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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

SU-D-209-03: Radiation Dose Reduction Using Real-Time Image Processing in Interventional Radiology

Purpose: To characterize changes in radiation dose after introducing a new real-time image processing technology in interventional radiology systems. Methods: Interventional radiology (IR) procedures are increasingly complex, at times requiring substantial time and radiation dose. The risk of inducing tissue reactions as well as long-term stochastic effects such as radiation-induced cancer is not trivial. To reduce this risk, IR systems are increasingly equipped with dose reduction technologies.Recently, ClarityIQ (Philips Healthcare) technology was installed in our existing neuroradiology IR (NIR) and vascular IR (VIR) suites respectively. ClarityIQ includes real-time image processing that reduces noise/artifacts, enhances images, and sharpens edges while also reducing radiation dose rates. We reviewed 412 NIR (175 pre- and 237 post-ClarityIQ) procedures and 329 VIR (156 preand 173 post-ClarityIQ) procedures performed at our institution pre- and post-ClarityIQ implementation. NIR procedures were primarily classified as interventional or diagnostic. VIR procedures included drain port, drain placement, tube change, mesenteric, and implanted venous procedures. Air Kerma (AK in units of mGy) was documented for all the cases using a commercial radiation exposure management system. Results: When considering all NIR procedures, median AK decreased from 1194 mGy to 561 mGy. When considering all VIR procedures, median AK decreased from 49 to 14 mGy. Both NIR and VIR exhibited a decrease in AK exceeding 50% after ClarityIQ implementation, a statistically significant (p<0.05) difference. Of the 5 most common VIR procedures, all median AK values decreased, but significance (p<0.05) was only reached in venous access (N=53), angio mesenteric (N=41), and drain placement procedures (N=31). Conclusion: ClarityIQ can reduce dose significantly for both NIR and VIR procedures. Image quality was not assessed in conjunction with the dose reduction.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Image processing analysis of nuclear track parameters for CR-39 detector irradiated by thermal neutron

CR-39 detector which covered with boric acid (H{sub 3}Bo{sub 3}) pellet was irradiated by thermal neutrons from ({sup 241}Am - {sup 9}Be) source with activity 12Ci and neutron flux 10{sup 5} n. cm{sup −2}. s{sup −1}. The irradiation times -T{sub D} for detector were 4h, 8h, 16h and 24h. Chemical etching solution for detector was sodium hydroxide NaOH, 6.25N with 45 min etching time and 60 C° temperature. Images of CR-39 detector after chemical etching were taken from digital camera which connected from optical microscope. MATLAB software version 7.0 was used to image processing. The outputs of image processing of MATLAB software were analyzed and found the following relationships: (a) The irradiation time -T{sub D} has behavior linear relationships with following nuclear track parameters: i) total track number - N{sub T} ii) maximum track number - MRD (relative to track diameter - D{sub T}) at response region range 2.5 µm to 4 µm iii) maximum track number - M{sub D} (without depending on track diameter - D{sub T}). (b) The irradiation time -T{sub D} has behavior logarithmic relationship with maximum track number - M{sub A} (without depending on track area - A{sub T}). The image processing technique principally track diameter - D{sub T} can be take into account to classification of α-particle emitters, In addition to the contribution of these technique in preparation of nano- filters and nano-membrane in nanotechnology fields.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Measurement and image processing evaluation of surface modifications of dental implants G4 pure titanium created by different techniques

Foreign substances and organic tissue interaction placed into the jaw in order to eliminate tooth loss involves a highly complex process. Many biological reactions take place as well as the biomechanical forces that influence this formation. Osseointegration denotes to the direct structural and functional association between the living bone and the load-bearing artificial implant's surface. Taking into consideration of the requirements in the manufacturing processes of the implants, surface characterizations with high precise measurement techniques are investigated and thus long-term success of dental implant is emphasized on the importance of these processes in this study. In this research, the detailed surface characterization was performed to identify the dependence of the manufacturing techniques on the surface properties by using the image processing methods and using the scanning electron microscope (SEM) for morphological properties in 3D and Taylor Hobson stylus profilometer for roughness properties in 2D. Three implant surfaces fabricated by different manufacturing techniques were inspected, and a machined surface was included into the study as a reference specimen. The results indicated that different surface treatments were strongly influenced surface morphology. Thus 2D and 3D precise inspection techniques were highlighted on the importance for surface characterization. Different image analyses techniques such as Dark-light technique were used to verify the surface measurement results. The computational phase was performed using image processing toolbox in Matlab with precise evaluation of the roughness for the implant surfaces. The relationship between the number of black and white pixels and surface roughness is presented. FFT image processing and analyses results explicitly imply that the technique is useful in the determination of surface roughness. The results showed that the number of black pixels in the image increases with increase in surface roughness.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

WE-G-204-01: BEST IN PHYSICS (IMAGING): Effect of Image Processing Parameters On Nodule Detectability in Chest Radiography

Purpose: To investigate the effect of varying system image processing parameters on lung nodule detectability in digital radiography. Methods: An anthropomorphic chest phantom was imaged in the posterior-anterior position using a GE Discovery XR656 digital radiography system. To simulate lung nodules, a polystyrene board with 6.35mm diameter PMMA spheres was placed adjacent to the phantom (into the x-ray path). Due to magnification, the projected simulated nodules had a diameter in the radiographs of approximately 7.5 mm. The images were processed using one of GE’s default chest settings (Factory3) and reprocessed by varying the “Edge” and “Tissue Contrast” processing parameters, which were the two user-configurable parameters for a single edge and contrast enhancement algorithm. For each parameter setting, the nodule signals were calculated by subtracting the chest-only image from the image with simulated nodules. Twenty nodule signals were averaged, Gaussian filtered, and radially averaged in order to generate an approximately noiseless signal. For each processing parameter setting, this noise-free signal and 180 background samples from across the lung were used to estimate ideal observer performance in a signal-known-exactly detection task. Performance was estimated using a channelized Hotelling observer with 10 Laguerre-Gauss channel functions. Results: The “Edge” and “Tissue Contrast” parameters each had an effect on the detectability as calculated by the model observer. The CHO-estimated signal detectability ranged from 2.36 to 2.93 and was highest for “Edge” = 4 and “Tissue Contrast” = −0.15. In general, detectability tended to decrease as “Edge” was increased and as “Tissue Contrast” was increased. A human observer study should be performed to validate the relation to human detection performance. Conclusion: Image processing parameters can affect lung nodule detection performance in radiography. While validation with a human observer study is needed, model observer detectability for common tasks could provide a means for optimizing image processing parameters.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Benchmarking image processing techniques for porosity measurement in polymer additive manufacturing: Review and experimental analysis

An image processing workflow is proposed for porosity measurement in polymer additive manufacturing. Various techniques, including global and local thresholding, region growing, and K-means clustering, were applied to microscopic images of carbon fiber reinforced acrylonitrile butadiene styrene (CF-ABS) and benchmarked for their ability to accurately measure porosity. Global methods included Otsu, minimum error, iterative, and entropy-based thresholding, while local methods included Niblack, Bernsen, Sauvola, and Bradley-Roth algorithms. Artificial uneven illumination was introduced to test local adaptive thresholds. Results showed significant differences in porosity values across methods. Otsu, region growing, and K-means clustering excelled under uniform illumination, while Sauvola and Bradley-Roth performed better with uneven illumination. Comparison with X-ray computed tomography (XCT) revealed slightly lower porosity values (2.55 %) than optimized methods (2.73–2.79 %) due to XCT's lower resolution excluding smaller pores. While XCT offers finer pore detection, it limits sample volume and underestimates porosity due to spatial variation. Validation using artificial grayscale images with 5 % porosity confirmed that Otsu, Bradley-Roth, region growing, and Sauvola algorithms produced accurate results. Although tested on a single material system, these methods can be adapted to others with optimization. In conclusion, given XCT's high computational and time costs, this study highlights suitable image processing techniques as cost-effective alternatives for porosity analysis in polymer composites.

Additive manufacturing↗
DOE OSTI2017

Visual Sensing and Image Processing for Error Detection in Laser Metal Wire Deposition

Laser metal deposition with wire (LMD-w) involves feeding metal wire through a nozzle and melting the wire with a high-power laser. With efficient process control, comprising sensing, processing and feedback correction of errors, the technology has a potential of changing the course of manufacturing. However, the limitation most often encountered in LMD is the difficulty in controlling the process. Monitoring and control of metal additive manufacturing processes has been mostly researched on powder-based systems, and has not yet been investigated to much extent on the side of metal wire feed arrangements. This work proposes a method for detecting cavities in a deposited layer in the LMD-w process via optical inspection and processing of images obtained from a high-resolution camera. The aim is to develop an effective sensing module which automatically detects discontinuities in each layer before proceeding to subsequent layers, to reduce part porosity and improve inter-layer bond integrity.

Adediran, Adeola↗
DOE OSTI2026

Image processing pipeline for AI-driven nanoparticle megalibrary characterization

Recent innovations have made it possible to produce megalibraries, millions of structurally and compositionally distinct nanoparticles on a chip. These megalibraries yield vast volumes of data that are impossible to analyze manually, necessitating the development of automated tools. In previous work, we created a binary classification machine learning model to select quality nanoparticle images for downstream analysis. In this work, we show that adding a custom image processing step before training can produce significantly higher-performing models in a fraction of the time and make them more robust to different image noise levels and microscope acquisition settings. The image processing pipeline proposed here effectively cleans raw nanoparticle images, enhances key features, and allows us to use much lower resolution images and simpler neural network model architectures. These features result in higher performance and significant cost savings. Experiments demonstrate superior performance relative to baseline, including an 18.2% improvement in recall and a 13.1% increase in accuracy. Given the high cost of downstream analysis, it is critical to minimize false positives, and our best-performing model reaches a precision of 95.9% and a weighted F-score of 95.1% on an unseen test set. Additionally, model training time is reduced from hours to less than a minute. We also show that, using this custom image processing pipeline, model performance is significantly improved at lower pixel resolutions compared to downsizing alone. We expect that adopting this pipeline for AI-driven automated nanoparticle characterization will allow researchers to rapidly and accurately analyze much greater volumes of data, thereby accelerating materials discovery.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2014

F3D Image Processing and Analysis for Many - and Multi-core Platforms

F3D is written in OpenCL, so it achieve[sic] platform-portable parallelism on modern mutli-core CPUs and many-core GPUs. The interface and mechanims to access F3D core are written in Java as a plugin for Fiji/ImageJ to deliver several key image-processing algorithms necessary to remove artifacts from micro-tomography data. The algorithms consist of data parallel aware filters that can efficiently utilizes[sic] resources and can work on out of core datasets and scale efficiently across multiple accelerators. Optimizing for data parallel filters, streaming out of core datasets, and efficient resource and memory and data managements over complex execution sequence of filters greatly expedites any scientific workflow with image processing requirements. F3D performs several different types of 3D image processing operations, such as non-linear filtering using bilateral filtering and/or median filtering and/or morphological operators (MM). F3D gray-level MM operators are one-pass constant time methods that can perform morphological transformations with a line-structuring element oriented in discrete directions. Additionally, MM operators can be applied to gray-scale images, and consist of two parts: (a) a reference shape or structuring element, which is translated over the image, and (b) a mechanism, or operation, that defines the comparisons to be performed between the image and the structuring element. This tool provides a critical component within many complex pipelines such as those for performing automated segmentation of image stacks. F3D is also called a "descendent" of Quant-CT, another software we developed in the past. These two modules are to be integrated in a next version. Further details were reported in: D.M. Ushizima, T. Perciano, H. Krishnan, B. Loring, H. Bale, D. Parkinson, and J. Sethian. Structure recognition from high-resolution images of ceramic composites. IEEE International Conference on Big Data, October 2014.

Source record↗
DOE OSTI2014

F3D Image Processing and Analysis for Many - and Multi-core Platforms

F3D is written in OpenCL, so it achieve[sic] platform-portable parallelism on modern mutli-core CPUs and many-core GPUs. The interface and mechanims to access F3D core are written in Java as a plugin for Fiji/ImageJ to deliver several key image-processing algorithms necessary to remove artifacts from micro-tomography data. The algorithms consist of data parallel aware filters that can efficiently utilizes[sic] resources and can work on out of core datasets and scale efficiently across multiple accelerators. Optimizing for data parallel filters, streaming out of core datasets, and efficient resource and memory and data managements over complex execution sequence of filters greatly expedites any scientific workflow with image processing requirements. F3D performs several different types of 3D image processing operations, such as non-linear filtering using bilateral filtering and/or median filtering and/or morphological operators (MM). F3D gray-level MM operators are one-pass constant time methods that can perform morphological transformations with a line-structuring element oriented in discrete directions. Additionally, MM operators can be applied to gray-scale images, and consist of two parts: (a) a reference shape or structuring element, which is translated over the image, and (b) a mechanism, or operation, that defines the comparisons to be performed between the image and the structuring element. This tool provides a critical component within many complex pipelines such as those for performing automated segmentation of image stacks. F3D is also called a "descendent" of Quant-CT, another software we developed in the past. These two modules are to be integrated in a next version. Further details were reported in: D.M. Ushizima, T. Perciano, H. Krishnan, B. Loring, H. Bale, D. Parkinson, and J. Sethian. Structure recognition from high-resolution images of ceramic composites. IEEE International Conference on Big Data, October 2014.

Source record↗
DOE OSTI2022

Facile alignment estimation in carbon nanotube films using image processing

Whether a macroscopic assembly of carbon nanotubes can exhibit the one-dimensional properties expected from individual nanotubes critically depends on how well the nanotubes are aligned inside the assembly. Therefore, a simple and accurate method for assessing the degree of alignment is desired for the rapid characterization of carbon nanotube films and fibers. Here, we present an end-to-end solution for determining the global and local spatial orientation of carbon nanotubes in films within a short amount of time using a fast, precise, and economical approach based on an image processing method applied to scanning electron microscopy images. Further, we first use Laplacian edge enhancement filtering for improving the appearance of edge regions, which is followed by image partitioning into multiple blocks to capture the nanoscale orientation characteristics and total variation-based image decomposition of these image blocks. We then perform a 2D-fast Fourier transform on the image decomposed textural components of these edge-enhanced image blocks to determine the orientation distribution, which is utilized to estimate the 2D nematic order parameter. To show the effectiveness of our method, we corroborated our results against results obtained with other state-of-the-art image processing and experimental techniques.

2D-FFT↗
DOE OSTI2018

Applying image processing methods to study hydrodynamic characteristics in a rectangular spouted bed

This paper presents systematic image processing methods for investigating hydrodynamic characteristics in a rectangular spouted bed (30.2 mm × 101.6 mm). The objective is to provide a non-intrusive technique to determine the bed expansion ratio, the jet height and the fountain height due to their significant influence on the hydrodynamics of the entire bed. A high-speed video camera is used in the experiment with Geldart D particles as the bed material. After the image acquisition, image denoising, thresholding, logical and mask operations are applied correspondingly for the boundary discrimination, based on which an algorithm has been developed to determine the bed height successfully. With the discriminated boundary, morphological operations viz. erosion and dilation have been used to accurately determine the jet height and the fountain height. The feasibility of the technique is proven through profiles of bed expansion ratio, jet height and fountain height under three initial bed heights (0.0762 m, 0.102 m, and 0.127 m) for two different nozzle sizes (0.0096 m and 0.0127 m). Depending on the determination of the hydrodynamic characteristics, different flow regimes can be distinguished comprehensively through profiles of the bed expansion ratio and bed pressure drop in combination with the standard deviation of jet/fountain height. The proposed flow regime discrimination verifies the feasibility and effectiveness of the image processing methods in assessing the solids behavior in the spouted beds. Results about the variation of the jet height and the fountain height under current operating conditions as determined by the new technique are also discussed.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Wafer-scale waveguide sidewall roughness scattering loss characterization by image processing

Photonic integrated circuits (PICs) are vital for developing affordable, high-performance optoelectronic devices that can be manufactured at an industrial scale, driving innovation and efficiency in various applications. Optical loss of modes in thin film waveguides and devices is a critical measure of their performance. Thin film growth, lithography, masking, and etching processes are imperfect processes that introduce significant sidewall and top-surface roughness and cause dominating optical losses in waveguides and photonic structures. This roughness, as perturbations couple light from guided to far-field radiation modes, leads to scattering losses that can be estimated from theoretical models. Typically, with UV-based lithography, sidewall roughness is significantly larger than wafer-top surface roughness. Atomic force microscopy (AFM) imaging measurement gives a 3D and high-resolution roughness profile, but the measurement is inconvenient, costly, and unscalable for large-scale PICs and at wafer-scale. Here, we evaluate the sidewall roughness profile based on 2D high-resolution scanning electron microscope (SEM) imaging. We characterized the loss on two homemade nitride and oxide films on 3-inch silicon wafers with 12 waveguide devices on each and correlated the scattering loss estimated from a 2D image-based sidewall profile and theoretical Payne model. The lowest loss of guided fundamental transverse electric (TE 0 ) mode is found at 0.075 dB/cm at 633 nm across 24 devices, a record at visible wavelength. Our work shows 100% success (edge continuity span exceeding 95% of image width/height) in edge detection in image processing of all images to estimate autocorrelation function and optical mode loss. These demonstrations offer valuable insights into waveguide sidewall roughness and a comparison of experimental and 2D SEM image processing based loss estimations with applications in loss characterization at wafer-scale PICs.

Khurana, Mohit (ORCID:0000000207898672)↗
DOE OSTI2022

A New Workflow of X-ray CT Image Processing and Data Analysis of Structural Features in Rock Using Open-Source Software

X-ray computed tomography (CT) images of rock specimens often contain artifacts which must be corrected before scientific analyses are performed. Here, we present a new workflow of automated image processing to utilize poor-quality X-ray CT scan images. The workflow runs on the open-source image analysis software and efficiently separates desired features from low-contrast scanned images. The new workflow is a two-step technique using contrast enhancement and automated feature segmentation to generate noise-free binary images. The results of binary images using the proposed workflow and using a conventional thresholding technique are analyzed to show the quality of the proposed method. The paper also presents a workflow of estimating the structural geometries of features in two and three dimensions. The results of the structural feature analyses and computational time were compared between the open-source (ImageJ) and commercial image analysis software (Bruker Computed Tomography Analyzer). The commercial software was more computationally efficient, but the task-specific macros in open-source software enabled the user-desired automation in image processing and data extraction of desired structural features of comparable quality.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Influence of Standard Image Processing of 3D X-ray Microscopy on Morphology, Topology and Effective Properties

Estimating porous media properties is a vital component of geosciences and the physics of porous media. Until now, imaging techniques have focused on methodologies to match image-derived flows or geomechanical parameters with experimentally identified values. Less emphasis has been placed on the compromise between image processing techniques and the consequences on topological and morphological characteristics and on computed properties such as permeability. The effects of some of the most popular image processing techniques (filtering and segmentation) available in open source on 3D X-ray Microscopy (micro-XRM) images are qualitatively and quantitatively discussed. We observe the impacts of various filters such as erosion-dilation and compare the efficiency of Otsu’s method of thresholding and the machine-learning-based software Ilastik for segmentation.

58 GEOSCIENCES↗
DOE OSTI2021

Image processing methods for Rayleigh scattering measurements of diesel spray mixing at high repetition rate

This work describes the diagnostic implementation and image processing methods to quantitatively measure diesel spray mixing injected into a high-pressure, high-temperature environment. We used a high-repetition-rate pulse-burst laser developed in-house, a high-speed CMOS camera, and optimized the optical configuration to capture Rayleigh scattering images of the vaporized fuel jets inside a constant volume chamber. The experimental installation was modified to reduce reflections and flare levels to maximize the images’ signal-to-noise ratios by anti-reflection coatings on windows and surfaces, as well as series of optical baffles. Because of the specificities of the high-speed system, several image processing techniques had to be developed and implemented to provide quantitative fuel concentration measurements. We find that these methods involve various correction procedures such as camera linearity, laser intensity fluctuation, dynamic background flare, as well as beam-steering effects. Image inpainting was also applied to correct the Rayleigh scattering signal from large scatterers (e.g. particulates). The experiments demonstrate that applying planar laser Rayleigh scattering at high repetition rate to quantitatively resolve the mixing of fuel and ambient gases in diesel jets is challenging, but possible. The thorough analysis of the experimental uncertainty and comparisons to past data prove that such measurements can be accurate, whilst providing valuable information about the mixing processes of high-pressure diesel jets.

47 OTHER INSTRUMENTATION↗
DOE OSTI2014

SU-E-I-12: Characterization of Edge Effects in a Commercial Low-Dose Image Processing System

Purpose: Minimizing radiation dose while preserving image quality is critical in fluoroscopic imaging. One recent development is a noise reduction system (Allura Clarity) offered by Philips. Others have reported approximately 50% reduction in air kerma when using Clarity. These studies, however, provide only a cursory look at how the Clarity system affects image quality. The purpose of this work was to evaluate the effect of Clarity on the appearance of high-frequency image information. Methods: A lead attenuator with a smooth edge was imaged on two Philips Allura FD20 detectors: one with Clarity and one without. The edge was positioned in the center of the field of view and images were obtained under the following conditions: 40cm and 11cm fields of view, single shot and continuous fluoroscopy modes, and using abdomen and cardiac protocols, for a total of sixteen imaging conditions. Profiles were drawn perpendicular to the edge across 80% of its length, averaged to reduce noise, normalized to the maximum pixel value, and plotted as a function of distance. Results: For all single-shot acquisitions and most fluoroscopic images, overshoot of the edge was observed. This effect was more substantial for single-shot acquisitions (∼20%) than for fluoroscopic images (∼50%). For fluoroscopic acquisition, the overshoot decayed more quickly with the Clarity system. However, the system with Clarity introduced a ringing effect for both single-shot and fluoroscopic images that is not present on the non-Clarity system. Conclusion: Previous reports have demonstrated a substantial dose reduction when using Clarity but the impact this has on image appearance has not been characterized. One demonstrated difference is the change in appearance of high-frequency image information. It remains to be determined whether this effect may impact clinical images adversely.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Mapping Benthic Habitat Conditions and Seafloor Deposits using Sediment Profile Imaging and a Semiautomated Image Processing System

Objectives/Scope: Sediment profile imaging (SPI) technology characterizes in situ physical, geochemical, and biological seafloor features. SPI was required by Mexico’s Agency for Safety, Energy, and Environment in 2017 for environmental baseline surveys (EBS) of oil lease blocks in the southern Gulf of Mexico. Because of its ability to provide information on benthic community health and the distribution of very thin (down to the centimeter scale) layers of deposited materials, SPI technology is highly effective for mapping drill cuttings or drilling muds released around wellheads during exploration or production and documenting their ecological impacts. SPI technology has not been widely used in the oil and gas industry for EBS or other monitoring activities. A primary objective of this work is to improve the transparency and consistency of the SPI data generation and data management process. Methods, Procedures, Process: The SPI camera works like an inverted periscope and obtains an undisturbed 21x15-cm cross-sectional image of the upper sediment column. The camera is internally powered and can be deployed rapidly from a standard winch in depths to 4,000 m. Many stations can be sampled in a single day by “pogo-sticking” across a survey area. Sediment grain size, penetration depth, surface boundary roughness, natural and anthropogenic depositional layers, depth of the oxidized surface sediment layer, maximum biogenic mixing depth, and infaunal successional stage can be directly measured at sea or immediately following the cruise. Final SPI data sets can be provided within a few weeks of the survey. Results, Observations, Conclusions: Details on the features measured in SPI images and the underlying interpretive paradigms are presented. To standardize the SPI data generation process, Integral Consulting Inc. has developed 1) a semiautomated image analysis platform, and 2) a SPI data-specific database architecture that allows both numerical and non-numerical metrics to be incorporated into a standard database structure. An integrated, software-based SPI analysis platform has been developed that imports image files and metadata and provides a graphical user interface. The software automatically stores the data, which can then be reviewed for quality assurance, plotted, statistically analyzed, and mapped or exported to other platforms (e.g., Esri ArcGIS©) for further evaluation. Image processing algorithms have been developed using a combination of open-source and commercially available software packages (e.g., MATLAB® and OpenCV) to automatically quantify key parameters. Novel/Additive Information: SPI technology’s underutilization in the oil and gas industry may be in part due to a lack of standardization in the measurement of basic features in SPI images. A primary objective of this work is to develop a streamlined, standardized, and transparent process for generating and managing SPI data.

02 PETROLEUM↗