Search NASA⌕ Search

SEARCH · Search NASA

Results for “Image Analysis”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

IBEX: An open infrastructure software platform to facilitate collaborative work in radiomics

Purpose: Radiomics, which is the high-throughput extraction and analysis of quantitative image features, has been shown to have considerable potential to quantify the tumor phenotype. However, at present, a lack of software infrastructure has impeded the development of radiomics and its applications. Therefore, the authors developed the imaging biomarker explorer (IBEX), an open infrastructure software platform that flexibly supports common radiomics workflow tasks such as multimodality image data import and review, development of feature extraction algorithms, model validation, and consistent data sharing among multiple institutions. Methods: The IBEX software package was developed using the MATLAB and C/C++ programming languages. The software architecture deploys the modern model-view-controller, unit testing, and function handle programming concepts to isolate each quantitative imaging analysis task, to validate if their relevant data and algorithms are fit for use, and to plug in new modules. On one hand, IBEX is self-contained and ready to use: it has implemented common data importers, common image filters, and common feature extraction algorithms. On the other hand, IBEX provides an integrated development environment on top of MATLAB and C/C++, so users are not limited to its built-in functions. In the IBEX developer studio, users can plug in, debug, and test new algorithms, extending IBEX’s functionality. IBEX also supports quality assurance for data and feature algorithms: image data, regions of interest, and feature algorithm-related data can be reviewed, validated, and/or modified. More importantly, two key elements in collaborative workflows, the consistency of data sharing and the reproducibility of calculation result, are embedded in the IBEX workflow: image data, feature algorithms, and model validation including newly developed ones from different users can be easily and consistently shared so that results can be more easily reproduced between institutions. Results: Researchers with a variety of technical skill levels, including radiation oncologists, physicists, and computer scientists, have found the IBEX software to be intuitive, powerful, and easy to use. IBEX can be run at any computer with the windows operating system and 1GB RAM. The authors fully validated the implementation of all importers, preprocessing algorithms, and feature extraction algorithms. Windows version 1.0 beta of stand-alone IBEX and IBEX’s source code can be downloaded. Conclusions: The authors successfully implemented IBEX, an open infrastructure software platform that streamlines common radiomics workflow tasks. Its transparency, flexibility, and portability can greatly accelerate the pace of radiomics research and pave the way toward successful clinical translation.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2008

RHSEG and Subdue: Background and Preliminary Approach for Combining these Technologies for Enhanced Image Data Analysis, Mining and Knowledge Discovery

Under a project recently selected for funding by NASA's Science Mission Directorate under the Applied Information Systems Research (AISR) program, Tilton and Cook will design and implement the integration of the Subdue graph based knowledge discovery system, developed at the University of Texas Arlington and Washington State University, with image segmentation hierarchies produced by the RHSEG software, developed at NASA GSFC, and perform pilot demonstration studies of data analysis, mining and knowledge discovery on NASA data. Subdue represents a method for discovering substructures in structural databases. Subdue is devised for general-purpose automated discovery, concept learning, and hierarchical clustering, with or without domain knowledge. Subdue was developed by Cook and her colleague, Lawrence B. Holder. For Subdue to be effective in finding patterns in imagery data, the data must be abstracted up from the pixel domain. An appropriate abstraction of imagery data is a segmentation hierarchy: a set of several segmentations of the same image at different levels of detail in which the segmentations at coarser levels of detail can be produced from simple merges of regions at finer levels of detail. The RHSEG program, a recursive approximation to a Hierarchical Segmentation approach (HSEG), can produce segmentation hierarchies quickly and effectively for a wide variety of images. RHSEG and HSEG were developed at NASA GSFC by Tilton. In this presentation we provide background on the RHSEG and Subdue technologies and present a preliminary analysis on how RHSEG and Subdue may be combined to enhance image data analysis, mining and knowledge discovery.

Tilton, James C.↗
DOE OSTI2012

ERRATIC JET WOBBLING IN THE BL LACERTAE OBJECT OJ287 REVEALED BY SIXTEEN YEARS OF 7 mm VLBA OBSERVATIONS

We present the results from an ultra-high-resolution 7 mm Very Long Baseline Array study of the relativistic jet in the BL Lacertae object OJ287 from 1995 to 2011 containing 136 total intensity images. Analysis of the image sequence reveals a sharp jet-position-angle swing by >100 Degree-Sign during [2004,2006], as viewed in the plane of the sky, which we interpret as the crossing of the jet from one side of the line of sight to the other during a softer- and longer-term swing of the inner jet. Modulating such long-term swing, our images also show for the first time a prominent erratic wobbling behavior of the innermost {approx}0.4 mas of the jet with fluctuations in position angle of up to {approx}40 Degree-Sign over timescales {approx}2 yr. This is accompanied by highly superluminal motions along non-radial trajectories, which reflect the remarkable non-ballistic nature of the jet plasma on these scales. The erratic nature and short timescales of the observed behavior rule out scenarios such as binary black hole systems, accretion disk precession, and interaction with the ambient medium as possible origins of the phenomenon on the scales probed by our observations, although such processes may cause longer-term modulation of the jet direction. We propose that variable asymmetric injection of the jet flow, perhaps related to turbulence in the accretion disk, coupled with hydrodynamic instabilities leads to the non-ballistic dynamics that causes the observed non-periodic changes in the direction of the inner jet.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Effect of Coaxial Electrode Geometry on the Electric Field Enhancement Factor for a High Voltage Vacuum Gap

We present an experimental analysis of the change in the electric field enhancement factor with varying gap size and penetration depth (P.D) of cathode into anode for a coaxial vacuum gap, diagnosed using Fowler–Nordheim analysis and optical imaging via scanning electron microscope (SEM) and time integrated Digital single lens reflex camera (DSLR). Data were collected on the Coaxial Gap Breakdown Machine (240 A, 25 kV, 150 ns, 0.1 Hz). Experiments using five different gap sizes at nine different P.Ds are compared over runs comprising 50 shots for each case. The results show a strong link between enhancement factor and gap size, with P.D and surface topology. For large gap sizes, 150, 330, and 700 μm, the average enhancement factor value increases with increasing P.D. For smaller gap sizes, 50 and 100 μm, the average enhancement factor decreases with P.D. SEM imaging before and after plasma formation for each gap size allows for quantifying surface finish, microprotrusion growth, average blast diameter, and an estimation of the surface area breakdowns occupy. Time integrated DSLR imaging analysis of the gap at each shot allows for a determination of the distribution of breakdowns about the circumference of the gap for each case tested. Here, the Fowler–Nordheim analysis allows for a quantitative analysis of the surface roughness of all gap sizes tested. Results show that for large gap sizes, the gap geometry and increasing area of breakdown is the main cause for increasing average enhancement factor. For small gap sizes, the dominant driving factor for small average enhancement factors—that subsequently decrease with P.D—is significant changes in surface topology due to an increased number of breakdowns.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Micrometer-resolved film dosimetry using a microscope in microbeam radiation therapy

Purpose: Microbeam radiation therapy (MRT) is a still preclinical tumor therapy approach that uses arrays of a few tens of micrometer wide parallel beams separated by a few 100 μm. The production, measurement, and planning of such radiation fields are a challenge up to now. Here, the authors investigate the feasibility of radiochromic film dosimetry in combination with a microscopic readout as a tool to validate peak and valley doses in MRT, which is an important requirement for a future clinical application of the therapy. Methods: Gafchromic{sup ®} HD-810 and HD-V2 films are exposed to MRT fields at the biomedical beamline ID17 of the European Synchrotron Radiation Facility (ESRF) and are afterward scanned with a microscope. The measured dose is compared with Monte Carlo calculations. Image analysis tools and film handling protocols are developed that allow accurate and reproducible dosimetry. The performance of HD-810 and HD-V2 films is compared and a detailed analysis of the resolution, noise, and energy dependence is carried out. Measurement uncertainties are identified and analyzed. Results: The dose was measured with a resolution of 5 × 1000 μm{sup 2} and an accuracy of 5% in the peak and between 10% and 15% in the valley region. As main causes for dosimetry uncertainties, statistical noise, film inhomogeneities, and calibration errors were identified. Calibration errors strongly increase at low doses and exceeded 3% for doses below 50 and 70 Gy for HD-V2 and HD-810 films, respectively. While the grain size of both film types is approximately 2 μm, the statistical noise in HD-V2 is much higher than in HD-810 films. However, HD-810 films show a higher energy dependence at low photon energies. Conclusions: Both film types are appropriate for dosimetry in MRT and the microscope is superior to the microdensitometer used before at the ESRF with respect to resolution and reproducibility. However, a very careful analysis of the image data is required. Dosimetry at low photon energies should be performed with great caution due to the energy sensitivity of the films. In this respect, HD-V2 films showed to have an advantage over HD-810 films. However, HD-810 films have a lower statistical noise level. When a higher resolution is required, e.g., for the dosimetry of pencil beam irradiations, noise may render HD-V2 films inapplicable.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2000

Spectral analysis of gappy images with application to Sea WiFS data

For various reasons, such as the presence of clouds, satellite orbit configuration, data sampling pattern, remote sensing instruments yield irregularly spaced or gappy/patchy measurements. To obviate these problems, we use original, non-gridded satellite data and construct a spatio-temporal (or just spatial) autocorrelation function of the field's variation.

image analysis spectral analysis Sea WiFS↗
NASA NTRS2009

Adaptive Morphological Feature-Based Object Classifier for a Color Imaging System

Utilizing a Compact Color Microscope Imaging System (CCMIS), a unique algorithm has been developed that combines human intelligence along with machine vision techniques to produce an autonomous microscope tool for biomedical, industrial, and space applications. This technique is based on an adaptive, morphological, feature-based mapping function comprising 24 mutually inclusive feature metrics that are used to determine the metrics for complex cell/objects derived from color image analysis. Some of the features include: Area (total numbers of non-background pixels inside and including the perimeter), Bounding Box (smallest rectangle that bounds and object), centerX (x-coordinate of intensity-weighted, center-of-mass of an entire object or multi-object blob), centerY (y-coordinate of intensity-weighted, center-of-mass, of an entire object or multi-object blob), Circumference (a measure of circumference that takes into account whether neighboring pixels are diagonal, which is a longer distance than horizontally or vertically joined pixels), . Elongation (measure of particle elongation given as a number between 0 and 1. If equal to 1, the particle bounding box is square. As the elongation decreases from 1, the particle becomes more elongated), . Ext_vector (extremal vector), . Major Axis (the length of a major axis of a smallest ellipse encompassing an object), . Minor Axis (the length of a minor axis of a smallest ellipse encompassing an object), . Partial (indicates if the particle extends beyond the field of view), . Perimeter Points (points that make up a particle perimeter), . Roundness [(4(pi) x area)/perimeter(squared)) the result is a measure of object roundness, or compactness, given as a value between 0 and 1. The greater the ratio, the rounder the object.], . Thin in center (determines if an object becomes thin in the center, (figure-eight-shaped), . Theta (orientation of the major axis), . Smoothness and color metrics for each component (red, green, blue) the minimum, maximum, average, and standard deviation within the particle are tracked. These metrics can be used for autonomous analysis of color images from a microscope, video camera, or digital, still image. It can also automatically identify tumor morphology of stained images and has been used to detect stained cell phenomena (see figure).

McDowell, Mark↗
DOE OSTI2011

BioSig-Air-Force

1) Configured servers: In coordination with the INSIGHT team, a hardware configuration was selected. Two nodes were purchased, configured, and shipped with compatible OS and database installation. The servers have been stress tested for reliability as they use leading edge technologies. Each node has two CPUs and 12 cores per CPU with maximum onboard memory for high performance. 2) LIM and Experimental module: The original BioSig system was developed for cancer research. Accordingly, the LIM system its corresponding web pages are being modified to facilitate (i) pathogene-donor interactions, (ii) media composition, (iii) chemical and siRNA plate configurations. The LIM system has been redesigned. The revised system allows design of new media and tracking it from lot-to-lot so that variations in the phenotypic responses can be tracked to a specific media and lot number. Similar associations are also possible with other experimental factors (e.g., donor-pathoge, siRNA, and chemical). Furthermore, the design of the experimental variables has also been revised to (i) interact with the newly developed LIM system, (ii) simplify experimental specifications, and (iii) test for potential operator's error during the data entry. Part of the complication has been due to the handshake between multiple teams that provide the small molecule plates and the team that creates assay plates. Our efforts have focused to harmonize these interactions (e.g., various data formats) so that each assay plate can be mapped to its source so that a correct set of experimental variables can be associated with each image. For example, depending upon the source of the chemical plates, they may have different formats. We have developed a canonical representation that registers SMILES code, for each chemical compound, along with its physiochemical properties. The schema for LIM conjunction with customized Web pages. 3) Import of Images and computed descriptors module: In coordination with the INSIGHT team, policies were designed to route images and computed representation into BioSig. This module (i) examines for completion of image analysis, and imports images, computed masks, and descriptors into BioSig. A database API for efficient retrieval of selection of descriptors (among thousands) was designed and implemented. 4) Computed segmentation masks from external software were imported, boundaries computed, and overlaid on images for quality control.

Source record↗
NASA NTRS1981

The interactive astronomical data analysis facility - image enhancement techniques to Comet Halley

PDP 11/40 computer is at the heart of a general purpose interactive data analysis facility designed to permit easy access to data in both visual imagery and graphic representations. The major components consist of: the 11/40 CPU and 256 K bytes of 16-bit memory; two TU10 tape drives; 20 million bytes of disk storage; three user terminals; and the COMTAL image processing display system. The application of image enhancement techniques to two sequences of photographs of Comet Halley taken in Egypt in 1910 provides evidence for eruptions from the comet's nucleus.

Kinglesmith, D. A., III↗
DOE OSTI2015

SU-E-J-260: Quantitative Image Feature Analysis of Multiphase Liver CT for Hepatocellular Carcinoma (HCC) in Radiation Therapy

Purpose: To identify the effective quantitative image features (radiomics features) for prediction of response, survival, recurrence and metastasis of hepatocellular carcinoma (HCC) in radiotherapy. Methods: Multiphase contrast enhanced liver CT images were acquired in 16 patients with HCC on pre and post radiation therapy (RT). In this study, arterial phase CT images were selected to analyze the effectiveness of image features for the prediction of treatment outcome of HCC to RT. Response evaluated by RECIST criteria, survival, local recurrence (LR), distant metastasis (DM) and liver metastasis (LM) were examined. A radiation oncologist manually delineated the tumor and normal liver on pre and post CT scans, respectively. Quantitative image features were extracted to characterize the intensity distribution (n=8), spatial patterns (texture, n=36), and shape (n=16) of the tumor and liver, respectively. Moreover, differences between pre and post image features were calculated (n=120). A total of 360 features were extracted and then analyzed by unpaired student’s t-test to rank the effectiveness of features for the prediction of response. Results: The five most effective features were selected for prediction of each outcome. Significant predictors for tumor response and survival are changes in tumor shape (Second Major Axes Length, p= 0.002; Eccentricity, p=0.0002), for LR, liver texture (Standard Deviation (SD) of High Grey Level Run Emphasis and SD of Entropy, both p=0.005) on pre and post CT images, for DM, tumor texture (SD of Entropy, p=0.01) on pre CT image and for LM, liver (Mean of Cluster Shade, p=0.004) and tumor texture (SD of Entropy, p=0.006) on pre CT image. Intensity distribution features were not significant (p>0.09). Conclusion: Quantitative CT image features were found to be potential predictors of the five endpoints of HCC in RT. This work was supported in part by the National Cancer Institute Grant R01CA172638.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

The persistent shadow of the supermassive black hole of M 87. I. Observations, calibration, imaging, and analysis

In April 2019, the Event Horizon Telescope (EHT) Collaboration reported the first-ever event-horizon-scale images of a black hole, resolving the central compact radio source in the giant elliptical galaxy M 87. These images reveal a ring with a southerly brightness distribution and a diameter of ∼42 μas, consistent with the predicted size and shape of a shadow produced by the gravitationally lensed emission around a supermassive black hole. These results were obtained as part of the April 2017 EHT observation campaign, using a global very long baseline interferometric radio array operating at a wavelength of 1.3 mm. Here, we present results based on the second EHT observing campaign, taking place in April 2018 with an improved array, wider frequency coverage, and increased bandwidth. In particular, the additional baselines provided by the Greenland telescope improved the coverage of the array. Multiyear EHT observations provide independent snapshots of the horizon-scale emission, allowing us to confirm the persistence, size, and shape of the black hole shadow, and constrain the intrinsic structural variability of the accretion flow. We have confirmed the presence of an asymmetric ring structure, brighter in the southwest, with a median diameter of 43.3−3.1+1.5 μas. The diameter of the 2018 ring is remarkably consistent with the diameter obtained from the previous 2017 observations. On the other hand, the position angle of the brightness asymmetry in 2018 is shifted by about 30° relative to 2017. The perennial persistence of the ring and its diameter robustly support the interpretation that the ring is formed by lensed emission surrounding a Kerr black hole with a mass ∼6.5 × 109 M⊙. The significant change in the ring brightness asymmetry implies a spin axis that is more consistent with the position angle of the large-scale jet.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Characterization of mechanical properties and deformation behavior of highly irradiated 316L stainless steel from target modules at the Spallation Neutron Source using digital image correlation analysis

Tensile specimens with irradiation doses ranging from approximately 0.7 to 7.6 displacements per atom (dpa) were fabricated from 316L samples removed from Spallation Neutron Source (SNS) target vessels after service. Tensile tests were performed in conjunction with digital image correlation (DIC) characterization, which permitted detailed analysis of the strain distribution and deformation behavior of the specimen gauge sections during testing. The results show, while considerable hardening (500–700 MPa increase in yield strength) occurred in the 316L SNS target material with increasing dose, appreciable ductility (19–23% total elongation) remained in the material after irradiation to 7.6 dpa in the mixed neutron/proton environment of the SNS target region. The DIC data showed the strain distribution and deformation behavior of all irradiated specimens were nonuniform during testing. Strain localization in the form of deformation bands or “hotspots” was observed during testing, and in some specimens, the bands propagated as deformation waves along the gauge section. The “traveling wave” deformation behavior affected the amount of elongation each specimen achieved, and the scatter in the elongation data was attributed to the large variation in deformation behavior observed among different specimens. Bands of localized strain, or deformation bands, oriented at specific angles were observed in two-dimensional plots of the strain distribution in the gauge sections of some specimens. Overall, this paper describes the experimental procedures, results, and deformation behavior of tensile specimens from SNS targets after service and illustrates the value of DIC characterization during tensile testing of irradiated materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Individual Wave Detection and Tracking within a Rotating Detonation Engine through Computer Vision Object Detection applied to High-Speed Images

Known for their simplistic design and continuous detonation, rotating detonation engines (RDEs) constitute a majority of current pressure gain combustion (PGC) research efforts. Experimental RDE operation times have been continuously extended through the use of rig cooling techniques. As the window of observable behavior is expanded, and as the technology matures toward eventual integration within gas turbines, monitoring techniques must evolve to better match industrial diagnostics. High-speed image analysis techniques prove useful to capture and evaluate the unsteady detonation behavior within the RDE. Traditional image analysis techniques, however, require extensive processing times which prohibit simultaneous monitoring. To better address this problem, a computer vision object detection methodology is proposed to quickly detect individual detonation waves within a single down-axis image. Detonation waves are detected in individual images by the implemented computer vision method You Only Look Once (YOLO) object detection network. In order to detect detonation waves, the network must first be trained using RDE images of interest, for which each required phase of network development is outlined. Detection of waves is improved through proper treatment of the collected image set, variation of Intersection over Union (IoU) and confidence thresholding, and through a parametric study of annotation dimensions. Each detected wave is described by its location and rotational direction, and locations are tracked to calculate wave velocity across each frame, leading to a timestep resolution of 20 µs. Wave velocities are also calculated through a series of frames, leading to a suitable average velocity estimation using as few as 10 frames. Uncertainty analysis accounting for variation in camera framerate, pixel width and annotation centroid locations estimates a total uncertainty of ±4.3% for velocity calculations, using the smallest annotation boxes. This method offers great reductions in processing times, as a step toward real-time monitoring of detonation waves within an RDE. Improving on previous studies, this technique is impartial to wave modes not included in the original training set and calculates wave velocities independent of high-speed pressure data. The ability to isolate waves within predicted bounding boxes will likely facilitate analysis of pixel intensity variation as an estimation of wave strength in future work.

Johnson, Kristyn↗
DOE OSTI2021

Individual Wave Detection and Tracking within a Rotating Detonation Engine through Computer Vision Object Detection applied to High-Speed Images

Known for their simplistic design and continuous detonation, rotating detonation engines (RDEs) constitute a majority of current pressure gain combustion (PGC) research efforts. Experimental RDE operation times have been continuously extended through the use of rig cooling techniques. As the window of observable behavior is expanded, and as the technology matures toward eventual integration within gas turbines, monitoring techniques must evolve to better match industrial diagnostics. High-speed image analysis techniques prove useful to capture and evaluate the unsteady detonation behavior within the RDE. Traditional image analysis techniques, however, require extensive processing times which prohibit simultaneous monitoring. To better address this problem, a computer vision object detection methodology is proposed to quickly detect individual detonation waves within a single down-axis image. Detonation waves are detected in individual images by the implemented computer vision method You Only Look Once (YOLO) object detection network. In order to detect detonation waves, the network must first be trained using RDE images of interest, for which each required phase of network development is outlined. Detection of waves is improved through proper treatment of the collected image set, variation of Intersection over Union (IoU) and confidence thresholding, and through a parametric study of annotation dimensions. Each detected wave is described by its location and rotational direction, and locations are tracked to calculate wave velocity across each frame, leading to a timestep resolution of 20 µs. Wave velocities are also calculated through a series of frames, leading to a suitable average velocity estimation using as few as 10 frames. Uncertainty analysis accounting for variation in camera framerate, pixel width and annotation centroid locations estimates a total uncertainty of ±4.3% for velocity calculations, using the smallest annotation boxes. This method offers great reductions in processing times, as a step toward real-time monitoring of detonation waves within an RDE. Improving on previous studies, this technique is impartial to wave modes not included in the original training set and calculates wave velocities independent of high-speed pressure data. The ability to isolate waves within predicted bounding boxes will likely facilitate analysis of pixel intensity variation as an estimation of wave strength in future work.

Johnson, Kristyn↗
DOE OSTI2016

Reference tissue normalization in longitudinal 18F-florbetapir positron emission tomography of late mild cognitive impairment

Background: Semiquantitative methods such as the standardized uptake value ratio (SUVR) require normalization of the radiotracer activity to a reference tissue to monitor changes in the accumulation of amyloid-β (Aβ) plaques measured with positron emission tomography (PET). The objective of this study was to evaluate the effect of reference tissue normalization in a test–retest 18F-florbetapir SUVR study using cerebellar gray matter, white matter (two different segmentation masks), brainstem, and corpus callosum as reference regions. Methods: We calculated the correlation between 18F-florbetapir PET and concurrent cerebrospinal fluid (CSF) Aβ1–42 levels in a late mild cognitive impairment cohort with longitudinal PET and CSF data over the course of 2 years. In addition to conventional SUVR analysis using mean and median values of normalized brain radiotracer activity, we investigated a new image analysis technique—the weighted two-point correlation function (wS2)—to capture potentially more subtle changes in Aβ-PET data. Results: Compared with the SUVRs normalized to cerebellar gray matter, all cerebral-to-white matter normalization schemes resulted in a higher inverse correlation between PET and CSF Aβ1–42, while the brainstem normalization gave the best results (high and most stable correlation). Compared with the SUVR mean and median values, the wS2 values were associated with the lowest coefficient of variation and highest inverse correlation to CSF Aβ1–42 levels across all time points and reference regions, including the cerebellar gray matter. Conclusions: The selection of reference tissue for normalization and the choice of image analysis method can affect changes in cortical 18F-florbetapir uptake in longitudinal studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Conditional random fields for pattern recognition applied to structured data

Pattern recognition uses measurements from an input domain, X, to predict their labels from an output domain, Y. Image analysis is one setting where one might want to infer whether a pixel patch contains an object that is “manmade” (such as a building) or “natural” (such as a tree). Suppose the label for a pixel patch is “manmade”; if the label for a nearby pixel patch is then more likely to be “manmade” there is structure in the output domain that can be exploited to improve pattern recognition performance. Modeling P(X) is difficult because features between parts of the model are often correlated. Therefore, conditional random fields (CRFs) model structured data using the conditional distribution P(Y|X = x), without specifying a model for P(X), and are well suited for applications with dependent features. This paper has two parts. First, we overview CRFs and their application to pattern recognition in structured problems. Our primary examples are image analysis applications in which there is dependence among samples (pixel patches) in the output domain. Second, we identify research topics and present numerical examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

MR texture analysis: potential imaging biomarker for predicting the chemotherapeutic response of patients with colorectal liver metastases

Purpose: The purpose of the study was to determine whether the pre-treated MR texture features of colorectal liver metastases (CRLMs) are predictive of therapeutic response after chemotherapy. Methods: The study included twenty-six consecutive patients (a total of 193 liver metastasis) with unrespectable CRLMs at our institution from August 2014 to February 2016. Lesions were categorized into either responding group or non-responding group according to changes in size. Texture analysis was quantified on T2-weighted images by two radiologists with consensus on regions of interest which were manually drawn on the largest cross-sectional area of the lesions. Five histogram features (mean, variance, skewness, kurtosis, and entropy{sup 1}) and five gray level co-occurrence matrix features (GLCM; angular second moment (ASM), entropy{sup 2}, contrast, correlation, and inverse difference moment (IDM)) were extracted. The texture parameters were statistically analyzed to identify the differences between the two groups, and the potential predictive parameters to differentiate the responding group from the non-responding group were subsequently tested using multivariable logistic regression analysis. Results: A total of 107 responding and 86 non-responding lesions were evaluated. A higher variance, entropy{sup 1}, contrast, entropy{sup 2} and a lower ASM, correlation, IDM were independently (P < 0.05) associated with a good response to chemotherapy with the areas under the ROC curves (AUCs) of 0.602–0.784. Variance (P < 0.001) and ASM (P = 0.001) remained potential predictive values to discriminate responding lesions from non-responding lesions when tested using multivariable logistic regression analysis. The highest AUC of the predictors from the association of variance and ASM was 0.814. Conclusion: MR texture features on pre-treated T2 images have the potential to predict the therapeutic response of colorectal liver metastases.

62 RADIOLOGY AND NUCLEAR MEDICINE↗