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At least 973 records · Page 54

Two and Three Dimensional near Infrared Subcutaneous Structure Imager Using Real Time Nonlinear Video Processing

An imager is provided for viewing subcutaneous structures. In an embodiment of the invention, the imager includes a camera configured to generate a video frame, and an adaptive nonlinear processor. The adaptive nonlinear processor is configured to adjust a signal of the video frame below a first threshold to a maximum dark level and to adjust the signal of the video frame above a second threshold to a maximum light level. The imager further includes a display, configured to display the processed video frame.

Krasowski, Michael J↗

Skipper-in-CMOS: Nondestructive Readout With Subelectron Noise Performance for Pixel Detectors

The Skipper-in-CMOS image sensor integrates the nondestructive readout capability of skipper charge coupled devices (Skipper-CCDs) with the high conversion gain of a pinned photodiode (PPD) in a CMOS imaging process while taking advantage of in-pixel signal processing. This allows both single photon counting as well as high frame rate readout through highly parallel processing. The first results obtained from a ${15} \times {15}~\mu $ m2 pixel cell of a Skipper-in-CMOS sensor fabricated in Tower Semiconductor’s commercial 180-nm CMOS image sensor process are presented. Measurements confirm the expected reduction of the readout noise with the number of samples down to deep subelectron noise of $0.15\text {e}^ - $ , demonstrating the charge transfer operation from the PPD and the single photon counting operation when the sensor is exposed to light. This article also discusses new testing strategies employed for its operation and characterization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Progress Update on Iterative Reconstruction of Neutron Tomographic Images

This report satisfies the fiscal year 2016 technical deliverable to report on progress in development of fast iterative reconstruction algorithms for project OR16-3DTomography-PD2Jb, "3D Tomography and Image Processing Using Fast Neutrons." This project has two overall goals. The first of these goals is to extend associated-particle fast neutron transmission and, particularly, induced-reaction tomographic imaging algorithms to three dimensions. The second of these goals is to automatically segment the resultant tomographic images into constituent parts, and then extract information about the parts, such as the class of shape and potentially shape parameters. This report addresses of the component of the project concerned with three-dimensional (3D) image reconstruction.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Machine vision guided sensor positioning system for leaf temperature assessment

A sensor positioning system was developed for monitoring plants' well-being using a non-contact sensor. Image processing algorithms were developed to identify a target region on a plant leaf. A novel algorithm to recover view depth was developed by using a camera equipped with a computer-controlled zoom lens. The methodology has improved depth recovery resolution over a conventional monocular imaging technique. An algorithm was also developed to find a maximum enclosed circle on a leaf surface so the conical field-of-view of an infrared temperature sensor could be filled by the target without peripheral noise. The center of the enclosed circle and the estimated depth were used to define the sensor 3-D location for accurate plant temperature measurement.

NASA Discipline Life Support Systems↗

Measuring Airflow With Digital Holographic Interferometry

Digital image-processing system assists in analysis of holographic interferometric images of flow about airfoil. Operating semiautomatically, identifies, counts, and labels interference fringes, then processes distances between fringes into distribution of pressure on surface of airfoil. Yields data on pressure faster than manual image-analyses techniques, and data compares favorably with those obtained by manual analysis and by probe measurements of pressure. Uses computer programs evaluating interferograms along straight or curved lines represented by polygon segments. User prompted for such inputs as reference points and fringe number. Digitizes holographic interferograms of flow and processes them into data of pressure or other properties of flow.

Torres, Francisco J.↗

Characteristics of a New X-Ray Imaging System for Interventional Procedures: Improved Image Quality and Reduced Radiation Dose

PurposeTo compare image quality and radiation exposure between a new angiographic imaging system and the preceding generation system during uterine artery embolization (UAE).Materials and MethodsIn this retrospective, IRB-approved two-arm study, 54 patients with symptomatic uterine fibroids were treated with UAE on two different angiographic imaging systems. The new system includes optimized acquisition parameters and real-time image processing algorithms. Air kerma (AK), dose area product (DAP) and acquisition time for digital fluoroscopy (DF) and digital subtraction angiography (DSA) were recorded. Body mass index was noted as well. DF image quality was assessed objectively by image noise measurements. DSA image quality was rated by two blinded, independent readers on a four-rank scale. Statistical differences were assessed with unpaired t tests and Wilcoxon rank-sum tests.ResultsThere was no significant difference between the patients treated on the new (n = 36) and the old system (n = 18) regarding age (p = 0.10), BMI (p = 0.18), DF time (p = 0.35) and DSA time (p = 0.17). The new system significantly reduced the cumulative AK and DAP by 64 and 72%, respectively (median 0.58 Gy and 145.9 Gy*cm{sup 2} vs. 1.62 Gy and 526.8 Gy*cm{sup 2}, p < 0.01 for both). Specifically, DAP for DF and DSA decreased by 59% (75.3 vs. 181.9 Gy*cm{sup 2}, p < 0.01) and 78% (67.6 vs. 312.2 Gy*cm{sup 2}, p < 0.01), respectively. The new system achieved a significant decrease in DF image noise (p < 0.01) and a significantly better DSA image quality (p < 0.01).ConclusionsThe new angiographic imaging system significantly improved image quality and reduced radiation exposure during UAE procedures.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Generalized Quantum Convolution for Multidimensional Data

The convolution operation plays a vital role in a wide range of critical algorithms across various domains, such as digital image processing, convolutional neural networks, and quantum machine learning. In existing implementations, particularly in quantum neural networks, convolution operations are usually approximated by the application of filters with data strides that are equal to the filter window sizes. One challenge with these implementations is preserving the spatial and temporal localities of the input features, specifically for data with higher dimensions. In addition, the deep circuits required to perform quantum convolution with a unity stride, especially for multidimensional data, increase the risk of violating decoherence constraints. In this work, we propose depth-optimized circuits for performing generalized multidimensional quantum convolution operations with unity stride targeting applications that process data with high dimensions, such as hyperspectral imagery and remote sensing. We experimentally evaluate and demonstrate the applicability of the proposed techniques by using real-world, high-resolution, multidimensional image data on a state-of-the-art quantum simulator from IBM Quantum.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Review of coherent optical data processing

The fundamental features of coherent optical data processing systems are reviewed for potential application to image processing. The application of optical spatial filtering to image mapping and cloud motion is discussed.

Vanderlugt, A.↗

Unlocking the Mysteries of the Moon’s Shadowed Regions

The Moon poles host large quantities of water-ice deposits in the permanently shadowed regions (PSRs), which are vital for enabling sustainable human space exploration, making these regions high-priority targets for upcoming Artemis missions [1]. Unfortunately, today, the best available orbital lunar imagery [2, 3] lacks the meter-scale resolution and signal needed to understand the geomorphology and trafficability of PSRs, complicating the planning and execution of future missions seeking to explore PSRs. We have developed an image enhancement tool called HORUS (Hyper-effective nOise Removal Unet Software) [4, 5], designed to enhance LRO Narrow-Angle Camera (NAC) optical low-light imagery of permanently shadowed regions by effectively removing the CCD-related, photon, and other residual noises that corrupt the images. The tool is composed of two deep learning neural networks trained on environmental metadata and real and synthetic imagery, the latter generated by a physical noise model (LROC). We demonstrated that HORUS effectively produces low-noise, high-resolution images (~1.5m/px), achieving a 5 to 10x improvement over existing long-exposure images of PSRs. HORUS allows scientists and engineers to identify geomorphic features (e.g., craters and boulders) in shadowed regions as small as 3 meters across as well as to peek inside of small shadowed regions, for the first time. The tool was deployed and thoroughly validated for NASA's VIPER mission [6], where it was applied to 20 candidate target regions across the lunar South Pole. Additionally, we conducted different approaches to validate the resulting HORUS-processed images. With HORUS denoised images, VIPER scientists can increase their confidence on what surface features (previously unseen) exist in the shadowed regions, helping them plan rover traverses more safely and efficiently (e.g., Fig. 1) In this manuscript, we will describe how VIPER scientists are utilizing HORUS denoised images to extract new information from the terrain and increase their confidence in what surface features exist in the shadowed regions. In combination with other high-resolution images and digital elevation maps, HORUS images are helping the team analyze potential lading and science sites, as well as planning traverses more safely and efficiently (e.g., Fig. 1). Additionally, we will describe how HORUS tool unlocks a broad range of scientific and exploration applications to other Artemis and CPLS missions to the lunar poles, including (but not limited to) geomorphic analysis, change detection, surface hazard detection, and terrain relative navigation.

artificial intelligence↗

SU-F-T-114: A Novel Anatomically Predictive Extension Model of Computational Human Phantoms for Dose Reconstruction in Retrospective Epidemiological Studies of Second Cancer Risks in Radiotherapy Patients

Purpose: Recent advances in cancer treatments have greatly increased the likelihood of post-treatment patient survival. Secondary malignancies, however, have become a growing concern. Epidemiological studies determining secondary effects in radiotherapy patients require assessment of organ-specific dose both inside and outside the treatment field. An essential input for Monte Carlo modeling of particle transport is radiological images showing full patient anatomy. However, in retrospective studies it is typical to only have partial anatomy from CT scans used during treatment planning. In this study, we developed a multi-step method to extend such limited patient anatomy to full body anatomy for estimating dose to normal tissues located outside the CT scan coverage. Methods: The first step identified a phantom from a library of body size-dependent computational human phantoms by matching the height and weight of patients. Second, a Python algorithm matched the patient CT coverage location in relation to the whole body phantom. Third, an algorithm cut the whole body phantom and scaled them to match the size of the patient. Then, merged the two anatomies into one whole body. We entitled this new approach, Anatomically Predictive Extension (APE). Results: The APE method was examined by comparing the original chest-abdomen-pelvis CT images of the five patients with the APE phantoms developed from only the chest part of the CAP images and whole body phantoms. We achieved average percent differences of tissue volumes of 25.7%, 34.2%, 16.5%, 26.8%, and 31.6% with an average of 27% across all patients. Conclusion: Our APE method extends the limited CT patient anatomy to whole body anatomy by using image processing and computational human phantoms. Our ongoing work includes evaluating the accuracy of these APE phantoms by comparing normal tissue doses in the APE phantoms and doses calculated for the original full CAP images under generic radiotherapy simulations. This research was supported by the NIH Intramural Research Program.

60 APPLIED LIFE SCIENCES↗

The formation of quantum images and their transformation and super-resolution reading

Images formed by light with suppressed photon fluctuations are interesting objects for studies with the aim of increasing their limiting information capacity and quality. This light in the sub-Poisson state can be prepared in a resonator filled with a medium with Kerr nonlinearity, in which self-phase modulation takes place. Spatially and temporally multimode light beams are studied and the production of spatial frequency spectra of suppressed photon fluctuations is described. The efficient operation regimes of the system are found. A particular schematic solution is described, which allows one to realize the potential possibilities laid in the formation of the squeezed states of light to a maximum degree during self-phase modulation in a resonator for the maximal suppression of amplitude quantum noises upon two-dimensional imaging. The efficiency of using light with suppressed quantum fluctuations for computer image processing is studied. An algorithm is described for interpreting measurements for increasing the resolution with respect to the geometrical resolution. A mathematical model that characterizes the measurement scheme is constructed and the problem of the image reconstruction is solved. The algorithm for the interpretation of images is verified. Conditions are found for the efficient application of sub-Poisson light for super-resolution imaging. It is found that the image should have a low contrast and be maximally transparent.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

TU-F-18C-01: Breast Tissue Decomposition Using Spectral CT After Distortion Correction

Purpose: To investigate the feasibility of accurate breast tissue compositional characterization by using spectral-distortion-corrected dual energy images from a photon-counting spectral CT. Methods: Thirty eight postmortem breasts were imaged with a Cadmium-Zinc-Telluride (CZT)-based photon-counting spectral CT system at beam energy of 100 kVp. The energy-resolved detector sorted photons into low and high energy bins with a splitting energy of 42 keV. The estimated mean glandular dose (MGD) for each breast was approximately 2.0 mGy. Dual energy technique was used to decompose breast tissue into water, lipid, and protein contents. Two image-based methods were investigated to improve the accuracy of tissue compositional characterization. The first method simply limited the recorded spectra up to 90 keV. This reduced the pulse pile-up artifacts but it has some dose penalty. The second method corrected the spectral information of all measured photons by using a spectral distortion correction technique. Breasts were then chemically decomposed into their respective water, lipid, and protein contents, which was used as the reference standard. The accuracy of the tissue compositional measurement with spectral CT was evaluated by the root-mean-square (RMS) errors in percentage composition. Results: The errors in quantitative material decomposition were significantly reduced after the appropriate image processing methods. As compared to the chemical analysis as the reference standard, the averages of the RMS errors were estimated to be 15.5%, 3.3%, and 2.8% for the raw, energy-limited, and spectral-corrected images, respectively. Conclusion: Spectral CT can be used to accurately quantify the water, lipid, and protein contents in breast tissues by implementing a spectral distortion correction algorithm. The tissue compositional information can potentially improve the sensitivity and specificity for breast cancer diagnosis.

60 APPLIED LIFE SCIENCES↗

Quantitative Imaging with a Mobile Phone Microscope

Use of optical imaging for medical and scientific applications requires accurate quantification of features such as object size, color, and brightness. High pixel density cameras available on modern mobile phones have made photography simple and convenient for consumer applications; however, the camera hardware and software that enables this simplicity can present a barrier to accurate quantification of image data. This issue is exacerbated by automated settings, proprietary image processing algorithms, rapid phone evolution, and the diversity of manufacturers. If mobile phone cameras are to live up to their potential to increase access to healthcare in low-resource settings, limitations of mobile phone–based imaging must be fully understood and addressed with procedures that minimize their effects on image quantification. Here we focus on microscopic optical imaging using a custom mobile phone microscope that is compatible with phones from multiple manufacturers. We demonstrate that quantitative microscopy with micron-scale spatial resolution can be carried out with multiple phones and that image linearity, distortion, and color can be corrected as needed. Using all versions of the iPhone and a selection of Android phones released between 2007 and 2012, we show that phones with greater than 5 MP are capable of nearly diffraction-limited resolution over a broad range of magnifications, including those relevant for single cell imaging. We find that automatic focus, exposure, and color gain standard on mobile phones can degrade image resolution and reduce accuracy of color capture if uncorrected, and we devise procedures to avoid these barriers to quantitative imaging. By accommodating the differences between mobile phone cameras and the scientific cameras, mobile phone microscopes can be reliably used to increase access to quantitative imaging for a variety of medical and scientific applications.

59 BASIC BIOLOGICAL SCIENCES↗

Raster Metafile And Raster Metafile Translator Programs

Raster Metafile (RM) computer program is generic raster-image-format program, and Raster Metafile Translator (RMT) program is assortment of software tools for processing images prepared in this format. Processing includes reading, writing, and displaying RM images. Such other image-manipulation features as minimal compositing operator and resizing option available under RMT command structure. RMT written in FORTRAN 77 and C language.

Randall, Donald P.↗

The precision-processing subsystem for the Earth Resources Technology Satellite.

Description of the precision processor, a subsystem in the image-processing system for the Earth Resources Technology Satellite (ERTS). This processor is a special-purpose image-measurement and printing system, designed to process user-selected bulk images to produce 1:1,000,000-scale film outputs and digital image data, presented in a Universal-Transverse-Mercator (UTM) projection. The system will remove geometric and radiometric errors introduced by the ERTS multispectral sensors and by the bulk-processor electron-beam recorder. The geometric transformations required for each input scene are determined by resection computations based on reseau measurements and image comparisons with a special ground-control base contained within the system; the images are then printed and digitized by electronic image-transfer techniques.

Chapelle, W. E.↗

SU-F-I-10: Spatially Local Statistics for Adaptive Image Filtering

Purpose: To facilitate adaptive image filtering operations, addressing spatial variations in both noise and signal. Such issues are prevalent in cone-beam projections, where physical effects such as X-ray scattering result in spatially variant noise, violating common assumptions of homogeneous noise and challenging conventional filtering approaches to signal extraction and noise suppression. Methods: We present a computational mechanism for probing into and quantifying the spatial variance of noise throughout an image. The mechanism builds a pyramid of local statistics at multiple spatial scales; local statistical information at each scale includes (weighted) mean, median, standard deviation, median absolute deviation, as well as histogram or dynamic range after local mean/median shifting. Based on inter-scale differences of local statistics, the spatial scope of distinguishable noise variation is detected in a semi- or un-supervised manner. Additionally, we propose and demonstrate the incorporation of such information in globally parametrized (i.e., non-adaptive) filters, effectively transforming the latter into spatially adaptive filters. The multi-scale mechanism is materialized by efficient algorithms and implemented in parallel CPU/GPU architectures. Results: We demonstrate the impact of local statistics for adaptive image processing and analysis using cone-beam projections of a Catphan phantom, fitted within an annulus to increase X-ray scattering. The effective spatial scope of local statistics calculations is shown to vary throughout the image domain, necessitating multi-scale noise and signal structure analysis. Filtering results with and without spatial filter adaptation are compared visually, illustrating improvements in imaging signal extraction and noise suppression, and in preserving information in low-contrast regions. Conclusion: Local image statistics can be incorporated in filtering operations to equip them with spatial adaptivity to spatial signal/noise variations. An efficient multi-scale computational mechanism is developed to curtail processing latency. Spatially adaptive filtering may impact subsequent processing tasks such as reconstruction and numerical gradient computations for deformable registration. NIH Grant No. R01-184173.

60 APPLIED LIFE SCIENCES↗

(abstract) The 1994 Laboratory Calibration Of TIMS

This summary describes the spatial, spectral, and radiometric calibration of TIMS performed at the JPL Thermal Infrared Calibration Facility (TIRCAL) between May and August 1994. The 1994 calibration of TIMS was the first to make use of the new EXABYTE (8mm helical-scan tape) recording system. With the new recorder, the TIMS data tapes may be read directly on any computer system that has an EXABYTE tape drive. We analyzed the calibration data sets using image processing procedures written in IDL (Research Systems, Inc., Boulder, CO 80303).

TIMS↗

Massively parallel processor

A brief description is given of the Massively Parallel Processor (MPP). Major applications of the MPP are in the area of image processing (where the operands are often very small integers) from very high spatial resolution passive image sensors, signal processing of radar data, and numerical modeling simulations of climate. The system can be programmed in assembly language or a high level language. Information on background, status, architecture, programming, hardware reliability, applications, and the MPP's development as a national resource for parallel algorithm research are presented in outline form.

Source record↗