Engineering topics

Gray, Elizabeth

Publications and source records attributed to Gray, Elizabeth.

9 records

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↗

NASA NTRS2009

Detecting Phase Boundaries in Hard-Sphere Suspensions

A special image-data-processing technique has been developed for use in experiments that involve observation, via optical microscopes equipped with electronic cameras, of moving boundaries between the colloidal-solid and colloidal-liquid phases of colloidal suspensions of monodisperse hard spheres. During an experiment, it is necessary to adjust the position of a microscope to keep the phase boundary within view. A boundary typically moves at a speed of the order of microns per hour. Because an experiment can last days or even weeks, it is impractical to require human intervention to keep the phase boundary in view. The present image-data-processing technique yields results within a computation time short enough to enable generation of automated-microscope-positioning commands to track the moving phase boundary

McDowell, Mark↗

NASA NTRS2008

Cell-Detection Technique for Automated Patch Clamping

A unique and customizable machinevision and image-data-processing technique has been developed for use in automated identification of cells that are optimal for patch clamping. [Patch clamping (in which patch electrodes are pressed against cell membranes) is an electrophysiological technique widely applied for the study of ion channels, and of membrane proteins that regulate the flow of ions across the membranes. Patch clamping is used in many biological research fields such as neurobiology, pharmacology, and molecular biology.] While there exist several hardware techniques for automated patch clamping of cells, very few of those techniques incorporate machine vision for locating cells that are ideal subjects for patch clamping. In contrast, the present technique is embodied in a machine-vision algorithm that, in practical application, enables the user to identify good and bad cells for patch clamping in an image captured by a charge-coupled-device (CCD) camera attached to a microscope, within a processing time of one second. Hence, the present technique can save time, thereby increasing efficiency and reducing cost. The present technique involves the utilization of cell-feature metrics to accurately make decisions on the degree to which individual cells are "good" or "bad" candidates for patch clamping. These metrics include position coordinates (x,y) in the image plane, major-axis length, minor-axis length, area, elongation, roundness, smoothness, angle of orientation, and degree of inclusion in the field of view. The present technique does not require any special hardware beyond commercially available, off-the-shelf patch-clamping hardware: A standard patchclamping microscope system with an attached CCD camera, a personal computer with an imagedata- processing board, and some experience in utilizing imagedata- processing software are all that are needed. A cell image is first captured by the microscope CCD camera and image-data-processing board, then the image data are analyzed by software that implements the present machine-vision technique. This analysis results in the identification of cells that are "good" candidates for patch clamping (see figure). Once a "good" cell is identified, a patch clamp can be effected by an automated patchclamping apparatus or by a human operator. This technique has been shown to enable reliable identification of "good" and "bad" candidate cells for patch clamping. The ultimate goal in further development of this technique is to combine artificial-intelligence processing with instrumentation and controls in order to produce a complete "turnkey" automated patch-clamping system capable of accurately and reliably patch clamping cells with a minimum intervention by a human operator. Moreover, this technique can be adapted to virtually any cellular-analysis procedure that includes repetitive operation of microscope hardware by a human.

McDowell, Mark↗

NASA NTRS2008

A Stereo Imaging Velocimetry Technique for Analyzing Structure of Flame Balls at Low Lewis-Number (SOFBALL) Data

Stereo Imaging Velocimetry (SIV) is a NASA Glenn Research Center (GRC) developed fluid physics technique for measuring threedimensional (3-D) velocities in any optically transparent fluid that can be seeded with tracer particles. SIV provides a means to measure 3-D fluid velocities quantitatively and qualitatively at many points. This technique provides full-field 3-D analysis of any optically clear fluid or gas experiment using standard off-the-shelf CCD cameras to provide accurate and reproducible 3-D velocity profiles for experiments that require 3-D analysis. A flame ball is a steady flame in a premixed combustible atmosphere which, due to the transport properties (low Lewis-number) of the mixture, does not propagate but is instead supplied by diffusive transport of the reactants, forming a premixed flame. This flame geometry presents a unique environment for testing combustion theory. We present our analysis of flame ball phenomena utilizing SIV technology in order to accurately calculate the 3-D position of a flame ball(s) during an experiment, which can be used as a direct comparison of numerical simulations.

McDowell, Mark↗

NASA NTRS2006

An Automated Classification Technique for Detecting Defects in Battery Cells

Battery cell defect classification is primarily done manually by a human conducting a visual inspection to determine if the battery cell is acceptable for a particular use or device. Human visual inspection is a time consuming task when compared to an inspection process conducted by a machine vision system. Human inspection is also subject to human error and fatigue over time. We present a machine vision technique that can be used to automatically identify defective sections of battery cells via a morphological feature-based classifier using an adaptive two-dimensional fast Fourier transformation technique. The initial area of interest is automatically classified as either an anode or cathode cell view as well as classified as an acceptable or a defective battery cell. Each battery cell is labeled and cataloged for comparison and analysis. The result is the implementation of an automated machine vision technique that provides a highly repeatable and reproducible method of identifying and quantifying defects in battery cells.

McDowell, Mark↗

NASA NTRS2006

Modular, Microprocessor-Controlled Flash Lighting System

A microprocessor-controlled lighting system generates brief, precisely timed, high-intensity flashes of light for scientific imaging at frame rates up to about 1 kHz. The system includes an array of light-emitting diodes (LEDs) that are driven in synchronism with an externally generated timing signal (for example, a timing signal generated by a video camera). The light output can be varied in peak intensity, pulse duration, pulse delay, and pulse rate, all depending on the timing signal and associated externally generated control signals. The array of LEDs comprises as many as 16 LED panels that can be attached together. Each LED panel is a module consisting of a rectangular subarray of 10 by 20 LEDs of advanced design on a printed-circuit board in a mounting frame with a power/control connector. The LED panels are controlled by an LED control module that contains an AC-to-DC power supply, a control board, and 8 LED-panel driver boards. In prior LED panels, the LEDs are packaged at less than maximum areal densities in bulky metal housings that reduce effective active areas. In contrast, in the present LED panels, the LEDs are packed at maximum areal density so as to afford 100-percent active area and so that when panels are joined side by side to form the array, there are no visible seams between them and the proportion of active area is still 100 percent. Each panel produces an illuminance of .5 x 10( exp 4) lux at a distance of 5.8 in. (approx.1.6 cm). The LEDs are driven according to a pulse-width-modulation control scheme that makes it safe to drive the LEDs beyond their rated steady-state currents in order to generate additional light during short periods. The drive current and the pulse-width modulation for each LED panel can be controlled independently of those of the other 15 panels. The maximum allowable duration of each pulse of drive current is a function of the amount of overdrive, the total time to be spent in overdrive operation, and the limitations of the LEDs. The system is configured to limit the overdrive according to values specific to each type of LED in the array. These values are coded into firmware to prevent inadvertent damage to the LED panels.

Kiefer, Dwayne↗

NASA NTRS2005

An Automatic Phase-Change Detection Technique for Colloidal Hard Sphere Suspensions

Colloidal suspensions of monodisperse spheres are used as physical models of thermodynamic phase transitions and as precursors to photonic band gap materials. However, current image analysis techniques are not able to distinguish between densely packed phases within conventional microscope images, which are mainly characterized by degrees of randomness or order with similar grayscale value properties. Current techniques for identifying the phase boundaries involve manually identifying the phase transitions, which is very tedious and time consuming. We have developed an intelligent machine vision technique that automatically identifies colloidal phase boundaries. The algorithm utilizes intelligent image processing techniques that accurately identify and track phase changes vertically or horizontally for a sequence of colloidal hard sphere suspension images. This technique is readily adaptable to any imaging application where regions of interest are distinguished from the background by differing patterns of motion over time.

McDowell, Mark↗

NASA NTRS2004

An Optimal Cell Detection Technique for Automated Patch Clamping

While there are several hardware techniques for the automated patch clamping of cells that describe the equipment apparatus used for patch clamping, very few explain the science behind the actual technique of locating the ideal cell for a patch clamping procedure. We present a machine vision approach to patch clamping cell selection by developing an intelligent algorithm technique that gives the user the ability to determine the good cell to patch clamp in an image within one second. This technique will aid the user in determining the best candidates for patch clamping and will ultimately save time, increase efficiency and reduce cost. The ultimate goal is to combine intelligent processing with instrumentation and controls in order to produce a complete turnkey automated patch clamping system capable of accurately and reliably patch clamping cells with a minimum amount of human intervention. We present a unique technique that identifies good patch clamping cell candidates based on feature metrics of a cell's (x, y) position, major axis length, minor axis length, area, elongation, roundness, smoothness, angle of orientation, thinness and whether or not the cell is only particularly in the field of view. A patent is pending for this research.

McDowell, Mark↗

NASA NTRS2004

Stereo Imaging Velocimetry Technique Using Standard Off-the-Shelf CCD Cameras

Stereo imaging velocimetry is a fluid physics technique for measuring three-dimensional (3D) velocities at a plurality of points. This technique provides full-field 3D analysis of any optically clear fluid or gas experiment seeded with tracer particles. Unlike current 3D particle imaging velocimetry systems that rely primarily on laser-based systems, stereo imaging velocimetry uses standard off-the-shelf charge-coupled device (CCD) cameras to provide accurate and reproducible 3D velocity profiles for experiments that require 3D analysis. Using two cameras aligned orthogonally, we present a closed mathematical solution resulting in an accurate 3D approximation of the observation volume. The stereo imaging velocimetry technique is divided into four phases: 3D camera calibration, particle overlap decomposition, particle tracking, and stereo matching. Each phase is explained in detail. In addition to being utilized for space shuttle experiments, stereo imaging velocimetry has been applied to the fields of fluid physics, bioscience, and colloidal microscopy.

McDowell, Mark↗