‐type contacts. In this work, GaN‐based laser diode stacks with TJs are analyzed. The devices are grown with plasma‐assisted molecular beam epitaxy and include two waveguides with active regions consisting of 25 nm wide InGaN quantum well (QW), each, which are connected through a TJ. A second TJ is placed on top to eliminate the p‐type contact. In this study, the time‐dependent spectral behavior of such structures is analyzed by streak camera measurements. The two QWs have different lasing thresholds and emission wavelength. Using the temporal resolution of the streak camera, a lasing onset of the second QW a few nanoseconds after the first one is observed. Single shot measurements show unusual intensity oscillations from both QWs, however, one is stable and the other laser diode (LD) has irregular oscillations.

Towards Real-Time 3D Terrain Reconstruction from Aerial Imagery

We present a near real-time solution for 3D reconstruction from aerial images captured by consumer UAVs. Our core idea is to simplify the multi-view stereo problem into a series of two-view stereo matching problems. Our method applies to UAVs equipped with only one camera and does not require special stereo-capturing setups. We found that the neighboring two video frames taken by UAVs flying at a mid-to-high cruising altitude can be approximated as left and right views from a virtual stereo camera. By leveraging GPU-accelerated real-time stereo estimation, efficient PnP correspondence solving algorithms, and an extended Kalman filter, our system simultaneously predicts scene geometry and camera position/orientation from the virtual stereo cameras. Also, this method allows for the user selection of varying baseline lengths, which provides more flexibility given the trade-off between camera resolution, effective measuring distance, flight altitude, and mapping accuracy. Our method outputs dense point clouds at a constant speed of 25 frames per second and is validated on a variety of real-world datasets with satisfactory results.

The Evolution of Spinal Endoscopy: Design and Image Analysis of a Single-Use Digital Endoscope Versus Traditional Optic Endoscope.

Spinal endoscopy has evolved significantly since its inception, offering minimally invasive solutions for various spinal pathologies. This study introduces a promising innovation in spinal endoscopy-a single-use digital endoscope designed to overcome the drawbacks of traditional optic endoscopes. Traditional endoscopes, despite their utility, present challenges such as fragility, complex disinfection processes, weight issues, and susceptibility to mechanical malfunctions. The digital endoscope, with its disposable nature, lighter weight, and improved image quality, aims to enhance surgical procedures and patient safety. The digital endoscope system comprises a 30-degree 1000 × 1000 pixel resolution camera sensor with a 4.3 mm working channel, and LED light sources replacing optical fibers. The all-in-one touch screen tablet serves as the host computer, providing portability and simplified operation. Image comparisons between the digital and optic endoscopes revealed advantages in the form of increased field of view, lesser distortion, greater close-range resolution, and enhanced luminance. The single-use digital endoscope demonstrates great potential for revolutionizing spine endoscopic surgeries, offering convenience, safety, and superior imaging capabilities compared to traditional optic endoscopes.

Multicentric study on the reproducibility and robustness of PET-based radiomics features with a realistic activity painting phantom.

Previously, we developed an "activity painting" tool for PET image simulation; however, it could simulate heterogeneous patterns only in the air. We aimed to improve this phantom technique to simulate arbitrary lesions in a radioactive background to perform relevant multi-center radiomic analysis. We conducted measurements moving a 22Na point source in a 20-liter background volume filled with 5 kBq/mL activity with an adequately controlled robotic system to prevent the surge of the water. Three different lesion patterns were "activity-painted" in five PET/CT cameras, resulting in 8 different reconstructions. We calculated 46 radiomic indeces (RI) for each lesion and imaging setting, applying absolute and relative discretization. Reproducibility and reliability were determined by the inter-setting coefficient of variation (CV) and the intraclass correlation coefficient (ICC). Hypothesis tests were used to compare RI between lesions. By simulating precisely the same lesions, we confirmed that the reconstructed voxel size and the spatial resolution of different PET cameras were critical for higher order RI. Considering conventional RIs, the SUVpeak and SUVmean proved the most reliable (CV<10%). CVs above 25% are more common for higher order RIs, but we also found that low CVs do not necessarily imply robust parameters but often rather insensitive RIs. Based on the hypothesis test, most RIs could clearly distinguish between the various lesions using absolute resampling. ICC analysis also revealed that most RIs were more reproducible with absolute discretization. The activity painting method in a real radioactive environment proved suitable for precisely detecting the radiomic differences derived from the different camera settings and texture characteristics. We also found that inter-setting CV is not an appropriate metric for analyzing RI parameters' reliability and robustness. Although multicentric cohorts are increasingly common in radiomics analysis, realistic texture phantoms can provide indispensable information on the sensitivity of an RI and how an individual RI parameter measures the texture.