Algorithms to Preprocess Microarray Image Data

2021 ◽  
pp. 69-78
Author(s):  
Paolo Zaffino ◽  
Maria Francesca Spadea
Keyword(s):  
Image Data ◽  
Microarray Image

Web-based tissue microarray image data analysis: Initial validation testing through prostate cancer Gleason grading

2001 ◽  
Vol 32 (4) ◽  
pp. 417-427 ◽  
Author(s):  
G.Steven Bova ◽  
Giovanni Parmigiani ◽  
Jonathan I. Epstein ◽  
Thomas Wheeler ◽  
Neil R. Mucci ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Data Analysis ◽  
Tissue Microarray ◽  
Image Data ◽  
Web Based ◽  
Gleason Grading ◽  
Initial Validation ◽  
Microarray Image ◽  
Validation Testing

scholarly journals Fully Automatic Quantification of Microarray Image Data

2002 ◽  
Vol 12 (2) ◽  
pp. 325-332 ◽  
Author(s):  
A. N. Jain
Keyword(s):  
Image Data ◽  
Automatic Quantification ◽  
Microarray Image ◽  
Fully Automatic

AUTOMATED MICROARRAY IMAGE GRIDDING USING IMAGE PROJECTION VECTORS COUPLED WITH POWER SPECTRUM MODEL

2010 ◽  
Vol 24 (04) ◽  
pp. 581-600 ◽  
Author(s):  
NING DENG ◽  
HUILONG DUAN
Keyword(s):  
Power Spectrum ◽  
Image Data ◽  
Microarray Experiment ◽  
Microarray Image ◽  
Image Projection ◽  
Microarray Datasets ◽  
Microarray Analyses ◽  
Microarray Image Processing ◽  
Spectrum Model ◽  
Experimental Comparisons

Microarray technology has been increasingly recognized as a powerful means for monitoring the expression levels of thousands of genes simultaneously. Microarray image processing is an essential aspect of microarray experiment, of which gridding is thought to be the most important step of spot recognition. Many times, microarray image gridding requires assisted intervention to achieve the acceptable accuracy. In this paper, an automatic microarray image gridding algorithm was presented by using image projection vectors together with power spectrum model. For obtaining grid position, the image projection vectors were utilized by adequately considering the grid parameters. On the other hand, as a preprocessing procedure of microarray gridding, detection of the grid rotation was involved in our study by using power spectrum analyses of the image projection vectors. Our approach has been evaluated by three different microarray datasets. Experimental comparisons with up-to-date approaches by using both synthetic and real image data are demonstrated. The gridding result was shown to be very accurate, and able to provide correct gridding dataset for the downstream microarray analyses. In summary, our study demonstrated the combination of image projection vectors with power spectrum model as a powerful strategy for microarray image gridding.

Dynamical Scattering in Crystalline Biological Specimens. I. Criteria of Validity for Scattering Approximations

1975 ◽  
Vol 33 ◽  
pp. 192-193
Author(s):  
Robert M. Glaeser ◽  
Bing K. Jap
Keyword(s):  
Biological Sciences ◽  
Electron Microscopy ◽  
Electron Microscope ◽  
Electron Diffraction ◽  
Born Approximation ◽  
Materials Science ◽  
Image Data ◽  
Dynamical Effect ◽  
Crystallographic Structure ◽  
Electron Microscope Image

The dynamical scattering effect, which can be described as the failure of the first Born approximation, is perhaps the most important factor that has prevented the widespread use of electron diffraction intensities for crystallographic structure determination. It would seem to be quite certain that dynamical effects will also interfere with structure analysis based upon electron microscope image data, whenever the dynamical effect seriously perturbs the diffracted wave. While it is normally taken for granted that the dynamical effect must be taken into consideration in materials science applications of electron microscopy, very little attention has been given to this problem in the biological sciences.

Application of an image management system in microscopy

1992 ◽  
Vol 50 (2) ◽  
pp. 1052-1053
Author(s):  
Richard S. Chemock
Keyword(s):  
Data Storage ◽  
Cost Savings ◽  
Image Data ◽  
Analytical Techniques ◽  
Operational Mode ◽  
Fine Grained ◽  
Image Recording ◽  
Primary Output ◽  
Capacity Data ◽  
Image Management System

One of the most common tasks in a typical analysis lab is the recording of images. Many analytical techniques (TEM, SEM, and metallography for example) produce images as their primary output. Until recently, the most common method of recording images was by using film. Current PS/2R systems offer very large capacity data storage devices and high resolution displays, making it practical to work with analytical images on PS/2s, thereby sidestepping the traditional film and darkroom steps. This change in operational mode offers many benefits: cost savings, throughput, archiving and searching capabilities as well as direct incorporation of the image data into reports.The conventional way to record images involves film, either sheet film (with its associated wet chemistry) for TEM or PolaroidR film for SEM and light microscopy. Although film is inconvenient, it does have the highest quality of all available image recording techniques. The fine grained film used for TEM has a resolution that would exceed a 4096x4096x16 bit digital image.

Digital differential hysteresis iimage processing displays what the microscope acquires but the eye can't see

1995 ◽  
Vol 53 ◽  
pp. 642-643
Author(s):  
Klaus-Ruediger Peters
Keyword(s):  
Image Processing ◽  
Visual System ◽  
High Precision ◽  
Image Data ◽  
Processing Technology ◽  
Output Data ◽  
Contrast Resolution ◽  
Pixel Intensity ◽  
Data Reading ◽  
Hysteresis Properties

Differential hysteresis processing is a new image processing technology that provides a tool for the display of image data information at any level of differential contrast resolution. This includes the maximum contrast resolution of the acquisition system which may be 1,000-times higher than that of the visual system (16 bit versus 6 bit). All microscopes acquire high precision contrasts at a level of <0.01-25% of the acquisition range in 16-bit - 8-bit data, but these contrasts are mostly invisible or only partially visible even in conventionally enhanced images. The processing principle of the differential hysteresis tool is based on hysteresis properties of intensity variations within an image.Differential hysteresis image processing moves a cursor of selected intensity range (hysteresis range) along lines through the image data reading each successive pixel intensity. The midpoint of the cursor provides the output data. If the intensity value of the following pixel falls outside of the actual cursor endpoint values, then the cursor follows the data either with its top or with its bottom, but if the pixels' intensity value falls within the cursor range, then the cursor maintains its intensity value.

SPECTRA: A program for processing electron images of crystals

1992 ◽  
Vol 50 (1) ◽  
pp. 132-133
Author(s):  
M.F. Schmid ◽  
R. Dargahi ◽  
M. W. Tam
Keyword(s):  
Lattice Distortion ◽  
Data Transfer ◽  
Reciprocal Lattice ◽  
Data Entry ◽  
Image Data ◽  
Distortion Correction ◽  
Specimen Preparation ◽  
Electron Image ◽  
Computer Controlled ◽  
Program Modules

Electron crystallography is an emerging field for structure determination as evidenced by a number of membrane proteins that have been solved to near-atomic resolution. Advances in specimen preparation and in data acquisition with a 400kV microscope by computer controlled spot scanning mean that our ability to record electron image data will outstrip our capacity to analyze it. The computed fourier transform of these images must be processed in order to provide a direct measurement of amplitudes and phases needed for 3-D reconstruction.In anticipation of this processing bottleneck, we have written a program that incorporates a menu-and mouse-driven procedure for auto-indexing and refining the reciprocal lattice parameters in the computed transform from an image of a crystal. It is linked to subsequent steps of image processing by a system of data bases and spawned child processes; data transfer between different program modules no longer requires manual data entry. The progress of the reciprocal lattice refinement is monitored visually and quantitatively. If desired, the processing is carried through the lattice distortion correction (unbending) steps automatically.

Information and estimation in image processing

1987 ◽  
Vol 45 ◽  
pp. 14-17
Author(s):  
B. Roy Frieden
Keyword(s):  
Image Processing ◽  
Optical System ◽  
Large Amplitude ◽  
Communication Theory ◽  
Image Data ◽  
Direct Approach ◽  
Ill Posed

Despite the skill and determination of electro-optical system designers, the images acquired using their best designs often suffer from blur and noise. The aim of an “image enhancer” such as myself is to improve these poor images, usually by digital means, such that they better resemble the true, “optical object,” input to the system. This problem is notoriously “ill-posed,” i.e. any direct approach at inversion of the image data suffers strongly from the presence of even a small amount of noise in the data. In fact, the fluctuations engendered in neighboring output values tend to be strongly negative-correlated, so that the output spatially oscillates up and down, with large amplitude, about the true object. What can be done about this situation? As we shall see, various concepts taken from statistical communication theory have proven to be of real use in attacking this problem. We offer below a brief summary of these concepts.

Comparison of Techniques for EELS Mapping in Biology

1996 ◽  
Vol 54 ◽  
pp. 300-301
Author(s):  
R.D. Leapman ◽  
S.B. Andrews
Keyword(s):  
Electron Microscope ◽  
Transmission Electron Microscope ◽  
Image Data ◽  
Beam Current ◽  
Quantitative Information ◽  
Acquisition Time ◽  
Uniform Thickness ◽  
Electron Dose ◽  
Array Detectors ◽  
Transmission Electron

Elemental mapping of biological specimens by electron energy loss spectroscopy (EELS) can be carried out both in the scanning transmission electron microscope (STEM), and in the energy-filtering transmission electron microscope (EFTEM). Choosing between these two approaches is complicated by the variety of specimens that are encountered (e.g., cells or macromolecules; cryosections, plastic sections or thin films) and by the range of elemental concentrations that occur (from a few percent down to a few parts per million). Our aim here is to consider the strengths of each technique for determining elemental distributions in these different types of specimen.On one hand, it is desirable to collect a parallel EELS spectrum at each point in the specimen using the ‘spectrum-imaging’ technique in the STEM. This minimizes the electron dose and retains as much quantitative information as possible about the inelastic scattering processes in the specimen. On the other hand, collection times in the STEM are often limited by the detector read-out and by available probe current. For example, a 256 x 256 pixel image in the STEM takes at least 30 minutes to acquire with read-out time of 25 ms. The EFTEM is able to collect parallel image data using slow-scan CCD array detectors from as many as 1024 x 1024 pixels with integration times of a few seconds. Furthermore, the EFTEM has an available beam current in the µA range compared with just a few nA in the STEM. Indeed, for some applications this can result in a factor of ~100 shorter acquisition time for the EFTEM relative to the STEM. However, the EFTEM provides much less spectral information, so that the technique of choice ultimately depends on requirements for processing the spectrum at each pixel (viz., isolated edges vs. overlapping edges, uniform thickness vs. non-uniform thickness, molar vs. millimolar concentrations).

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