Biomedical subjects

Stephen E Reichenbach

Publications and source records attributed to Stephen E Reichenbach.

6 recordsLinked to original sources

PubMed2006

Small-kernel superresolution methods for microscanning imaging systems.

Two computationally efficient methods for superresolution reconstruction and restoration of microscanning imaging systems are presented. Microscanning creates multiple low-resolution images with slightly different sample-scene phase shifts. The digital processing methods developed here combine the low-resolution images to produce an image with higher pixel resolution (i.e., superresolution) and higher fidelity. The methods implement reconstruction to increase resolution and restoration to improve fidelity in one-pass convolution with a small kernel. One method uses a small-kernel Wiener filter and the other method uses a parametric cubic convolution filter. Both methods are based on an end-to-end, continuous-discrete-continuous microscanning imaging system model. Because the filters are constrained to small spatial kernels they can be efficiently applied by convolution and are amenable to adaptive processing and to parallel processing. Experimental results with simulated imaging and with real microscanned images indicate that the small-kernel methods efficiently and effectively increase resolution and fidelity.

Journal Article↗

PubMed2006

Comparative visualization for comprehensive two-dimensional gas chromatography.

This paper investigates methods for comparing datasets produced by comprehensive two-dimensional gas chromatography (GC x GC). Chemical comparisons are useful for process monitoring, sample classification or identification, correlative determinations, and other important tasks. GC x GC is a powerful new technology for chemical analysis, but methods for comparative visualization must address challenges posed by GC x GC data: inconsistency and complexity. The approach extends conventional techniques for image comparison by utilizing specific characteristics of GC x GC data and developing new methods for comparative visualization and analysis. The paper describes techniques that register (or align) GC x GC datasets to remove retention-time variations; normalize intensities to remove sample amount variations; compute differences in local regions to remove slight misregistrations and differences in peak shapes; employ color (hue), intensity, and saturation to simultaneously visualize differences and values; and use tools for masking, three-dimensional visualization, and tabular presentation with controls for graphical highlights to significantly improve comparative analysis of GC x GC datasets. Experimental results indicate that the comparative methods preserve chemical information and support qualitative and quantitative analyses.

Benzene Derivatives↗

PubMed2006

Image interpolation by two-dimensional parametric cubic convolution.

Cubic convolution is a popular method for image interpolation. Traditionally, the piecewise-cubic kernel has been derived in one dimension with one parameter and applied to two-dimensional (2-D) images in a separable fashion. However, images typically are statistically nonseparable, which motivates this investigation of nonseparable cubic convolution. This paper derives two new nonseparable, 2-D cubic-convolution kernels. The first kernel, with three parameters (designated 2D-3PCC), is the most general 2-D, piecewise-cubic interpolator defined on [-2, 2] x [-2, 2] with constraints for biaxial symmetry, diagonal (or 90 degrees rotational) symmetry, continuity, and smoothness. The second kernel, with five parameters (designated 2D-5PCC), relaxes the constraint of diagonal symmetry, based on the observation that many images have rotationally asymmetric statistical properties. This paper also develops a closed-form solution for determining the optimal parameter values for parametric cubic-convolution kernels with respect to ensembles of scenes characterized by autocorrelation (or power spectrum). This solution establishes a practical foundation for adaptive interpolation based on local autocorrelation estimates. Quantitative fidelity analyses and visual experiments indicate that these new methods can outperform several popular interpolation methods. An analysis of the error budgets for reconstruction error associated with blurring and aliasing illustrates that the methods improve interpolation fidelity for images with aliased components. For images with little or no aliasing, the methods yield results similar to other popular methods. Both 2D-3PCC and 2D-5PCC are low-order polynomials with small spatial support and so are easy to implement and efficient to apply.

Algorithms↗

PubMed2005

Peak pattern variations related to comprehensive two-dimensional gas chromatography acquisition.

Identifying compounds of interest for peaks in data generated by comprehensive two-dimensional gas chromatography (GC x GC) is a critical analytical task. Manually identifying compounds is tedious and time-consuming. An alternative is to use pattern matching. Pattern matching identifies compounds by matching previously observed patterns with known peaks to newly observed patterns with unidentified peaks. The fundamental difficulty of pattern matching comes from peak pattern distortions that are caused by differences in data acquisition conditions. This paper investigates peak pattern variations related to varying oven temperature ramp rate and inlet gas pressure and evaluates two types of affine transformations for matching peak patterns. The experimental results suggest that, over the experimental ranges, the changes in temperature ramp rate generate non-linear pattern variations and changes in gas pressure generate nearly linear pattern variations. The results indicate the affine transformations can largely remove the pattern variations and can be used for applications such as pattern matching and normalizing retention times to retention indices.

Chromatography, Gas↗

PubMed2005

Computer language for identifying chemicals with comprehensive two-dimensional gas chromatography and mass spectrometry.

This paper describes a language for expressing criteria for chemical identification with comprehensive two-dimensional gas chromatography paired with mass spectrometry (GC x GC-MS) and presents computer-based tools implementing the language. The Computer Language for Indentifying Chemicals (CLIC) allows expressions that describe rules (or constraints) for selecting chemical peaks or data points based on multi-dimensional chromatographic properties and mass spectral characteristics. CLIC offers chromatographic functions of retention times, functions of mass spectra, numbers for quantitative and relational evaluation, and logical and arithmetic operators. The language is demonstrated with the compound-class selection rules described by Welthagen et al. [W. Welthagen, J. Schnelle-Kreis, R. Zimmermann, J. Chromatogr. A 1019 (2003) 233-249]. A software implementation of CLIC provides a calculator-like graphical user-interface (GUI) for building and applying selection expressions. From the selection calculator, expressions can be used to select chromatographic peaks that meet the criteria or create selection chromatograms that mask data points inconsistent with the criteria. Selection expressions can be combined with graphical, geometric constraints in the retention-time plane as a powerful component for chemical identification with template matching or used to speed and improve mass spectrum library searches.

Gas Chromatography-Mass Spectrometry↗

PubMed2003

Image background removal in comprehensive two-dimensional gas chromatography.

This paper describes a new technique for removing the background level from digital images produced in comprehensive two-dimensional gas chromatography (GCxGC). Background removal is an important first step in the larger problem of quantitative analysis. The approach estimates the background level across the chromatographic image based on structural and statistical properties of GCxGC data. Then, the background level is subtracted from the image, producing a chromatogram in which the peaks rise above a near-zero mean background. After the background level is removed, further analysis is required to determine the quantitative relationship between the peaks and chemicals in the sample. The algorithm is demonstrated experimentally to be effective at determining and removing the background level from GCxGC images. The algorithm has several parametric controls and is incorporated into an interactive program with graphical interface for rapid and accurate detection of GCxGC peaks.

Chromatography, Gas↗