U.S. patent application number 16/150578 was filed with the patent office on 2019-01-31 for method and system for projecting image with differing exposure times.
The applicant listed for this patent is Life Technologies Holdings PTE Limited, PIERCE BIOTECHNOLOGY, INC.. Invention is credited to Jason ARAVICH, Yanpeng CAO, Eric HOMMEMA, Suk HONG, Nikki JARRETT, Benyong SHI, Kok Siong TEO.
Application Number | 20190034755 16/150578 |
Document ID | / |
Family ID | 58499625 |
Filed Date | 2019-01-31 |
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United States Patent
Application |
20190034755 |
Kind Code |
A1 |
HONG; Suk ; et al. |
January 31, 2019 |
METHOD AND SYSTEM FOR PROJECTING IMAGE WITH DIFFERING EXPOSURE
TIMES
Abstract
Systems and methods generate a projected image at an optimal
exposure time. Images are captured different exposure times. Pixels
that satisfy an intensity threshold percentage for each image are
selected. The intensity values of the selected pixels are then
evaluated to determine whether the selected pixels are distributed
above a lower intensity threshold and below an upper intensity
threshold. The linear relationship is projected to determine an
optimal exposure time that has an optimal exposure time duration
that exceeds each exposure time duration associated with each of
the captured images when the linear relationship exists between
each of the captured images. A projected image associated with the
optimal exposure time is generated from one or more of the captured
images.
Inventors: |
HONG; Suk; (San Marcos,
CA) ; JARRETT; Nikki; (Roscoe, IL) ; HOMMEMA;
Eric; (Roscoe, IL) ; ARAVICH; Jason;
(Pittsburgh, PA) ; CAO; Yanpeng; (Fujian, CN)
; SHI; Benyong; (Singapore, SG) ; TEO; Kok
Siong; (Singapore, SG) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Life Technologies Holdings PTE Limited
PIERCE BIOTECHNOLOGY, INC. |
Carlsbad
Carlsbad |
CA
CA |
US
US |
|
|
Family ID: |
58499625 |
Appl. No.: |
16/150578 |
Filed: |
October 3, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15294667 |
Oct 14, 2016 |
10115034 |
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16150578 |
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14264150 |
Apr 29, 2014 |
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15294667 |
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61818107 |
May 1, 2013 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 27/44721 20130101;
H04N 5/2353 20130101; H04N 5/2351 20130101; G06K 9/4647 20130101;
G06T 7/0002 20130101; G06T 2207/30072 20130101; G06T 2207/30168
20130101; G06T 2207/10144 20130101 |
International
Class: |
G06K 9/46 20060101
G06K009/46; G06T 7/00 20060101 G06T007/00; H04N 5/235 20060101
H04N005/235 |
Claims
1. A method for creating a projected image comprising: capturing an
image of an item or data relating to an image of an item with an
image capture device having a plurality of light sensing units, at
a first exposure time; receiving as an input or determining a
second exposure time; and creating a projected image of said item
at said second exposure time based upon said captured image or said
captured data relating to the image.
2. The method of claim 1 wherein each light sensing unit provides
an output relating to the image or the data relating to the image,
and wherein the creating step includes creating the projected image
based upon the output of the light sensing units.
3. The method of claim 2 wherein the creating step includes
creating the projected image by extrapolating the output of the
light sensing units.
4. The method of claim 3 wherein the extrapolating is based upon an
assumption of a linear or a known non-linear relationship between
the output of the light sensing units and exposure time.
5. The method of claim 1 wherein said second exposure time is
greater than said first exposure time.
6. The method of claim 1 wherein the first exposure time is less
than a minute.
7. The method of claim 1 wherein said receiving or determining step
includes determining said second exposure time by carrying out the
following steps: determining a signal intensity for each light
sensing unit based upon said captured image; determining a number
or percentage of said light sensing units that exceed a first
intensity value; determining a multiplier of said first exposure
time which will cause said number or percentage of light sensing
units to exceed a second intensity value; and applying said
multiplier to said first exposure time to arrive at said second
exposure time.
8. The method of claim 7 further comprising capturing a
supplemental image or data related to a supplemental image with the
image capture device at the second exposure time.
9. The method of claim 1 further comprising receiving user input of
a third exposure time, and creating a projected image of said item
at said third exposure time based upon said captured image or said
captured data relating to the image.
10. The method of claim 1 further comprising displaying said
projected image.
11. The method of claim 10 further comprising: receiving user input
relating to a desired exposure time, which is determined based upon
a user's review of the displayed projected image; and capturing a
supplemental image or data relating to a supplemental image with
the image capture device at the desired exposure time.
12. The method of claim 1 wherein the item is an object including
light-emitting or light absorbing electrophoretic bands under no
illumination or under illumination with different wavelength of
light thereon.
13. The method of claim 1 further comprising after the capturing
step, identifying a region of interest in said image or said data
relating to said image, and wherein the creating step includes
creating a projected image of only said region of interest.
14. The method of claim 13 wherein the item is an object and a
background, and wherein the region of interest is an area including
the object.
15. The method of claim 13 wherein the region of interest is at
least partially identified by a user.
16.-35. (canceled)
Description
[0001] The present application is a Divisional of U.S. patent
application Ser. No. 15/294,667 filed Oct. 14, 2016, which is a
Continuation-in-Part (CIP) of co-pending U.S. patent application
Ser. No. 14/264,150 filed Apr. 29, 2014, which claims the filing
benefit of U.S. Provisional Application Ser. No. 61/818,107 filed
May 1, 2013, each disclosure of which is hereby incorporated herein
by reference in its entirety.
FIELD OF THE INVENTION
[0002] The present invention relates generally to pixel
optimization, and more particularly, to projecting an image with
different exposure times to determine the exposure time for pixel
optimization.
BACKGROUND OF THE INVENTION
[0003] Life science researchers routinely obtain images of mixtures
of macromolecules, such as DNA, RNA and proteins, and their
fragments, from stained gel electrophoresis samples and Western
blots. The images are then captured and analyzed to obtain
data.
[0004] In order to separate the complex mixtures using
electrophoresis, several samples containing the mixture are applied
to separate, spaced apart locations on the electrophoresis gel. An
electrical current is then applied to the gel, which causes the
individual samples to migrate through the gel within their
prescribed lane or track, thereby generating an invisible lane on
the gel. The complex mixture is then separated by size, i.e.,
molecular weight, and net charge in the gel matrix. The larger,
higher molecular weight with low net charge molecules remain
relatively nearer the place of sample loading on the gel or
membrane. The smaller, lower molecular weight molecules with high
net charge migrate farther from the sample loading place of the gel
or membrane. Each individual segregation of sample is then
identified as a band. The gel can then be stained for total sample
visualization, or transferred to a membrane for visualization of a
specific target of interest by blotting (Western blotting in the
case of proteins, Southern blotting in the case of DNA, and
Northern blotting in the case of RNA). The researcher then images
the gel, membrane or blot, collectively termed a substrate or
object, to analyze the target(s) of interest for amount, relative
or absolute, purity, and molecular weight. Such analysis requires
detection and identification of the lanes and bands in the
image.
[0005] The images of the object are typically acquired using one or
more visualization means, such as ultra violet illumination, white
light illumination, fluorescence, or chemiluminescence.
[0006] Finding proper exposure time is an important factor
affecting image quality and it is important for successful,
accurate pixel intensity measurement on the acquired image. Various
auto exposure methods have been developed, but those methods are
either complex, inaccurate and/or disregard user input. Optimal
exposure time for image capture is not always dependent on pixel
intensity of the entire image or any particular region of the
image. In some cases, the user/operator can best define which
object(s) on the captured image should be the target for optimal
exposure determination.
[0007] Images of the substrate, or of other objects, can be
captured by any of a wide variety of structures or devices, but in
one case takes the form of an imaging device or image capture
device utilizing/comprising a CCD (Charge Coupled Device), but can
also be used with autoradiography, scanners, CMOS (Complementary
Metal Oxide Semiconductor) imagers, phosphor imagers, and others.
In the case of the CCD, such a system utilizes an array of
light-sensitive optical elements, such as pixels or other light
sensing units. The pixels are configured such that when light
(photons) are detected by the pixels, each pixel provides an output
in the form of an electrical signal that is proportional or related
to the intensity of the detected light (photons). Multiple pixels
or arrays of pixels can also be combined together using the
well-known binning technique, and in this case each group of binned
pixels can be considered a single pixel.
[0008] Each pixel has a limited capacity for maximum light
exposure, also known as its saturation point. If too many pixels
reach their saturation point for a given image, the image is
considered over-exposed. In contrast, if too many of the pixels
receive insufficient light, the image lacks sufficient contrast and
is considered under-exposed. Thus, when capturing images it is
helpful to determine the optimal exposure time so that data from
the image can be accurately captured. Use of the optimal exposure
time maximizes the dynamic range of the pixel intensities in the
image, and minimizes the number of pixels that are saturated.
[0009] In previous systems, in order to determine the proper
exposure time for image acquisition, a trial-and-error image
acquisition process or a complex, inaccurate automatic exposure
method was utilized. The user/operator would need to carry out
multiple image acquisitions with differing exposure times, compare
the images, and make estimates as to the best exposure time.
However this process is labor-intensive and also takes up usage of
the imaging equipment that would otherwise be put to productive
use.
SUMMARY OF THE INVENTION
[0010] The present invention overcomes the foregoing and other
shortcomings and drawbacks of known pump monitoring devices for use
in fluid circuits. While the invention will be described in
connection with certain embodiments, it will be understood that the
invention is not limited to these embodiments. On the contrary, the
invention includes all alternatives, modifications and equivalents
as may be included within the spirit and scope of the present
invention.
[0011] In accordance with the principles of the present invention,
a computer implemented method generates a projected image at an
optimal exposure time. A plurality of images is captured with each
image captured at a different exposure time. Each image of the
plurality of images is assessed and pixels are selected that have
intensity values that satisfy an intensity threshold percentage for
each image. The intensity threshold percentage is a percentage of
pixels included in the image with each pixel included in the
percentage of pixels having an intensity value that is higher than
an intensity value for each pixel that is excluded from the
percentage of pixels. The intensity values are evaluated as to
whether the selected pixels are distributed above a lower intensity
threshold and below an upper intensity threshold. Whether a linear
relationship exists of the intensity values between each of the
captured images with the intensity values of the selected pixels
that are above the lower intensity threshold and below the upper
intensity threshold between each of the captured images is
determined. A linear relationship is projected to determine an
optimal exposure time that has an exposure time duration that
exceeds each exposure time duration associated with each of the
captured images when the linear relationship exists between each of
the captured images. The optimal exposure time is based upon a
pixel intensity saturation level threshold. A projected image
associated with the optimal exposure time is generated from one or
more of the captured images.
[0012] According to another aspect of the present invention, a
system for generating a projected image with an optimal exposure
time includes an image capturing device. The image capturing device
is configured to capture a plurality of images with each image
captured at a different exposure time. The system also includes at
least one processor with a memory coupled with the processor. The
memory includes instructions, that when executed by the processor
cause the processor to assess each image of the plurality of images
and select pixels that have intensity values that satisfy an
intensity threshold percentage for each image. The intensity
threshold percentage is a percentage of pixels included in the
image with each pixel included in the percentage of pixels having
an intensity value that is higher than an intensity value for each
pixel that is excluded from the percentage of pixels. The processor
is configured to evaluate whether the intensity values of the
selected pixels are distributed over a lower intensity threshold
and below an upper intensity threshold. The processor is configured
to determine whether a linear relationship exists of the intensity
values between each of the captured images with the intensity
values of the selected pixels that are above the lower intensity
threshold and below the upper intensity threshold between each of
the captured images. The processor is configured to determine an
optimal exposure time that has an exposure time duration that
exceeds each exposure time duration associated with each of the
captured images when the linear relationship exists between each of
the captured images. The optimal exposure time is based upon a
pixel intensity saturation level threshold. The processor is
configured to generate a projected image associated with the
optimal exposure time from one or more of the captured images.
[0013] The above and other objectives and advantages of the present
invention shall be made apparent from the accompanying drawings and
description thereof.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] The accompanying drawings, which are incorporated in and
constitute a part of this specification, illustrate an embodiment
of the invention and, together with a general description of the
invention given above, and the detailed description of the
embodiments given below, serve to explain the principles of the
invention.
[0015] FIG. 1 is a flowchart of an exemplary process for
determining the optimal exposure time for image acquisition based
upon a single image acquisition according to one embodiment of the
invention.
[0016] FIG. 2 is an image view of an object at an initial exposure
time according to one embodiment of the invention.
[0017] FIG. 3 is a projected image view of the object of FIG. 2 at
an exposure time calculated to be optimal according to one
embodiment of the invention.
[0018] FIG. 4 is a projected image view of the object of FIG. 2 at
an exposure time longer in duration than that of FIG. 3 according
to one embodiment of the invention.
[0019] FIG. 5 is a projected image view of the object of FIG. 2 at
an exposure time shorter than that of FIG. 3 according to one
embodiment of the invention.
[0020] FIG. 6 depicts various projected image views, at a 10 minute
exposure time, as compared to an actual image at a 10 minute
exposure time according to one embodiment of the invention.
[0021] FIG. 7 is an image view of an object in front of a
background according to one embodiment of the invention.
[0022] FIG. 8 is a flowchart of an exemplary process for
determining the optimal exposure time for an image according to one
embodiment of the invention.
[0023] FIG. 9 is an image view of images captured at different
exposure times according to one embodiment of the invention.
[0024] FIG. 10 is an image view of a threshold value that is
determined for the images and each of the pixels included in each
of the images is sorted based on the threshold value according to
one embodiment of the invention.
[0025] FIG. 11 is an image view of relationships that are plotted
for each of the images according to one embodiment of the
invention.
[0026] FIG. 12 is an image view of the optimal exposure time that
is determined based on the images that have exposure times that
have the most separated exposure time durations according to one
embodiment of the invention.
[0027] FIG. 13 is a schematic view of a general purpose computer
system suitable for operating the method and system disclosed
herein according to one embodiment of the invention.
DETAILED DESCRIPTION OF THE INVENTION
[0028] In the Detailed Description herein, references to "one
embodiment", "an embodiment", an "example embodiment", etc.,
indicate that the embodiment described may include a particular
feature, structure, or characteristic, but every embodiment may not
necessarily include the particular feature, structure, or
characteristic. Moreover, such phrases do not necessarily refer to
the same embodiment. Further, when a particular feature, structure,
or characteristic is described in connection with an embodiment of
the present invention, Applicants submit that it may be within the
knowledge of one skilled in the art to affect such feature,
structure, or characteristic in connection with other embodiments
of the present invention whether or not explicitly described.
[0029] Embodiments of the present invention may be implemented in
hardware, software, or any combination thereof. Embodiments of the
invention may also be implemented as instructions stored on a
machine-readable medium, which may be read and executed by one or
more processors. A machine-readable medium may include any
mechanism for storing or transmitting information in a form
readable by a machine (e.g., a computing device). For example, a
machine-readable medium may include read only memory (ROM); random
access memory (RAM); magnetic disk storage media; optical storage
media; flash memory devices; electrical, optical, acoustical or
other forms of propagated signals (e.g., carrier waves, infrared
signals, digital signals, etc.), and others. Further, firmware,
software, routines, instructions may be described herein as
performing certain actions. However, it should be appreciated that
such descriptions are merely for convenience and that such actions
in fact result from computing devices, processors, controllers, or
other devices executing the firmware, software, routines,
instructions, etc.
[0030] For purposes of this discussion, each of the various
components discussed can be considered a module, and the term
"module" shall be understood to include at least one software,
firmware, and hardware (such as one or more circuit, microchip, or
device, or any combination thereof), and/or any combination
thereof. In addition, it will be understood that each module can
include one, or more than one, component within an actual device,
and each component that forms a part of the described module can
function either cooperatively or independently of any other
component forming a part of the module. Conversely, multiple
modules described herein can represent a single component within an
actual device. Further components within a module can be in a
single device or distributed among multiple devices in a wired or
wireless manner.
[0031] The following detailed description refers to the
accompanying drawings that illustrate exemplary embodiments of the
present invention. Other embodiments are possible, and
modifications can be made to the embodiments within the spirit and
scope of this description. Those skilled in the art with access to
the teachings provided herein will recognize additional
modifications, applications, and embodiments within the scope
thereof and additional fields in which embodiments would be of
significant utility. Therefore, the detailed description is not
meant to limit the present invention to the embodiments described
below.
[0032] Thus, in one embodiment the present invention provides a
system and method for providing, and/or enabling a user/operator to
determine the optimal exposure time for image acquisition based
upon only a single image acquisition. With reference to FIG. 1, a
test or preliminary image of the substrate or object is first
obtained under a test, or first, exposure time. The timing/value of
the test exposure time can vary as needed, and according to the
specific equipment and nature of image expected to be acquired.
However, the test exposure time in many cases is shorter than the
normal or expected full or optimal exposure time. In one case, for
example, the test exposure time is between about 10 milliseconds
and about 10 minutes, or in another case between about 1 second and
about 60 seconds, or more particularly between about 5 seconds and
about 25 seconds, or in one case less than about 60 minutes, or in
another case less than about 1 minute.
[0033] After the image (test image) at the test exposure time is
acquired, noise subtraction algorithms (including accounting for
dark noise, bias noise, flat field, etc.) are applied to the
data/image in a well-known manner. The signal intensity, or output,
of each pixel can then be arranged/ordered. The system then
analyzes the number of pixels that exceed a threshold value (and/or
are projected to exceed a threshold value), and uses that number to
determine the optimal exposure time.
[0034] By way of example, in a 16 bit image system, the output of
each pixel can be an integer ranging from 0 to 65,535. Of course, 8
bit, 32 bit, gray or color or other imaging systems can be
utilized, and the output values of each pixel can therefore vary
widely. The system can then analyze the number of pixels that are
at saturation value (e.g. at 65,535 for a 16 bit image) or at some
value close to saturation (e.g. 90% of saturation in one case, at a
value at or exceeding 58,982), or some other threshold value. The
system will have pre-programmed into it, or stored therein or
provided thereto, a number representing the number of pixels or a
percentage of pixels which should be at or above the threshold
value to provide the desired/optimal image. For example, in one
case it may be known that a best image can be expected to be
provided if 5% of the pixels are at or near saturation or above the
threshold value. Alternately, rather than considering a percentage
of pixels, the system may instead analyze the raw number of pixels
that are at or near saturation or above the threshold value.
[0035] Thus, in the case of a 2.1 megapixel CCD array, and
continuing to use the 5% number as an example, the system may use a
number of 0.105 megapixels at the cut-off which are desired to be
above the threshold value. Of course, the cut-off percentage and
cut-off number of pixels can vary widely depending upon the type of
image desired/expected, the properties of the equipment, etc. In
addition, it should be understood that rather than utilizing the
cut-off number at this point, projected values for each pixel can
be generated, and then a threshold is applied, and/or the optimal
exposure time calculated in other manners.
[0036] As mentioned above, in addition to using a percentage of
pixels that can be at or above the threshold, a raw number of
pixels can be used for this purpose. To determine the raw number of
pixels that can be at or above the threshold, various methods can
be utilized including but not limited to: A) analyzing the number
of pixels typically encompassed by an object of interest on an
image at different binning levels, resolutions, etc. and/or B)
analyzing the number of pixels that reach saturation at different
binning levels, resolutions, etc. using the maximum exposure time
when no object is imaged (i.e., background such that only
uncompensated random noise is present in the image).
[0037] It can be assumed that the intensity value for each pixel
will increase linearly/proportionally, or at some known non-linear
rate, with respect to increased exposure time. Thus once the data
for the test image is known, and the threshold value for high
intensity pixels are known, the optimal exposure time for the image
can be calculated. Continuing with the example set forth above and
assuming a linear relationship between pixel intensity and exposure
time, it can be seen that if it is desired that the image have
0.105 megapixels of its 2.1 pixels be close to saturation (at or
exceeding a threshold value of 58,982), the original exposure time
(say, 15 seconds) should be multiplied by a number, which needs to
be determined, to provide the desired output. For example, it may
be determined that if the test exposure time is increased 4 times,
0.105 megapixels of the test image will be at and/or exceed the
threshold value, resulting in an optimal exposure time of
4.times.15 seconds, or 60 seconds.
[0038] In one case, then, the optimal exposure time can be
calculated by: 1) determining the number of pixels that are desired
to exceed the threshold value; 2) from the test image data
conducted at a test image exposure, selecting the number of pixels,
from step 1, of pixels with the greatest intensity; 3) from the
group of pixels defined in step 2) selecting the pixel with the
smallest intensity value; 4) dividing the intensity value from step
3) by the time of the test image exposure; and 5) dividing
threshold value by the numerical result of step 4), resulting in
the optimal exposure time.
[0039] By way of example, consider the following 16 bit image data
from a simple eight pixel array, which represents pixel output
received after a 15 second exposure time:
TABLE-US-00001 TABLE 1 Pixel Number Pixel Output 1 5,000 2 10,000 3
15,000 4 20,000 5 25,000 6 30,000 7 35,000 8 40,000
[0040] In this case, let it be assumed that it is desired that 25%
of the pixels exceed a cut-off value of 58,982. In this case, then,
under step 1 above it is determined that two pixels, or 25% of the
eight total pixels, are desired to exceed the threshold value.
Under step 2 from above, the two pixels of highest intensity are
selected, which are pixels number 7 and 8. Under step 3, the pixel
with the smallest intensity value between pixels 7 and 8 (pixel 7)
is selected. The intensity value for pixel 7 (35,000) is then
divided by the test time exposure (15 seconds) resulting in a value
of 2333. The cut-off value (58,982) is then divided by 2333,
resulting in a value of about 25.3 seconds. 25.3 seconds can then
be considered the projected optimal exposure time.
[0041] Once the optimal projected exposure time is determined, a
projected image can be generated, projecting/extrapolating how the
image will look based upon the test image data, and assuming that
intensity varies directly with exposure time (i.e. assuming a
linear, or a known non-linear relationship between intensity and
exposure time). Continuing with the example above, the pixel output
of Table 1 will be multiplied by 25.3/15 resulting in the following
output:
TABLE-US-00002 TABLE 2 Pixel Number Pixel Output 1 8,433 2 16,867 3
25,300 4 33,733 5 42,167 6 50,600 7 59,033 8 65,535* (67,466) *In
this example, values that exceed the maximum intensity for a pixel
(65,535) are assigned the maximum intensity value (16 bit
image)
[0042] In this case, then, it can be seen that 25% of the pixels
exceed the pixel threshold of 58,982, as desired. Of course, there
are a wide variety of mathematical methods and algorithms and pixel
math techniques that can be utilized to determine an exposure time
that is projected to provide a minimum number/percentage of pixels
that exceed a threshold value, and the technique outlined above is
simply one example. The system and method specified and claimed
herein is not limited the specific technique shown above. In any
case, once the projected optimal exposure time image is calculated,
the projected image can be generated and presented to the
user/operator.
[0043] For example, FIG. 2 illustrates a test image at a test
exposure of ten seconds in the illustrated embodiment, as shown in
the exposure time display 10. In this figure the image is slightly
underexposed. The calculations outlined above can be applied to the
image/image data of FIG. 2, resulting in a projected optimal
exposure time of 17 seconds. FIG. 3, then, shows an image based
upon the data/image of FIG. 2 as presented to a user at a projected
optimal exposure time, or a second exposure time. In this case the
projected optimal exposure time is 17 seconds, as calculated by the
system in the manner outlined above.
[0044] The system may also provide the option to a user to manually
adjust the exposure time (i.e. via a user input device, resulting
in a third or user-created exposure time), and the system can
adjust the projected image accordingly. In other words, each pixel
output can be adjusted in a manner directly proportional to the
input exposure time to present an image to the user/operator, so
that the user/operator can see how the image is projected to look
at a user-defined exposure time. In the illustrated embodiment, the
input device takes the form of slider bar 12 that can be adjusted
by a user via a cursor control device of the like. In one case, the
projected image 14 is displayed in real time to match the position
of the slider bar 12 as it is moved so that the user/operator is
provided with instantaneous feedback.
[0045] When the slider bar 12 is utilized, the numerical value of
the exposure time is displayed in the exposure time display 10.
Alternately, or in addition, the user/operator may be able to
directly enter the numerical value of the desired exposure time to
be displayed, or control the numerical values with navigation
buttons 16, etc.
[0046] Thus, while the system can generate a projected optimal
exposure time, it is understood that in some cases the
user/operator may desire an exposure time different from the
calculated optimal exposure time, based upon the user/operator's
review of the displayed projected image. FIG. 4 presents a
projected image 14 where the user/operator had increased the
exposure time (to 5 minutes, in the illustrated embodiment) to the
point where the image 14 is over-exposed. FIG. 5 presents a
projected image where the user/operator had decreased the exposure
time (to 8 seconds, in the illustrated embodiment) to the point
where the image 14 is under-exposed.
[0047] The test/initial exposure time can be selected to provide an
accurate baseline image for use in projecting images and/or
determining optimal exposure times, while also providing
convenience for the user. In particular, the longer the
test/initial exposure time, the more accurate the (longer exposure
time) projected image(s) will be. FIG. 6 illustrates estimated
images for a ten minute exposure image, based upon test/initial
exposure times of 10 seconds, 15 seconds, 20 seconds, and 30
seconds. FIG. 6 also illustrates an actual image taken at 10
minutes. As can be seen, the longer the initial exposure time (or
closer to the actual exposure time), the more accurate the
projected image. On the other hand, making the initial exposure
time too long can take up time and resources.
[0048] The user can modify the exposure time and/or accept the
projected optimal exposure time as presented by the system, to
select/define the optimal/desired exposure time. Once the
optimal/desired exposure time is selected/defined, an image can be
acquired at the selected/defined exposure time and used for further
processing. The actual image acquired at the optimal or selected
exposure time will, of course, vary from the projected/preview
images as outlined above. In particular, in the projected/preview
images based upon the test image, the noise levels will be
disproportionally increased as compared to the actual image
acquired at the optimal/selected exposure time. Therefore, further
noise reduction processes can be applied to create clean
projected/preview images. However the system and method enables
optimal exposure time to be determined/selected using, in one case,
only a short, preliminary image acquisition time. The test image
may not, in some cases, provide visual data to a human viewing the
image, but may provide sufficient information after analysis to
provide the benefits outlined above. The system and method can be
used with nearly any light imaging system, but may find particular
utility with detecting low light objects.
[0049] In one case, the creation and display of projected images at
differing exposure times, and/or the determination of optimal
exposure time, can be limited to only a certain area or portion of
the test/initial image. In particular, in some cases the
test/initial image may include the entire substrate or object,
along with a background area. The object and background area may be
present a relatively high contrast when imaged. For example, the
object may be generally white or light, and the background may be
generally black or dark. In this case, or in other cases where
possible, it may be advantageous to distinguish the object from the
background area, and apply the techniques described herein only to
the object/substrate.
[0050] For example, FIG. 7 shows an image of a light
substrate/object in front of a dark background. After this
test/initial image is acquired, the system/method may determine the
outline of the object. Due to the high contrast between the object
and the background of FIG. 7, any of a wide variety of
edge-locating or contrast-locating algorithms may be utilized to
determine the boundary between the object and background. In the
illustrated embodiment the corners of the object are located using
well-known corner-locating algorithms, and the edges of the object
determined by projecting straight lines between the corners. Of
course, the object can have various other shapes besides
rectangular, in which case other suitable algorithms are utilized
to determine the outer edges of the object.
[0051] Once the shape and dimensions of the object are determined,
all data relating to areas outside the object can be ignored, and
not form the basis for any further image projecting or optimal
exposure time determinations as outlined above. For example, in one
case each pixel determined to be outside the object is set to an
arbitrary value (0, in one example), or each pixel simply remains
at its value from the initial/test image. In either case, the
exposure processing outlined above is carried out only on the image
pixels determined to be on/within the object, which can
significantly reduce amount of pixels to be processed. This process
thereby enables more rapid calculations, providing a quicker
response time and saving computing resources.
[0052] Thus, as outlined above, only a portion of the
originally-acquired image, or a region of interest ("ROI"), may be
utilized for the creation and display of projected images at
differing exposure times, and/or the determination of optimal
exposure time. In the illustrated embodiment, the entire
substrate/object forms the ROI. However, the ROI can constitute
various other areas, such as particular areas of interest in the
substrate/object.
[0053] As noted above, a threshold value may be determined for a
particular image captured at a particular exposure time. The
threshold value is the intensity value that an individual pixel
included in the particular image is to reach and/or exceed in order
to satisfy the threshold value. The image may sufficiently display
the particular characteristics necessary for a user to adequately
analyze the specimen when a sufficient percentage of pixels
included in the particular image that each satisfy the threshold
value is reached. The intensity value for a pixel is the amount of
energy that the pixel emits relative to the detection capabilities
of the detector that the pixel is associated with. An increased
intensity value for a pixel may trigger an increased sensitivity of
the detector that the pixel is associated with such that the pixel
contributes to a higher quality image. The greater the intensity
value generated by the greater percentage of pixels may result in
an increased sensitivity of the detector associated with each of
the pixels and thus generating a higher quality image.
[0054] The intensity value for each pixel may range from zero to a
saturation value that is based on the total quantity of pixels
included in an image. For example, each pixel included in a 16 bit
image may have an intensity value that ranges from 0 to 65,535. The
intensity value of each pixel may be based on various factors
including the exposure time in which each pixel is exposed to
capturing the image. The exposure time is the amount of time that
each pixel is exposed to the energy emitted by an environment that
the imaging system is attempting to capture. As the exposure time
for each pixel increases, the amount of energy that is detected by
each pixel increases based on an increase in sensitivity of the
detector that each pixel is associated with thus resulting in an
increased intensity value for each pixel. As the exposure time for
each pixel increases, the intensity value for each of the pixels
may also increase resulting in a higher quality image.
[0055] The threshold value may be a percentage that represents an
intensity value as compared to the saturation value that a pixel
included in the image is to satisfy. The saturation value may be
the maximum intensity value that the pixel may reach. The greater
percentage of pixels that satisfy the threshold value results in a
greater percentage of pixels that have intensity values that are
within a percentage of the saturation value that results in a
higher quality image. The percentage of pixels that have intensity
values that are within a percentage of the saturation value to
generate an image of sufficient quality such that the user may
adequately analyze the specimen is the pixel intensity saturation
level threshold.
[0056] For example, the pixel intensity saturation level threshold
that is to be satisfied to generate a 16 bit image of sufficient
quality is to have 10% of the total pixels in an image that have
intensity levels that exceed 64,000. In other words, the intensity
saturation level threshold that is to be satisfied for the 16 bit
image which is to have 10% of the total pixels in an image satisfy
a threshold value of 97% in which an intensity level of 64,000 is
97% of the saturation value of 65,535 for a 16 bit image.
[0057] In an embodiment, the pixels included in the image that have
intensity values with the highest intensity as compared to the
remaining pixels included in the image that have lower intensity
values may be selected to generate an image of sufficient quality
such that the user may adequately analyze the specimen. The pixels
may be evaluated to determine whether each pixel included in the
image is included in the intensity threshold percentage. The
intensity threshold percentage may be a percentile of pixels that
have the highest intensity levels as compared to the remaining
pixels not included in the intensity threshold percentage. For
example, the intensity threshold percentage may include pixels
included in the image that have intensity levels that are in the
top 1% as compared to the intensity levels of the pixels in the
remaining 99%. The intensity threshold percentage may be adjusted
by the user.
[0058] As noted above, the intensity value of each pixel may be
increased as the exposure time for each pixel increases. However, a
maximum exposure time may exist in which each pixel may become
oversaturated if exposed to the energy emitted by the environment
beyond the maximum exposure time. The detector that each pixel is
associated with may no longer exercise its detection
characteristics if exposed to a significant amount of energy
emitted by the environment after being exposed to such energy for
the maximum exposure time. Pixels that are oversaturated may
negatively impact the quality of the image.
[0059] Thus, the threshold value for a particular image that the
pixels are to satisfy in order to satisfy the pixel intensity
saturation level threshold of the particular image may be
determined. For example, the threshold value of a 16 bit image that
the pixels are to satisfy in order to satisfy the pixel intensity
saturation level threshold is 90% where the pixels to satisfy the
threshold value are to have an intensity value of 58,981 which is
90% of the saturation value of 65,535 for a 16 bit image. The
percentage of pixels that then satisfy the threshold value of 90%
to satisfy the pixel intensity saturation level threshold is 15%
where 15% of the total pixels in the 16 bit image satisfy the
threshold value of 90% where 15% of the total pixels have intensity
values that reach and/or exceed 58,981.
[0060] After the threshold value to be utilized is determined or
otherwise set for the particular image, the exposure time that is
required to ensure that the portion of pixels satisfy the threshold
value may be determined. However, determining the exposure time
that is required to ensure the pixel intensity saturation level
threshold of particular image is satisfied may not be an optimal
exposure time in order to attain an increased percentage of pixels
that satisfy the threshold value. As noted above, the threshold
value is the percentage of the saturation value for the particular
image that an individual pixel is to satisfy in order to satisfy
the threshold value.
[0061] However, the quality of the image that is to be evaluated by
the user not only increases based on the threshold value for an
individual pixel in the particular image to satisfy but also the
percentage of pixels included in the particular image that satisfy
the threshold value which is the pixel intensity saturation level
threshold. As noted in the example above, 15% of the pixels
included in the particular image are required to satisfy the
threshold level of having intensity levels within 90% of the
saturation level of the particular image in order to generate an
image of sufficient quality for the user to adequately examine the
specimen.
[0062] The percentage of pixels that have intensity values that
satisfy the threshold value may also increase as the exposure time
increases. For example, a first image may have 40,000 pixels that
satisfy the threshold level of 90% with an exposure time of 10 ms
while a second image may have 45,000 pixels that satisfy the
threshold level of 90% with an exposure time of 20 ms. Despite both
the first image and the second image satisfying the threshold level
of 90%, the second image with an exposure time of 20 ms has a
greater percentage of pixels that exceed the threshold level of 90%
as compared to the first image with an exposure time of 10 ms that
also exceeds the threshold level of 90%. Thus, simply determining
the exposure time such that individual pixels included in a
particular image satisfy the threshold level based on a particular
exposure time may not result in an optimal exposure time with an
increased percentage of pixels that satisfy the threshold
level.
[0063] As a result, simply determining an exposure time such that
individual pixels included in a particular image satisfy the
threshold level based on a particular exposure time may limit the
percentage of pixels that satisfy the threshold level. As noted in
the example above, simply determining an exposure time of 10 ms
because the exposure time is a relatively short exposure time
allowing a significant increase in specimens that may be analyzed
in a given period of time may result in a significantly less
percentage of pixels that satisfy the threshold level as compared
to an exposure time of longer duration.
[0064] However, simply determining an exposure time that has a
significant increase in duration to ensure that the maximum
percentage of pixels that satisfy the threshold level is attained
increases the risk in oversaturation of the pixels. As noted above,
oversaturation of the pixels may occur when the pixels are exposed
to the energy emitted by the environment to an extent that the
detection characteristics of the detector that the pixels are
associated with are no longer exercised resulting in a negative
impact on the image by the oversaturated pixels. Further, a
significantly increased exposure time may also unnecessarily
increase the amount of time required to analyze each specimen and
thus decreasing the amount of specimens that may be analyzed during
a period of time when an exposure time of shorter duration may
adequately provide an image with sufficient quality for the user to
adequately examine the specimen while increasing the amount of
specimens that may be analyzed during the period of time.
[0065] Rather than determining the optimal exposure time from a
single image, the optimal exposure time may be determined from a
linear relationship that exists between multiple images each
captured at different exposure times. As noted above, the
percentage of pixels that have intensity values that satisfy the
threshold value may increase as the exposure time increases. Thus,
for each individual image captured at a different exposure time has
a corresponding percentage of pixels that satisfy the threshold
value that increases for each increased exposure time. The linear
relationship between the percentages of pixels that satisfy the
threshold value for the multiple images may be determined to
project the optimal exposure time in order to capture an increased
percentage of pixels that exceed the pixel intensity saturation
level threshold without oversaturation.
[0066] One such implementation of determining the optimal exposure
time for an image is illustrated by process 800 in FIG. 8. Process
800 includes four primary steps: capture a quantity of images at
different exposure times 810, for each captured image, categorize
pixels and determine a threshold 820, verify whether a linear
relationship exists based on the captured images 830, and determine
an optimal exposure time based on at least one of the captured
images 840. Steps 810-840 are typically implemented in a computer,
e.g., via software and/or hardware, e.g., computer system 100 of
FIG. 13.
[0067] In step 810, a quantity of images may be captured at
different exposure times. As shown in FIG. 9, a plurality of images
910(a-n), where n is an integer equal to or greater than three, may
be captured with each of the images 910(a-n) being captured at
different exposure times. Each of the exposure times for each of
the corresponding images 910(a-n) may be determined such that a
range of exposure times in which different percentages of the
pixels that satisfy the threshold value may be generated.
[0068] The exposure times may range from a first exposure time that
has a shortest exposure time duration for the corresponding image
910a to an nth exposure time that has a longest exposure time
duration for the corresponding image 910n. The first exposure time
with the shortest exposure time duration may be sufficient in
duration such that a percentage of the pixels included in the
corresponding image 910a satisfy the threshold. The nth exposure
time with the longest exposure time duration may be greater than
each of the exposure times for corresponding images 910(a-c)
without having oversaturated pixels.
[0069] The quantity of images 910(a-n) may include a minimum of
three images such that a linear relationship may be established
between three exposure times that correspond to the minimum of
three images. However, the quantity of images 910(a-n) may range up
to any quantity of images greater than three. As the quantity of
images 910(a-n) increases, the accuracy of the resulting linear
relationship between the different exposure times that correspond
to each of the images 910(a-n). For example, the exposure time for
image 910a may be 10 ms, the exposure time for image 910b may be 20
ms, the exposure time for image 910c may be 40 ms, and the exposure
time for image 910n may be 80 ms. In an example embodiment, step
810 may be performed by processor 102 of computer system 100 as
shown in FIG. 13. The different exposure times for images 910(a-n)
may be based upon preset defaults tailored to a particular
application, e.g., fluorescent or chemiluminescent detection, and
may also be customized by a user.
[0070] In step 820, the pixels for each of the images 910(a-n) may
be categorized based on whether the pixels satisfy an intensity
threshold percentage. As shown in FIG. 10, an intensity threshold
percentage for the images 910(a-n) may be determined and each of
the pixels included in each of the images 910(a-n) may be sorted
based on the intensity threshold percentage. The intensity
threshold percentage may be a percentile of pixels that have the
highest intensity levels as compared to the remaining pixels not
included in the intensity threshold percentage. For example, the
intensity threshold percentage may include pixels included in the
image that have intensity levels that are in the top 1% as compared
to the intensity levels of the pixels in the remaining 99%. The
intensity threshold percentage may be based upon preset defaults
tailored to a particular application, and may also be adjusted by
the user.
[0071] As noted above, the threshold value for each of the images
910(a-n) may be determined such that the intensity value for an
individual pixel is to be within a percentage of the saturation
value for the image in order for the individual pixel to satisfy
the threshold. The intensity value that each individual pixel may
have may range from the lowest intensity 1010 when the individual
pixel has the lowest intensity value such as approximately zero to
the highest intensity 1030 when the individual pixel has a highest
intensity value when the individual pixel is saturated.
[0072] The intensity threshold percentage 1040 for each of the
images 910(a-n) may be determined such that the same intensity
threshold percentage 1040 is applied to each of the images
910(a-n). As noted above, the quality of the image not only
increases based on whether an individual pixel is included in the
intensity threshold percentage 1040 but also the percentage of the
pixels that are included in intensity threshold percentage 1040. In
order for the linear relationship between the percentage of pixels
that are included in the intensity threshold percentage 1040 for
each of the images 910(a-n) to be determined, the intensity
threshold percentage 1040 applied to each of the images 910(a-n) is
constant. For example, the intensity threshold percentage 1040
applied to each of the images 910(a-n) is 99% such the pixels with
intensity levels in the top 99% of the pixels included in the image
are included in the intensity threshold percentage 1040.
[0073] After each of the pixels that have intensity levels that are
included in the intensity threshold percentage 1040, such as the
pixels with intensity levels that are in the 99th percentile as
compared to the remaining pixels included in the image, are
selected, those selected pixels may then be evaluated. A lower
intensity threshold and an upper intensity threshold may be
established for the selected pixels that are included in the
intensity threshold percentage 1040. The lower intensity threshold
may be a lower intensity value that may exclude pixels included in
the intensity threshold percentage 1040 with intensity values that
are below the lower intensity threshold. The upper intensity
threshold may be an upper intensity value that may exclude pixels
included in the intensity threshold percentage 1040 with intensity
values that are above the upper intensity threshold.
[0074] The lower intensity threshold may be an intensity value
threshold that filters out pixels included in the intensity
threshold percentage 1040 with intensity values that may be
associated with noise. The lower intensity threshold may be an
intensity value that an intensity value of a pixel is to exceed in
order to ensure that the intensity value of the pixel is not
associated with noise. For example for a 16 bit image with an
intensity threshold percentage 1040 of 99%, the lower intensity
threshold may be 64,979 which is a value that is 100 units higher
than the intensity value of 64,879 which is the intensity value
that is within 99% of the saturation value of a 16 bit image of
65,535. Any pixel included in the intensity threshold percentage
1040 that exceeds the lower intensity threshold of 64,979 may be
considered as a pixel reflects the actual signal rather than noise.
The user may adjust the lower pixel intensity threshold, which may
also be preset based upon a particular application and/or the
characteristics of the system responsible for imaging the specimen
(e.g., the specific properties of the excitation source(s) or the
detector).
[0075] The upper intensity threshold may be an intensity value
threshold that filters out pixels included in the intensity
threshold percentage 1040 with intensity values that may be
oversaturated. The upper intensity threshold may be an intensity
value that an intensity value of a pixel is to fall below in order
to ensure that the intensity value of the pixel is not
oversaturated. For example, a 16 bit image with an intensity
threshold percentage 1040 of 99%, the upper intensity threshold may
be 65,435 which is a value that is 100 units lower than the
saturation value of 65,535 for a 16 bit image. Any pixel included
in the intensity threshold percentage 1040 that falls below the
upper intensity threshold of 65,435 may be considered as a pixel
that is not oversaturated. The user may adjust the upper intensity
threshold, which may also be preset based upon a particular
application and/or the characteristics of the system responsible
for imaging the specimen (e.g., the specific properties of the
excitation source(s) or the detector).
[0076] Each of the pixels that have intensity levels that are above
the lower intensity threshold and below the upper intensity
threshold may then be included in the linear relationship
determination discussed below in detail in step 820. In an example
embodiment, step 820 may be performed by processor 102 of computer
system 100 as shown in FIG. 13.
[0077] In step 830, whether a linear relationship exists based on
the captured images is verified. As shown in FIG. 11, relationships
1110, 1120, and 1130 are plotted for each of the images 910(a-n).
For example, relationship 1110 represents the relationship between
the exposure time of 10 ms and the percentage of pixels of 40,000
that satisfy the lower intensity threshold and the upper intensity
threshold for image 910. Relationship 1120 represents the
relationship between the exposure time of 20 ms and the percentage
of pixels of 45,000 that satisfy the lower intensity threshold and
the upper intensity threshold for image 910b. Relationship 1130
represents the relationship between the exposure time of 40 ms and
the percentage of pixels 50,000 that satisfy the lower intensity
threshold and the upper intensity threshold of image 910n. As noted
above, the intensity threshold percentage applied to each of the
images 910(a-n) is constant and the different exposure times
increase from a shortest exposure time duration in 10 ms to a
longest exposure time duration in 40 ms.
[0078] In order to determine whether a linear relationship exists
between each of the relationships 1110, 1120, and 1130, a
theoretical linear relationship 1150 between relationship 1110 and
1130 may first be determined. The theoretical linear relationship
1150 is the linear relationship that would exist between the image
910a with the exposure time with a first duration, as represented
by relationship 1110 in this example, and the image 910n with the
exposure time with a second duration, as represented by
relationship 1130 in this example. The exposure time with a first
duration is shorter than the exposure time with a second
duration.
[0079] However, as noted above, in order to determine the optimal
exposure time to obtain the percentage of pixels that satisfy the
threshold to generate an image enables the user to adequately
analyze the specimen, any amount of images with different exposure
times as well as a single image with a single exposure time may be
incorporated. However, for ease of discussion, the following
example discusses incorporating three images with three different
exposure times.
[0080] After the theoretical linear relationship 1150 is determined
between the relationship 1110 with the first exposure time duration
and the relationship 1130 with the second exposure time duration,
the relationship 1120 with the exposure time duration that is
between the exposure time durations of relationship 1110 and
relationship 1130 may be compared to the theoretical linear
relationship 1150. The distance 1140 of the relationship 1120 from
the theoretical linear relationship 1150 may be compared to a
threshold distance to determine whether a linear relationship
exists between relationships 1110, 1120, and 1130.
[0081] The threshold distance is a distance from the theoretical
linear relationship 1150 that when relationship 1120 is within the
threshold distance, a linear relationship exists between
relationships 1110, 1120, and 1130 such than an adequate optimal
exposure time may be determined from the linear relationship to
obtain the appropriate percentage of pixels that satisfy the
threshold to generate an image that enables the user to adequately
analyze the specimen. For example, image 910b with an exposure time
duration of 20 ms as represented by relationship 1120 is between
the image 910a with the first exposure time duration of 10 ms as
represented by relationship 1110 and the image 910n with the second
exposure time duration of 40 ms as represented by relationship
1130. The theoretical linear relationship 1150 between relationship
1110 and 1130 is then determined.
[0082] The distance 1140 of relationship 1120 is then compared to
the threshold distance to determine whether the distance 1140 is
within the threshold distance. An adequate linear relationship
between the relationships 1110, 1120, and 1130 to determine an
adequate optimal exposure time to obtain the appropriate percentage
of pixels that satisfy the pixel intensity saturation level
threshold to generate an image that enables the user to adequately
analyze the specimen when the distance 1140 of relationship 1120 is
within the threshold distance. Thus, the optimal exposure time may
then be calculated when the distance 1140 of the relationship 1120
is within the threshold distance based on the adequate linear
relationship between the relationships 1110, 1120, and 1130.
[0083] However, an inadequate linear relationship between the
relationships 1110, 1120, and 1130 exists when the distance 1140 of
relationship 1120 is beyond the threshold distance. The distance
1140 of relationship 1120 being beyond the threshold distance is
indicative that a weak linear relationship exists between the
relationships 1110, 1120, and 1130. Attempting to determine the
optimal exposure time based on a weak linear relationship may
actually result in an exposure time that is less than optimal which
would result in capturing a lesser percentage of pixels that
satisfy the threshold in the image and thus resulting in a lesser
quality image that hinders the user's ability to adequately analyze
the specimen.
[0084] Rather than moving forward with determining an exposure time
that is likely less than optimal when the distance 1140 of
relationship 1120 is beyond the threshold distance, an additional
image may be captured with an exposure time that has an exposure
time duration that exceeds the second exposure time duration of
relationship 1130. An adjusted theoretical linear relationship may
then be determined based on image 910a represented by relationship
1110, image 910b represented by relationship 1120, and image 910n
represented by relationship 1130 as well as the additional image
with an exposure time duration that exceeds the longest exposure
time duration of relationship 1130.
[0085] In this example, an additional image is captured with an
exposure time duration that exceeds the first exposure time
duration of 40 ms of relationship 1130. The distance 1140 for
relationship 1120 may then be recalculated and compared to the
threshold distance relative to the adjusted theoretical linear
relationship as opposed to the initial theoretical linear
relationship to determine whether an adequate linear relationship
exists between relationships 1110, 1120, 1130, and the additional
image. If so, the optimal exposure time may then be calculated
based on the additional image. If not, yet another image may be
captured with another exposure time that exceeds each of the
previous exposure time durations to determine whether the distance
1140 for relationship 1120 is within the threshold distance. This
is then repeated until the distance for relationship 1120 is within
the threshold distance. In an embodiment, the first exposure time
duration of relationship 1110 may be a shortest exposure time
duration for the relationships 1110, 1120, and 1130 and the second
exposure time duration of relationship 1130 may be a longest
exposure time duration for the relationships 1110, 1120, and 1130.
In an example embodiment, step 830 may be performed by processor
102 of computer system 100 as shown in FIG. 13.
[0086] In step 840, an optimal exposure time is determined based on
one or more of the captured images in the linear relationship. As
shown in FIG. 12, after the linear relationship between
relationships 1110, 1120, and 1130 is determined to be sufficient,
the optimal exposure time is determined based on one or more images
in the linear relationship. In an embodiment, the optimal exposure
time may be determined from the images that have exposure times
that have the most separated exposure time durations. The linear
relationship 1250 may be most robustly defined between the images
that have exposure times with the most separated exposure time
durations. For example, image 910a with the exposure time having
the shortest exposure time duration in 10 ms as represented by
relationship 1110 and image 910n with the exposure time having the
longest exposure time duration in 40 ms as represented by
relationship 1130 may most robustly define the linear relationship
1250 between images 910(a-n).
[0087] The optimal exposure time may then be determined based on
relationship 1110 and relationship 1130 based on the linear
relationship 1250 robustly defined between relationship 1110 and
relationship 1130. The linear relationship 1250 robustly defined
between relationship 1110 and relationship 1130 may then be
projected such that the optimal exposure time to obtain an
appropriate percentage of pixels that satisfy the pixel intensity
saturation level threshold to generate an image for the user to
adequately analyze the specimen may be determined.
[0088] For example, the linear relationship 1250 robustly defined
between relationship 1110 and relationship 1130 is projected to
determine relationship 1240. Relationship 1240 represents an
optimal exposure time of 80 ms such that a projected image that has
the optimal exposure time of 80 ms obtains 65,535 pixels that
satisfy the threshold value. Such a projected image with such a
significant percentage of pixels that satisfy the threshold value
may enable the user to adequately analyze the specimen.
[0089] The images that have exposure times with the most separated
exposure time durations may then be incorporated to the generation
of the projected image at the optimal exposure time. As noted
above, the linear relationship 1250 may be most robustly defined
between image 910a with an exposure time with the shortest exposure
time duration of 10 ms and image 910n with an exposure time with
the longest exposure time duration of 30 ms. Image 910a and image
910n may then be incorporated to generate the projected image with
the optimal exposure time of 80 ms that obtains 65,535 pixels that
satisfy the threshold value as represented by relationship 1240.
Such a projected image with such a significant percentage of pixels
that satisfy the threshold value may enable the user to adequately
analyze the specimen.
[0090] In an embodiment, the projected image may be generated from
a single image included in the images 910(a-n). In generating the
projected image from the single image, such as image 910n as
represented by relationship 1130, an optimal exposure time may be
determined simply by projecting image 910n based on the linear
relationship between the images 910(a-n). For example, image 910n
with an exposure time of 40 ms and 50,000 pixels that satisfy the
threshold value may be projected based on the linear relationship
between the images 910(a-n) to generate relationship 1240 with the
optimal exposure time of 80 ms and 65,535 pixels that satisfy the
threshold value. In an example embodiment, step 840 may be
performed by processor 102 of computer system 100 as shown in FIG.
13.
[0091] FIG. 13 shows an exemplary computer or computing system 100
that can be used to implement the method and system. The computer
system 100 can be a laptop, desktop, server, handheld device (e.g.,
personal digital assistant (PDA), smartphone, tablet), programmable
consumer electronics or programmable industrial electronics.
[0092] As illustrated the computer system 100 includes a processor
102 that can be any various available microprocessor(s). For
example, the processor can be implemented as dual microprocessors,
multi-core and other multiprocessor architectures. The computer
system 100 includes memory 104 that can include volatile memory,
nonvolatile memory or both. Nonvolatile memory can include read
only memory (ROM) for storage of basic routines for transfer of
information, such as during computer boot or start-up. Volatile
memory can include random access memory (RAM). The computer system
100 can include storage media 106 including, but not limited to,
magnetic or optical disk drives, flash memory, and memory
sticks.
[0093] The computer system 100 can incorporate one or more
interfaces, including ports 108 (e.g., serial, parallel, PCMCIA,
USB, FireWire) or interface cards 110 (e.g., sound, video, network,
etc.) or the like. In embodiments, an interface supports wired or
wireless communications. Input is received from any number of input
devices 112 (e.g., keyboard, mouse, joystick, microphone,
trackball, stylus, touch screen, scanner, camera, satellite dish,
another computer system and the like). The computer system 100 can
output data through an output device 114, such as a display (e.g.
CRT, LCD, plasma), speakers, printer, another computer or any other
suitable output device.
[0094] The description above references flowchart illustrations of
methods, apparatus (systems) and computer program products. It will
be understood that each block of the flowchart illustrations, and
combinations of blocks in the flowchart illustrations, and/or part
thereof, can be implemented by computer program instructions. These
computer program instructions may be loaded onto a computer or
other programmable data processing apparatus or otherwise encoded
into a logic device to produce a machine, such that the
instructions which execute on the computer or other programmable
data processing apparatus create means for implementing the
functions specified in the flowchart block or blocks. These
computer program instructions may also be stored in a computer
readable memory that can direct a computer or other programmable
data processing apparatus to function in a particular manner, such
that the instructions stored in the computer readable memory
produce an article of manufacture including instruction means that
implement the function specified in the flowchart block or blocks.
The computer program instruction may also be loaded onto a computer
or other programmable data processing apparatus to cause a series
of operational steps to be performed on the computer or other
programmable apparatus to produce a computer implemented process
such that the instructions which execute on the computer or other
programmable apparatus provide steps for implementing the functions
specified in the flowchart block or blocks.
[0095] Specific functional blocks, or parts or combinations
thereof, presented in relation to the disclosed methods and systems
are programmable as separate modules or functional blocks of code.
These modules are capable of being stored in a one or
multiple-computer storage media in a distributed manner. In one
embodiment, these modules are executed to perform the method and
system in whole or in part on a single computer. In one embodiment,
these modules are executed to perform the disclosed methods and
systems on multiple computers that cooperatively execute the
modules. In one embodiment, the programs are executed in a virtual
environment, where physical hardware operates an abstract layer
upon which the disclosed methods and systems are executed in whole
or in part across one or more physical hardware platforms.
[0096] In addition, it should be understood that the system and
method disclosed herein is not limited for use with imaging
substrates or objects after electrophoresis, and indeed is also not
limited to use in the life sciences field. Instead, the system and
method can be used in nearly any imaging system in which it is
desired to create a projected image and/or determine an optimal
exposure time.
[0097] While various aspects in accordance with the principles of
the invention have been illustrated by the description of various
embodiments, and while the embodiments have been described in
considerable detail, they are not intended to restrict or in any
way limit the scope of the invention to such detail. The various
features shown and described herein may be used alone or in any
combination. Additional advantages and modifications will readily
appear to those skilled in the art. The invention in its broader
aspects is therefore not limited to the specific details and
representative devices shown and described. Accordingly, departures
may be made from such details without departing from the scope of
the general inventive concept.
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