Laura M. Kegelmeyer

Temporal Global Changes in Gene Expression during Temperature Transition in Yersinia pestis

DNA microarrays encompassing the entire genome of Yersinia pestis were used to characterize global regulatory changes during steady-state vegetative growth occurring after shift from 26 to 37 degrees C in the presence and absence of Ca2+. Transcriptional profiles revealed that 51, 4, and 13 respective genes and open reading frames (ORFs) on pCD, pPCP, and pMT were thermoinduced and that the majority of these genes carried by pCD were downregulated by Ca2+. In contrast, Ca2+ had little effect on chromosomal genes and ORFs, of which 235 were thermally upregulated and 274 were thermally downregulated. The primary consequence of these regulatory events is profligate catabolism of numerous metabolites available in the mammalian host.

Local area signal-to-noise ratio (LASNR) algorithm for image segmentation

Many automated image-based applications have need of finding small spots in a variably noisy image. For humans, it is relatively easy to distinguish objects from local surroundings no matter what else may be in the image. We attempt to capture this distinguishing capability computationally by calculating a measurement that estimates the strength of signal within an object versus the noise in its local neighborhood. First, we hypothesize various sizes for the object and corresponding background areas. Then, we compute the Local Area Signal to Noise Ratio (LASNR) at every pixel in the image, resulting in a new image with LASNR values for each pixel. All pixels exceeding a pre-selected LASNR value become seed pixels, or initiation points, and are grown to include the full area extent of the object. Since growing the seed is a separate operation from finding the seed, each object can be any size and shape. Thus, the overall process is a 2-stage segmentation method that first finds object seeds and then grows them to find the full extent of the object. This algorithm was designed, optimized and is in daily use for the accurate and rapid inspection of optics from a large laser system (National Ignition Facility (NIF), Lawrence Livermore National Laboratory, Livermore, CA), which includes images with background noise, ghost reflections, different illumination and other sources of variation.

Groundtruth approach to accurate quantitation of fluorescence microarrays

To more accurately measure fluorescent signals from microarrays, we calibrated our acquisition and analysis systems by using groundtruth samples comprised of known quantities of red and green gene-specific DNA probes hybridized to cDNA targets. We imaged the slides with a full-field, white light CCD imager and analyzed them with our custom analysis software. Here we compare, for multiple genes, results obtained with and without preprocessing (alignment, color crosstalk compensation, dark field subtraction, and integration time). We also evaluate the accuracy of various image processing and analysis techniques (background subtraction, segmentation, quantitation and normalization). This methodology calibrates and validates our system for accurate quantitative measurement of microarrays. Specifically, we show that preprocessing the images produces results substantially closer to the known groundtruth for these samples.

Process for rapid detection of fratricidal defects on optics using linescan phase-differential imaging

Phase-defects on optics used in high-power lasers can cause light intensification leading to laser-induced damage of downstream optics. We introduce Linescan Phase Differential Imaging (LPDI), a large-area dark-field imaging technique able to identify phase-defects in the bulk or surface of large-aperture optics with a 67 second scan-time. Potential phase-defects in the LPDI images are indentified by an image analysis code and measured with a Phase Shifting Diffraction Interferometer (PSDI). The PSDI data is used to calculate the defects potential for downstream damage using an empirical laser-damage model that incorporates a laser propagation code. A ray tracing model of LPDI was developed to enhance our understanding of its phase-defect detection mechanism and reveal limitations.

Detection of Laser Optic Defects Using Gradient Direction Matching

That National Ignition Facility (NIF) at Lawrence Livermore National Laboratory (LLNL) will be the worlds largest and most energetic laser. It has thousands of optics and depends heavily on the quality and performance of these optics. Over the past several years, we have developed the NIF Optics Inspection Analysis System that automatically finds defects in a specific optic by analyzing images taken of that optic. This paper describes a new and complementary approach for the automatic detection of defects based on detecting the diffraction ring patterns in downstream optic images caused by defects in upstream optics. Our approach applies a robust pattern matching algorithm for images called Gradient Direction Matching (GDM). GDM compares the gradient directions (the direction of flow from dark to light) of pixels in a test image to those of a specified model and identifies regions in the test image whose gradient directions are most in line with those of the specified model. For finding rings, we use luminance disk models whose pixels have gradient directions all pointing toward the center of the disk. After GDM identifies potential rings locations, we rank these rings by how well they fit the theoretical diffraction ring pattern equation. We perform false alarm mitigation by throwing out rings of low fit. A byproduct of this fitting procedure is an estimate of the size of the defect and its distance from the image plane. We demonstrate the potential effectiveness of this approach by showing examples of rings detected in real images of NIF optics.

Deep learning for evaluating difficult-to-detect incomplete repairs of high fluence laser optics at the National Ignition Facility

Two machine-learning methods were evaluated to help automate the quality control process for mitigating damage sites on laser optics. The mitigation is a cone-like structure etched into locations on large optics that have been chipped by the high fluence (energy per unit area) laser light. Sometimes the repair leaves a difficult to detect remnant of the damage that needs to be addressed before the optic can be placed back on the beam line. We would like to be able to automatically detect these remnants. We try Deep Learning (convolutional neural networks using features autogenerated from large stores of labeled data, like ImageNet) and find it outperforms ensembles of decision trees (using custom-built features) in finding these subtle, rare, incomplete repairs of damage. We also implemented an unsupervised method for helping operators visualize where the network has spotted problems. This is done by projecting the credit for the result backwards onto the input image. This shows regions in an image most responsible for the networks decision. This can also be used to help understand the black box decisions the network is making and potentially improve the training process.

The HMDS coating flaw removal tool

In many high energy laser systems, optics with HMDS sol gel antireflective coatings are placed in close proximity to each other making them particularly susceptible to certain types of strong optical interactions. During the coating process, halo shaped coating flaws develop around surface digs and particles. Depending on the shape and size of the flaw, the extent of laser light intensity modulation and consequent probability of damaging downstream optics may increase significantly. To prevent these defects from causing damage, a coating flaw removal tool was developed that deploys a spot of decane with a syringe and dissolves away the coating flaw. The residual liquid is evacuated leaving an uncoated circular spot approximately 1mm in diameter. The resulting uncoated region causes little light intensity modulation and thus has a low probability of causing damage in optics downstream from the mitigated flaw site.

Signal and image processing research at the Lawrence Livermore National Laboratory

Lawrence Livermore National Laboratory is a large, multidisciplinary institution that conducts fundamental and applied research in the physical sciences. Research programs at the Laboratory run the gamut from theoretical investigations, to modeling and simulation, to validation through experiment. Over the years, the Laboratory has developed a substantial research component in the areas of signal and image processing to support these activities. This paper surveys some of the current research in signal and image processing at the Laboratory. Of necessity, the paper does not delve deeply into any one research area, but an extensive citation list is provided for further study of the topics presented.