Image analysis – Applications – Biomedical applications
Reexamination Certificate
1999-07-16
2004-06-22
Mehta, Bhavesh M. (Department: 2621)
Image analysis
Applications
Biomedical applications
C345S619000, C382S133000, C382S173000, C382S291000, C422S062000, C435S006120, C436S043000
Reexamination Certificate
active
06754375
ABSTRACT:
TECHNICAL FIELD
This invention relates to methods and systems for interactively developing at least one grid pattern such as a microarray pattern, as well as an array set of such patterns, and a computer-readable storage medium having a program for executing the methods.
BACKGROUND ART
An array pattern can be used to establish the expected locations of spots containing fluorescently labeled DNA samples on a suitable carrier such as a microscope slide or membrane. These are commonly known as microarrays or bio-chips. The locations are typically used in subsequent quantitative analysis using software and image processing algorithms.
An example of a process that is arranged in a grid pattern is the microarray. As previously mentioned, microarrays are created with fluorescently labeled DNA samples in a grid pattern consisting of rows
22
and columns
20
typically spread across a 1 by 3 inch glass microscope slide
24
as illustrated in FIG.
1
. The rows
22
extend along the smaller dimension of the slide
24
and the columns
20
extend along the larger dimension of the rectangular slide
24
. Each spot
26
in the grid pattern (or array)
28
represents a separate DNA probe and constitutes a separate experiment. A plurality of such grid pattern comprises an array set
30
. Reference or “target” DNA (or RNA) is spotted onto the glass slide
24
and chemically bonded to the surface. Fluorescently labeled “probe” DNA (or RNA) is introduced and allowed to hybridize with the target DNA. Excess probe DNA that does not bind is removed from the surface of the slide in a subsequent washing process.
The purpose of the experiment is to measure the binding affinity between the probe and target DNA to determine the likeness of their molecular structures: complementary molecules have a much greater probability of binding than unrelated molecules. The probe DNA is labeled with fluorescent labels that emit light when excited by an external light source of the proper wavelength. The brightness of each sample on the slide
24
is a function of the fluor density in that sample. The fluor density is a function of the binding affinity or likeness of the probe molecule to the target molecule. Therefore, the brightness of each sample can be mapped to the degree of similarity between the probe DNA and the target DNA in that sample. On a typical microarray, up to tens of thousands of experiments can be performed simultaneously on the probe DNA, allowing for a detailed characterization of complex molecules.
Scanning laser fluorescence microscopes or microarray readers can be used to acquire digital images of the emitted light from a microarray as illustrated in FIG.
2
. The digital images are comprised of several thousand to hundreds of millions of pixels that typically range in size from 5 to 50 microns. Each pixel in the image is typically represented by a 16 bit integer, allowing for 65,535 different grayscale values. The microarray reader sequentially acquires the pixels from the scanned microarray and writes them into an image file and stored on a computer hard drive.
As illustrated in
FIG. 2
, a confocal laser microarray scanner or microarray reader is commonly used to scan the microarray slide
24
to produce one image for each dye used by sequentially scanning the microarray with a laser of a proper wavelength for the particular dye. Each dye has a known excitation spectra as illustrated in
FIG. 3 and a
known emission spectra as illustrated in FIG.
4
. The scanner includes a beam splitter
32
which reflects a laser beam
34
towards an objective lens
36
which, in turn, focuses the beam at the surface of slide
24
to cause fluorescence spherical emission. A portion of the emission travels back through the lens
36
and the beam splitter
32
. After traveling through the beam splitter
32
, the fluorescence beam is reflected by a mirror
38
, travels through an emission filter
40
, a focusing detector lens
42
and a central pinhole
44
. After traveling through the central pinhole
44
, the fluorescence beam is detected by a detector, all in a conventional fashion.
Analysis and Quantitation of the Microarray
Analysis of the fluor density at each spot location requires software that utilize image processing algorithms to locate all the spots and measure the brightness of the pixels in each spot. Typical image processing algorithms utilize one or more methods of thresholding the image to differentiate the background from a spot. Fixed and dynamic thresholding algorithms can be used depending upon the amount of variability of the background brightness across the image. Local area thresholding can also be used to minimize the negative affect of a variable background brightness.
Once a valid threshold is obtained, a temporary image of a lower number of grayscales, typically a binary image with two different grayscales, is created that clearly shows the background separated from the spots. The next step is typically to run a deformable grid algorithm to quantify the X and Y locations of each spot. The deformable grid algorithm is required to account for microarray manufacturing variations that commonly occur causing the spot positions to vary on an irregular basis. In order to measure the true brightness levels at each spot, the true spot position must be measured in real time since it can vary from array to array and from microarray to microarray.
Once the location of each spot is determined, additional image processing algorithms calculate the spot brightness of each spot. This is done with the original full resolution image. Typically, a sophisticated dynamic local area threshold technique is calculated using pixels within the local area
38
which belong to the background
40
and which belong to the spot
26
as illustrated in FIG.
3
. Only the pixels that represent the spot
26
are used to calculate the brightness value for that spot
26
. The final brightness value is typically calculated as the mean, mode, or sum of the pixels that represent the spot
26
. The brightness value of the background
40
is also important to researchers, and is calculated in similar manner by the mean, mode, or sum of the pixels that represent the local background
40
.
The location and analysis of spots and background process continues for each image obtained from the microarray sample by the microarray reader. The number can vary from one to four or more images per microarray and depends upon the number of differently labeled probe DNA samples used in the creation of the microarray.
The Need for a Regular Grid Pattern and How to Create One.
In order for a researcher to calculate the brightness of each spot and local background, also known as to “quantitate” the microarray, of a large microarray pattern, he/she must create a map or pattern of the microarray spot locations. To perform quantitation on a small number of spots, typically less than 100, a user can manually locate the spot. Manual location of spot patterns with more than 100 spots become cumbersome while larger arrays are impossible to quantitate in this manner. Also, with the progression of the microarray inspection towards automation, efficient and accurate methods to create the array patterns and quantitate the spots will be required. The microarray map is a template that is used by the software to efficiently search for the true locations of each spot in the pattern.
To fully describe a regular grid pattern, several parameters are required—the number of rows of spots, the number of columns of spots, the distance between each row, the distance between each column, and the average diameter of each spot. The most straightforward way of creating a regular grid pattern would be to manually enter appropriate values for each of the parameters above. This method, however, requires a-priori knowledge of the complete pattern of the array. This information is difficult to obtain because of the variation in microarray fabrication methods, typically either by hand and by automatic array spotting equipment, and the wide range of variability in each
Noblett David A.
Stephan Todd J.
Yang Jun
Brooks & Kushman P.C.
Desire Gregory
Mehta Bhavesh M.
Packard Bioscience Company
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