Dataset Information

Experimental image dataset for validation of the noise-induced bias that affects Digital Image Correlation.

ABSTRACT: The present article shares with the scientific community several image sets used for experimentally validating the noise-induced bias on different Digital Image Correlation (DIC) formulations, as reported in Baldi et al. [1]. These sets are provided with a description of the experimental setup used for image acquisition. The basic idea is to acquire a series of images obtained by rigidly translating the target, where incremental displacement is of the order of a small fraction of a pixel. This condition requires a high-precision control of the target position through the tests. Moreover, the noise content of each image set is modulated using a statistical approach that uncouples the intensity field from the standard deviation. Lastly, the images are acquired with different exposure conditions to analyze the gray tone gradient effect on noise-induced bias.

SUBMITTER: Baldi A

PROVIDER: S-EPMC9046951 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Experimental image dataset for validation of the noise-induced bias that affects Digital Image Correlation.

Baldi Antonio A Santucci Pietro Maria PM Bertolino Filippo F

Data in brief 20220410

The present article shares with the scientific community several image sets used for experimentally validating the noise-induced bias on different Digital Image Correlation (DIC) formulations, as reported in Baldi et al. [1]. These sets are provided with a description of the experimental setup used for image acquisition. The basic idea is to acquire a series of images obtained by rigidly translating the target, where incremental displacement is of the order of a small fraction of a pixel. This c ...[more]

PMID: 35496488

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Project description:The infrastructure is in many countries aging and continuous maintenance is required to ensure the safety of the structures. For concrete structures, cracks are a part of the structure's life cycle. However, assessing the structural impact of cracks in reinforced concrete is a complex task. The purpose of this paper is to present a dataset that can be used to verify and compare the results of the measured crack propagation in concrete with the well-known Digital Image Correlation (DIC) technique and with Crack Monitoring from Motion (CMfM), a novel photogrammetric algorithm that enables high accurate measurements with a non-fixed camera. Moreover, the data can be used to investigate how existing cracks in reinforced concrete could be implemented in a numerical model. Therefore, the first potential area to use this dataset is within image processing techniques with a focus on DIC. Until recently, DIC suffered from one major disadvantage; the camera must be fixed during the entire period of data collection. Naturally, this decreases the flexibility and potential of using DIC outside the laboratory. In a recently published paper (Belloni et al., 2023), an innovative photogrammetric algorithm (CMfM) that enables the use of a moving camera, i.e. a camera that is not fixed during data acquisition, was presented. The imagery of this dataset (Sjölander et al., 2023) was used to verify the potential of this algorithm and could be used to validate other approaches for non-fixed cameras. The second potential area is structural engineering. The data can be used to verify non-linear material models used in finite element (FE) software to simulate the structural response of reinforced concrete. In particular, the data can be used to investigate how existing cracks should be modelled in a FE model. The dataset presented in this paper includes data collected from a three-point bending test performed in a laboratory environment on uncracked and pre-cracked reinforced concrete beams. Structural testing was performed using a displacement-controlled set-up, which continuously recorded the force and the vertical displacement of a centric placed loading piston. First, the response of three uncracked beams was recorded. Thereafter, photos of the resulting cracks were taken, and a detailed mapping was presented. Material properties for the concrete, e.g., compressive strength, are presented together with testing of the tensile capacity of the reinforcement and a compressive test of the soft fiber boards used at the support to ensure good contact between steel and concrete. Then, the structural response of the pre-cracked beams was tested. During this test, four fixed cameras were used to monitor the crack propagation at different locations on the beam. Images are presented at the start of the load sequences and at pre-defined load stops during the testing. Hence, the crack opening captured in the images can be correlated to the force-displacement data. Moreover, a non-fixed camera was used to capture additional imagery at the location of each fixed camera.

Project description:IntroductionOne major challenge in PET radiomics is its sensitivity to noise. Low signal-to-noise ratio (SNR) affects not only the precision but also the accuracy of quantitative metrics extracted from the images resulting in noise-induced bias. This phantom study aims to identify the radiomic features that are robust to noise in terms of precision and accuracy and to explore some methods that might help to correct noise-induced bias.MethodsA phantom containing three 18F-FDG filled 3D printed inserts, reflecting heterogeneous tracer uptake and realistic tumor shapes, was used in the study. The three different phantom inserts were filled and scanned with three different tumor-to-background ratios, simulating a total of nine different tumors. From the 40-minute list-mode data, ten frames each for 5 s, 10 s, 30 s, and 120 s frame duration were reconstructed to generate images with different noise levels. Under these noise conditions, the precision and accuracy of the radiomic features were analyzed using intraclass correlation coefficient (ICC) and similarity distance metric (SDM) respectively. Based on the ICC and SDM values, the radiomic features were categorized into four groups: poor, moderate, good, and excellent precision and accuracy. A "difference image" created by subtracting two statistically equivalent replicate images was used to develop a model to correct the noise-induced bias. Several regression methods (e.g., linear, exponential, sigmoid, and power-law) were tested. The best fitting model was chosen based on Akaike information criteria.ResultsSeveral radiomic features derived from low SNR images have high repeatability, with 68% of radiomic features having ICC ≥ 0.9 for images with a frame duration of 5 s. However, most features show a systematic bias that correlates with the increase in noise level. Out of 143 features with noise-induced bias, the SDM values were improved based on a regression model (53 features to excellent and 67 to good) indicating that the noise-induced bias of these features can be, at least partially, corrected.ConclusionTo have a predictive value, radiomic features should reflect tumor characteristics and be minimally affected by noise. The present study has shown that it is possible to correct for noise-induced bias, at least in a subset of the features, using a regression model based on the local image noise estimates.

Dataset Information

Experimental image dataset for validation of the noise-induced bias that affects Digital Image Correlation.

Publications

Experimental image dataset for validation of the noise-induced bias that affects Digital Image Correlation.

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