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Visualization and processing of multidimensional data using prefiltering and sorting criteria

a multi-dimensional data and prefiltering technology, applied in the field of visualizing and processing multi-dimensional data, can solve the problems of not being able to sort single-pixel spectra without additional parameters or human intervention, burdensome processing time, prior art sorting algorithms, etc., to facilitate independent sorting and backcoloring of individual groups, the effect of rapid and efficient extraction of useful information

Inactive Publication Date: 2005-05-26
YANG MARY M +5
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

[0008] Sorting can also be used for sequentially analyzing images and graphical data, such that the pixels that are ultimately displayed are restricted by at least two independent criteria. For example, pixels or features that have been extracted based on selected spectral criteria (e.g., absorbance) can be further analyzed based on temporal criteria (e.g., kinetics). This method of combined analysis provides a means for rapidly and efficiently extracting useful information from massive amounts of data. A further embodiment of sequential sorting involves discarding unwanted data during the sorting process. This ‘sort and lock’ procedure provides a useful new tool for data compression. This method for sorting and displaying multidimensional data from an image stack comprises the steps of: (a) selecting a subset of pixels from an image by a first algorithm; (b) discarding the pixels that are not selected; (c) selecting a subset of the remaining pixels by a second sorting algorithm; and (d) automatically indicating the final selection of pixels by back-coloring the corresponding pixels in the image. This type of multidimensional analysis can also be performed by manipulating the contour plot window. The method comprises the steps of (a) sorting the pixels by a first algorithm; (b) automatically indicating on the contour plot pixels sorted by the first algorithm; (c) selecting a subset of pixels in the contour plot; (d) sorting the subset of pixels by applying a second algorithm; (e) selecting a reduced subset of pixels in the contour plot; and (f) automatically indicating the final selection of pixels by backcoloring the reduced subset of pixels in the image. The present invention also provides a method for displaying a grouping bar that can be used to analyze images and graphical data within the graphical user interface (“GUI”). The grouping bar enables the user to segregate groups of pixels or features within a contour plot, and thereby facilitates independent sorting and backcoloring of the individual groups of pixels or features in the image. The methods of the present invention are applicable to a variety of problems involving complex, multidimensional, or gigapixel imaging tasks, including (for example) automated screening of genetic libraries expressing enzyme variants.

Problems solved by technology

In addition to requiring a burdensome amount of processing time, prior art sorting algorithms that may have been adequate to categorize and classify relatively noiseless feature data are not necessarily successful in sorting single-pixel spectra without additional parameters or human intervention.

Method used

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  • Visualization and processing of multidimensional data using prefiltering and sorting criteria
  • Visualization and processing of multidimensional data using prefiltering and sorting criteria
  • Visualization and processing of multidimensional data using prefiltering and sorting criteria

Examples

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example 1

[0080] In this example, an analysis was first performed using the spectral data obtained at the end of a 45 minute kinetic run to select pixels with the greatest 610 nm: 540 nm absorbance ratio. After data acquisition, the following steps were followed: [0081] 1. The 610 nm image from the Absorbance project was flat-fielded with the T0 image from the Timecourse project in order to generate a reference image. [0082] 2. Using this reference image, a pixel-based spectral analysis is created. The software wizard automatically identifies pixels with grayvalues in the lowest tenth percentile. These pixels correspond to the pixels with the highest absorbances at 610 nm. As previously discussed, the user can override these automatic selections by painting on the image or redefining the high and low grayvalue range for ROIs. [0083] 3. A microcolony-free region on the edge of the membrane disk is selected for I0. These values are input into the Beer-Lambert equation to calculate pixel absorba...

example 2

[0089] In this second sorting example, an analysis was first performed using the timecourse data obtained during a 45 minute kinetic run to select pixels meeting specific temporal criteria. In other examples, this kinetic run can be longer or shorter. In this case, the temporal criterion is the fastest absorbance increase at 610 nm. The following steps were followed: [0090] 1. The T=600 second image is flat-fielded with the T0 image from the Timecourse project in order to generate a reference image. [0091] 2. Using this reference image, a pixel-based kinetic analysis is created. The software wizard automatically identifies pixels with grayvalues in the lowest tenth percentile, corresponding to high absorbance at 610 nm and significant Abg-catalyzed product formation. As previously discussed, the user can override these automatic selections by painting on the image or redefining the high and low grayvalue range for ROIs. [0092] 3. A microcolony-free region on the edge of the membrane...

example 3

[0098] In a third sorting example, spectral data obtained at the end of a kinetic run (or during the run) is used to determine ROIs meeting a specific spectral criteria without performing a complete contour plot based spectral analysis. This is done by generating a reference image from absorbance images as previously described. Using the Abg experiment as an example, the 610 nm image can be divided by the 540 nm image and the pixels with the lowest grayvalues would correspond to the ‘bluest’ pixels. If a satisfactory pixel cutoff value has been previously determined, one can use this cutoff value to select ROIs without performing the entire spectral analysis and sorting described in steps 1-5 of EXAMPLE 1 above. A single reference image based on spectral data is generated and this image is used for the kinetic analysis as listed in steps 7-8.

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Abstract

Complex multidimensional datasets generated by digital imaging spectroscopy can be organized and analyzed by applying software and computer-based methods comprising sorting algorithms. Combinations of these algorithms to images and graphical data, allow pixels or features to be rapidly and efficiently classified into meaningful groups according to defined criteria. Multiple rounds of pixel or feature selection may be performed based on independent sorting criteria. In one embodiment sorting by spectral criteria (e.g., intensity at a given wavelength) is combined with sorting by temporal criteria (e.g., absorbance at a given time) to identify microcolonies of recombinant organisms harboring mutated genes encoding enzymes having desirable kinetic attributes and substrate specificity. Restriction of the set of pixels analyzed in a subsequent sort based on criteria applied in an earlier sort (“sort and lock” analyses) minimize computational and storage resources. User-defined criteria can also be incorporated into the sorting process by means of a graphical user interface that comprises a visualization tools including a contour plot, a sorting bar and a grouping bar, an image window, and a plot window that allow run-time interactive identification of pixels or features meeting one or more criteria, and display of their associated spectral or kinetic data. These methods are useful for extracting information from imaging data in applications ranging from biology and medicine to remote sensing.

Description

RELATED APPLICATION DATA [0001] This application is a divisional of co-pending U.S. patent application Ser. No. 09 / 767,595, filed Jan. 22, 2001, now U.S. Pat. No. ______, which claims the benefit of U.S. Provisional Application No. 60 / 177,575, filed Jan. 22, 2000 and U.S. Provisional Application No. 60 / 186,034, filed Mar. 1, 2000, the entire disclosures of which are hereby incorporated by reference in their entirety.STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT [0002] The U.S. Government has certain rights in this invention pursuant to Grant No. R44GM5555470 awarded by the National Institutes of Health.FIELD OF THE INVENTION [0003] The current invention relates generally to the visualization and processing of multidimensional data, and in particular, to data formed from a series of images. BACKGROUND OF THE INVENTION [0004] Sophisticated analysis of imaging data requires software that can rapidly identify meaningful regions of the image. Depending on the size and n...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06T11/20G06V10/58G06V20/13
CPCG06K9/00127G06K9/0014G06K9/6253G06K2009/00644G06K2009/4657G06T7/0012G06K9/0063G06T2207/10016G06T2207/10056G06T2207/30024Y10S707/99937Y10S128/922G06T2200/24G06V20/69G06V20/695G06V20/194G06V20/13G06V10/58G06F18/40
Inventor YANG, MARY M.BYLINA, EDWARD J.COLEMAN, WILLIAM J.DILWORTH, MICHAEL R.ROBLES, STEVEN J.YOUVAN, DOUGLAS C.
Owner YANG MARY M
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