METHODS FOR SCALING IMAGES TO
DIFFERING EXPOSURE TIMES
BACKGROUND
[0001] The invention relates generally to methods for scaling an image, taken
at a given exposure time, to a selected or different exposure time.
[0002] In the field of digital imaging of biological specimens stained with
fluorescent markers, the need has arisen to compare images of the same field of view
taken with different exposure times. In one instance there is a need to remove the
autofluorescence from a specimen of a biological material. Specimens of biological
materials typically contain substances which fluoresce over frequency ranges that
overlap with those of commonly used fluorescent markers used to examine the tissue
specimens for certain biological features. For instance, it is fairly common to stain
specimens with antibodies to proteins of interest that are conjugated to well
established fluorescent dyes such as Cy3 and Cy5. For example, human breast cancer
tissue might be stained with antibodies to the p53 tumor suppression protein
conjugated to Cy3 .
[0003] One approach to removing autofluorescence from the microscopy
image of tissue stained with such a fluorescent marker is to take an image of the same
field of view before staining with the fluorescent marker and then on a pixel by pixel
basis remove the signal intensity observed in the unstained image from the stained
image. The desired result is a corrected image in which the fluorescent signal
recorded in the channel of the fluorescent marker is just due to the binding of the
fluorescent marker. However, for such a subtraction to result in an accurate image the
two images should have substantially the same exposure time.
[0004] There are instances in which it is not convenient or possible to take
both the autofluorescence image and the fluorescent marker stained image at the same
exposure time. In general each type of image has its own optimum exposure time
which gives the best balance between obtaining signal from the maximum number of
pixels with avoiding the signal from any pixel saturating its recording channel and
also the best balance between true signal and background signal. In some cases it may
not be possible or practical to use an exposure time with some fluorescent markers as
long as that desirable for measuring autofluorescence because at such long exposure
times an unacceptably large number of the pixels are saturated, meaning that they are
receiving so much signal that further signal is undetectable.
[0005] Therefore there is a need to estimate what the signals at the pixels of a
fluorescent image would have been if the exposure time had been different. There
have been attempts to do so by using the formula ¾ =Iti *(t2/ti) wherein I i is the
intensity for exposure time t i and ¾ is the estimated intensity for exposure time t2.
However, the results of such estimates have not been fully satisfactory. There have
been attempts to improve upon this estimate by subtracting the background for each
exposure time according to the formula ¾ ={(Iti-background at ti) *(t2/ti)}-
(background at t¾ but the estimates still have not been as accurate as desired.
BRIEF DESCRIPTION
[0006] The methods and systems of the invention provide an accurate estimate
of the intensity of the signal from a pixel of a fluorescent image of a microscope
specimen at a given exposure time beginning with the actual signal intensity at a
different exposure time using the dark pixel intensity of the digital camera used to
take the actual image.
[0007] An example of the method, for scaling an image taken an optimal
exposure time to a selected exposure time, generally comprises: determining a dark
pixel intensity of an imaging device; acquiring a first image at an optimal exposure
time; and adjusting a pixel intensity of one or more pixels in the first image, based at
least in part on the dark pixel intensity, for a second exposure time that is different
from the optimal exposure time. Determining the dark pixel intensity may comprise,
setting an exposure time to zero; acquiring an intensity image; and calculating a mean
intensity for the entire intensity image. The optimal exposure time is typically based
at least in part on one or more settings for the imaging device.
[0008] In one example, the first image is of a biological material, wherein the
biological material may be stained or otherwise comprise a first biomarker. The
biomarker may comprise a fluorescent biomarker, whereby the first image of the
biological material will exhibits a signal in a fluorescent channel corresponding to the
biomarker. The biological material may be stained with a second biomarker, whereby
the method may further comprise taking a second image at the same or a different
optimal exposure time. The method may further comprise, adjusting a pixel intensity
of one or more pixels in the second image, based at least in part on the dark pixel
intensity, for a second exposure time that is different from the optimal exposure time.
The first and second image may be registered to form a composite image. The images
may be registered, for example, by identifying one or more morphological features
and aligning or co-registering the images using the identified morphological features.
The images may also be registered using other methods such as, but not limited to,
aligning the pixels of the images.
[0009] The method may further comprise acquiring an autofluorescence image
of the biological material, and, in some examples, subtracting the autofluorescence
image of the biological material from the first image. The autofluorescence image
may also be registered with the first and/or second image. The methods are not
limited to acquiring a first and second image. Any number of images may be taken of
the material and scaled and/or registered as needed or desired for a given use. Any
two or more of the images may be registered to form a composite image.
[0010] The methods may also comprise the steps for staining or otherwise
applying one or more biomarkers to the biological material, wherein the first image is
acquired after the biomarker is applied to the biological material. The biomarkers
may be adapted for one or more channels. The biomarker may be applied to the
material simultaneously or serially. Images may be taken using filters that correspond
to the biomarker channels.
DRAWINGS
[001 1] These and other features, aspects, and advantages of the present
invention will become better understood when the following detailed description is
read with reference to the accompanying drawings in which like characters represent
like parts throughout the drawings, wherein:
[0012] FIG. 1 is a graph of an example of a correlation between mean pixel
intensity of dark frame and exposure time associated with a digital imaging device;
[0013] FIG. 2A is an image having an exposure time of 200 msec;
[0014] FIG. 2B is the image of FIG. 2A having an exposure time of 500 msec;
[0015] FIG. 2C is a scatterplot of the pixel intensities of FIGs. 2A and 2B; and
[0016] FIG. 3 is a graph of an example of the effect of exposure time on pixel
intensity at a given region of interest.
DETAILED DESCRIPTION
[0017] One or more of the examples of the methods and systems of the
invention provide an accurate estimate of the intensity of the signal from a pixel of a
fluorescent image of a microscope specimen at a given exposure time beginning with
the actual signal intensity at a different exposure time using the dark pixel intensity of
the digital camera used to take the actual image.
[0018] Autofluorescence images and stained images are often captured at
different exposure times, but image subtraction requires that two images were
obtained using the same exposure time. Pixel intensity is generally linear to exposure
time as long as images are acquired at the linear range of a camera, but there typically
exists an intercept, which is set by the camera. For purposes of this description, this
intercept is referred to as dark pixel intensity. The dark pixel intensity is the pixel
intensity under conditions of no light and zero exposure time. Dark pixel intensity is
determined by a camera's readout noise (electron/pixel), gains, and DC offset. By
knowing, or otherwise determining, the dark pixel intensity of an image acquisition
set up, an image acquired at one exposure time may be linearly scaled to an image at a
different exposure time. Dark pixel intensity may be measured, for example, by
obtaining an image at no light and minimum exposure time, or by indirectly
calculating the intensity by capturing images at a series of exposure times,
[0019] Exposure time is a parameter during fluorescence microscope image
acquisition and generally ranges from several milliseconds to a couple of seconds
although it is not necessarily limited to this range. The correct exposure time is
important to maintain a linear representation of fluorescent signals. However, there
are other parameters during acquisition that also impact digital images including, but
not limited to, analog gain, digital gain, offset and binning.
[0020] An example of the method, for scaling an image taken an optimal
exposure time to a selected exposure time, generally comprises: determining a dark
pixel intensity of an imaging device; acquiring a first image at an optimal exposure
time; and adjusting a pixel intensity of one or more pixels in the first image, based at
least in part on the dark pixel intensity, for a second exposure time that is different
from the optimal exposure time. Determining the dark pixel intensity may comprise,
setting an exposure time to zero; acquiring an intensity image; and calculating a mean
intensity for the entire intensity image. The optimal exposure time is typically based
at least in part on one or more settings for the imaging device. In instances, for
example, when a given imaging device cannot be set at zero exposure time, the
exposure time may extrapolated to zero or may be set at the lowest possible setting
allowable for the given imaging device.
[0021] When the mean intensity is calculated, a single value of dark pixel
intensity may be applied one dimensionally. It may be extended to a two dimensional
matrix, for example, when the image is acquired at zero exposure time. The images
may also be averaged. For example, the compensating filter may be either a scalar or
an array value.
[0022] In one example, the first image is of a biological material, wherein the
biological material may be stained or otherwise comprise a first biomarker. The
biomarker may comprise a fluorescent biomarker, whereby the first image of the
biological material will exhibits a signal in a fluorescent channel corresponding to the
biomarker. The biological material may be stained with a second biomarker, whereby
the method may further comprise taking a second image at the same or a different
optimal exposure time. The method may further comprise, adjusting a pixel intensity
of one or more pixels in the second image, based at least in part on the dark pixel
intensity, for a second exposure time that is different from the optimal exposure time.
The first and second image may be registered to form a composite image. A final
image may also be calculated from two or more images, e.g. one image may be
subtracted from another image. The images may be registered, for example, by
identifying one or more morphological features and aligning or co-registering the
images using the identified morphological features. The images may also be
registered using other methods such as, but not limited to, aligning the pixels of the
images.
[0023] The method may further comprise acquiring an autofluorescence image
of the biological material, and, in some examples, subtracting the autofluorescence
image of the biological material from the first image. The autofluorescence image
may also be registered with the first and/or second image. The methods are not
limited to acquiring a first and second image. Any number of images may be taken of
the material and scaled and/or registered as needed or desired for a given use. Any
two or more of the images may be registered to form a composite image.
[0024] The methods may also comprise the steps for staining or otherwise
applying one or more biomarkers to the biological material, wherein the first image is
acquired after the biomarker is applied to the biological material. The biomarkers
may be adapted for one or more channels. The biomarker may be applied to the
material simultaneously or serially. Images may be taken using filters that correspond
to the biomarker channels.
[0025] A microscope may be used in the methods and systems to collect
photons emitted by a fluorophore and relay them to an imaging device, such as a
charge-coupled device (CCD) camera or detector. The CCD array detector converts
photons to a photo current, which in turn is converted to a voltage. Each detector
voltage in the array is ultimately digitized (A/D convert, e.g., analog gain and digital
gain) into a pixel value representing photon intensity. Most scientific grade CCD
cameras have 12 bit outputs, with pixel values ranging from 0 to 4095. Quantum
efficiency (Qe) and noise both effect CCD detector sensitivity. Photons not captured
by a CCD are generally viewed as losses and result in reduced sensitivity. Noise is
often generalized into three components: 1) dark noise which is inherent to the
photodetector, refers to spurious signal (largely thermally generated) created in
absence of incident photons; 2) photon noise which refers to statistical fluctuations in
photocurrent resulting from random arrival of photons; and 3) readout noise (also
known as read noise) which refers to the combined noise generated by components
that convert the photocurrent to a digital word. Collectively these noise components
may be referred to as camera noise and represent error that may be introduced during
the process of quantifying the electronic signal on the CCD. The signal to noise ratio
(SNR) is determined by:
where: P is photon flux (signal) incident on the CCD (photon/pixel/second), B is
background photon flux incident on the CCD (photon/pixel/pixel), t is integration
time or exposure time (second), D is the dark current in electrons/pixel/second and
Nr is readout noise (root mean square electrons /pixel). The SNR is time/speed
dependent, longer integration time yields higher SNR. As such, there is a trade-off
between rate (frame/second) and detection limit. The SNR is proportional to the
square root of the integration time. For example, in the process of relating different
protein expression levels using fluorescence microscopy, it may be desirable to use a
different integration time for each protein. The effect of dark current, readout noise,
and exposure time on pixel intensity of a digital image (e.g. staining intensity of a
protein) affects the accuracy of quantification.
[0026] The output of the CCD camera is ultimately a digital word produced by
conversion of photoelectrons to a voltage, which is the amplified as needed and
processed by an analog to digital converter (ADC). A brief consideration of the noise
terms associated with the process will follow. A voltage signal is produced by the
individual photodetectors that comprise the CCD according to the following equation:
V
signal
= E q
where N is the number of photoelectrons generated, q is the charge of an electron,
G is the voltage gain of the amplifier stage (typically unity) and C is the capacitance
of the detector. The trans-impedance gain is expressed in /electron, and is
v C j
typically on the order of 0.1 to 10, depending on the size of the detector. The voltage
is then converted to a digital word by comparison with the ADC reference voltage.
[0027] In one or more examples of the methods, the estimate is obtained by
subtracting the dark pixel value from the signal intensity measured at a given
exposure time, multiplying this difference by the ratio of the exposure time for which
an estimate is desired by the exposure time for the actual image and subtracting the
dark pixel intensity from this result. One example of the methods comprises using the
algorithm
[0028] The dark pixel intensity may be determined in various ways such as
using a single measurement at zero exposure time and taking several measurements,
each at a different exposure time, and extrapolating to zero exposure time. Most
digital cameras adapted to be used with fluorescent microscopes allow an image to be
taken at zero exposure time. In one or more examples, this comprises taking an image
with the camera isolated from a source of illumination and depending on the set up
may also involve taking an image with no specimen. Software can be used to obtain
an average intensity value across all the pixels in a field of view. In one example, this
value can simply be used as the dark pixel intensity while in another example the
average value for each exposure time may be used to extrapolate back to zero
exposure time. In doing such an extrapolation, the average intensity may be assumed
to be a linear function of exposure time.
[0029] The dark pixel intensity may be the same across the common
measurement channels. Its value is therefore generally independent of the
measurement channel used to determine its value.
[0030] One or more examples of the methods of the invention may also
comprise using the dark pixel intensity to improve the accuracy of autofluorescence
corrections. Such corrections may comprise correcting a first image with
measurements made on a second image. This second image may be taken at a
different exposure time than the first image thus one of the two images may be
adjusted to what it would have been had it been taken at the exposure time of the
other image.
[0031] One or more examples of the methods comprise, determining the dark
pixel intensity of a given camera setup to allow projections of the effect of exposure
time on the intensity at a given pixel in taking images in fluorescent microscopy. An
initial determination is made of the dark pixel intensity of a digital camera apparatus
adapted to take fluorescent images through a microscope. The dark pixel intensity
may be at least partly dependent on a given apparatus set up and is the same for all
fluorescent channels for that set up. Such values may then be used in projecting or
estimating the intensity at a given pixel at a given exposure time given a measured
value of the intensity at that pixel for a different exposure time. This projection may
be used to remove the effects of autofluorescence from an image of a biological
sample. The dark pixel intensity of a given camera set up may be determined, for
example, from a measurement at zero exposure time or an extrapolation to zero
exposure time of measurements made at several exposure times. In either example,
suitable software may be used to sum the intensity of the signals from all the pixels
that the digital camera is reading and produce an average value.
[0032] In the first example, a reading is taken from the camera in the absence
of any illumination. This may be accomplished by taking a reading from the camera
output with the shutter that regulates the input of light to the camera shut. The dark
pixel reading is dependent upon a given exposure time, for example, the time interval
over which pixel signals are read from the camera. Readings may be taken at a
number of exposure times. For example, multiple readings may be taken between 1
and 500 milliseconds and apply a linear regression equation to project the value at
zero exposure time. The reading may also be taken over a very short time interval
such as, for example, one millisecond.
[0033] In the latter example, the dark pixel intensity may be determined from
a series of images of the same field of view taken in a given fluorescent channel at
various exposure times. A pixel signal intensity average may be generated for each
exposure time, for example, by summing the signals from all of the pixels being read
by the camera and divide this value by the number of pixels involved to yield an
average value. A linear regression analysis may then be applied to determine a
relationship between exposure time and average pixel signal intensity. Such a
relationship may be used to project the signal intensity at zero exposure time that is
the dark pixel intensity. The relationship between exposure time and the average
pixel signal intensity is substantially linear over the range of exposure times in which
pixels have not become saturated with incident light, whereby their signal output is
no longer proportional to the incident light to which they are exposed.
[0034] The improved projection of pixel signal intensity at a particular
exposure time may be used to adjust or correct a given image for autofluorescence
effects. In determining and removing the effects of autofluorescence from a given
fluorescent image, an exposure time may be used which is not optimum or even
practical for an image displaying the features of interest. For instance, when
measuring the autofluorescence of a sample material, the exposure time for a given
fluorescent channel, to obtain a desirable signal to noise ratio, may need to be longer
than typically optimal for an image of the specimen after it has been stained with a
fluorescent marker active in that channel. If the specimen takes up a significant
amount of the stain because it contains an abundance of the feature to which the stain
is directed, an optimal exposure time for a stained image may be considerably shorter
than the optimal time exposure time for determining autofluorescence. It also may
well be that at the longer exposure times, the pixels of the sample image would
become so saturated that they would not able to provide any more signal if the amount
of incident light they are exposed to increases further beyond the saturation level.
Example
[0035] For the camera, a Hamamatsu ORCA-ER-12AG deep cooled digital
camera (model: C4742-80-12AG, Hamamatsu City, Japan) was used. Its dark current
was 0.03 electrons/pixel/s. readout noise is 6 RMS electrons /pixel. The imager was a
Zl upright microscope (Carl Zeiss Microimaging Inc.Thornwood, NJ) with 20 x Plan
Apochromat objective (NA=0.8). Grayscale images were acquired using a DAPI filter
set (Excitation @ 365/40 nm and Emission @ 445/50 nm), Cy3 filter cube (Excitation
@ 550/25 nm and Emission @ 605/70 nm), and Cy5 filter cube (Excitation @640/30
nm and Emission @ 690/50 nm). Analog gain, digital gain, analog offset, and digital
offset were all set at zero.
[0036] To prepare the sample, paraffin-embedded LNCaP cells IHC control
SignalSlide™ (#8101) were obtained from Cell Signaling Technology (Beverly, MA).
A breast cancer slide was obtained from Thermo Fish Scientific (Fremont, CA). A
normal skin slide was obtained from Biochain (Hayward, CA). After standard
dewaxing and antigen retrieval treatments, LNCaP cells were incubated with rabbit
monoclonal glycogen synthase kinase-3 (GSK-3) antibody ( 1:50, Cat# CS9315, Cell
Signaling Technology) at 4°C overnight. Secondary Donkey anti-rabbit antibody,
conjugated with Cy3 (Jackson ImmunoRe search, West Grove, PA), was incubated
(1:250) for 1 hour at room temperature. The breast cancer slide and skin tissue slide
were stained with ALCAM antibody (1:40, Product ID: NCL-CD1 16, Leica
microsystem, Bannockburn, IL). Donkey anti-mouse antibody,conjugated with Cy3
and Cy5 secondary antibodies, were used for breast cancer slide and skin tissue slide,
respectively. All slides were finally counterstained with DAPI and mounted with
VectaShield (HI 000, Vector Laboratory, Burlingame, CA).
[0037] To determine the dark pixel intensity, the reflector remained closed to
avoid light entering the camera. Triple images (no sample) were acquired using
different exposure times (1, 50, 100, 250, 500 msec). Images were saved as ZVI
format to retain acquisition setting and raw TIFF intensity values. Each image was
subsequently read by ImageJ BioFormat Importer and mean intensity of each image
was measured using ImageJ (National Institutes of Health, version 1.42d).
[0038] To correlate pixel intensities of the two images obtained at different
exposure time at each pixel, the Signal Slide was used to capture two images at 200
msec and 500 msec. The images were exported to TIFF format and subsequently read
by MATLAB image processing toolbox (MathWork, Natick, MA). The pixel
intensities were stored in two matrices with a dimension of 1344 x 1024. To compare
pixel intensities at a corresponding position, the 2D matrix was "reshaped" to a ID
matrix (13756256 x 1). A MATLAB statistics toolbox was used to perform linear
regression between two ID matrices.
[0039] To investigate the relationship between pixel intensity and exposure
time at region of interest (ROI) level, the normal skin slide and breast cancer slides
stained with ALCAM, a membrane protein, were used. These slides were imaged
using no light, and with light and exposure times of 0, 1, 2, 4, 8, 16, 32, 64, 96, 128,
160, 200, 250, 300, 350, 400, 450, 500, 550, 600 msec. Two images were obtained
for each exposure time. A region of background (no tissue) was manually selected
using drawing function of ImageJ . DAPI background and Cy3 background were
quantified using ROI manager function and selecting only background region. DAPI
channel image exposed at 16 msec (optimal exposure time) was thresholded to
identified DAPI positive area and was used to define nuclear mask. This mask was
applied to all images and DAPI signal was quantified only within the masked regions.
Cy3 signal and Cy5 signal were quantified on the membrane region using a similar
approach.
[0040] To calibrate fluorescence intensity, standard fluorescence beads
(F36909, Invitrogen, Carlsbad, CA) were used as the fluorescence intensity standard.
The intensities of the beads are listed as 0.00667, 0.03, 0.1, 0.33, and 1. The beads
were first imaged at a constant exposure time of 75 msec. Then different exposure
times were used for these beads. Specifically, 300 and 150 msec were used for beads
of intensity 0.00667, 150 and 75 msec for beads of 0.03 and 0.1. 75 and 30 msec for
beads of 0.33, and 30 msec for beads of 1.
[0041] Table 1 shows the changes in mean pixel intensity in response to
exposure time without light emission to the camera. There is a slight increase in mean
intensity with exposure time. The average is about 200, which expends 5% of the
dynamic range of a 12-bit camera. To separate the dark noise contribution
(proportional to exposure time) from the readout noise contribution (constant), a
linear regression was obtained.
Table 1- Mean and Standard Deviation of Dark Image Intensity
Pixellntensity =0.03 14 • + 199.21
[0042] Since noise exhibits a Poisson distribution in a counting system, where
the standard deviation is square root of the mean, the observed signal on a CCD
camera is given by
Signal = P +B) -Q +D -t +N Eq. 4
[0043] Under the condition of no light, DarkSignal is given by
DarkSignal =D -t +N;
[0044] DarkSignal is given in the unit of electron/pixel. This signal will
undergo A/D convert and finally digitize to Pixellntensity,
Pixellntensity =G D -t +N )+offset Eq. 6
where is the combined gain (digital gain and analog gain), and offset is combined
offset.
[0045] Comparing Eq. 3 and Eq. 6, the slope is 0.0314 ( G-D) . Since D is 0.03
electron/pixel/sec (from manufacturer data sheet), suggesting G is very close to 1.
Both analog and digital gains were set at 0 . The calculated constant/intercept
{G -N +offset) can be lumped into one constant, dark pixel intensity. It comprises the
combination of and offset . Dark pixel intensity is independent of dark current or
exposure time, and it is a constant as long as camera setting is fixed except for
exposure time.
[0046] It is clear from Eq. 3 that the majority of Pixellntensity is from and
offset (199.21). Dark current has only a negligible effect with a slope of 0.03 14.
[0047] FIG. 1 shows an example, using a Hamamatsu camera, of the
correlation between dark pixel intensity and noise RMS.
[0048] To determine the effect of exposure time on the pixel intensity at the
individual pixel level, Signallntensity, in this example, follows an equation similar to
Signallntensity =G[(P +B) Q +D t +NRJ+offset Eq. 7
[0049] Rearrange Eq. 7 to obtain
Signallntensity =G[(P +B) Q +D ] t +DarkPixellntensity Eq. 8
where DarkPixelMensity =G N +offset, and it is the intercept (199.21) in FIG. 1. For the
end user of fluorescence microscope, dark pixel intensity can be approximately
determined by acquiring an image under no light condition and exposed at the zero or
shortest exposure time allowed by the camera and then measuring the average
intensity of the image. To adjust image pixel intensity to different exposure time, the
following simplified equation may be used:
Intensity(t ) - Darkpixellntensity Intensity(t ) - Darkpixellntensity
[0050] This equation can be used at single pixel level, ROI level, and whole
image level. FIG. 2 shows pixel intensities at corresponding positions from two
images exposed at 200 msec (A) and 500 msec (B). The regression analysis suggests
the linear relationship:
= 2 ra i - 301 q 10
[0051] The error variance from the regression line is 172 and it is uniform
across the dynamic range of pixel intensity. The slope, 2.5, was the ratio of exposure
time (2.5 in this case). R2 = 0.976. The intercept of this regression (301) seems to be
different from the intercept of 199. However, a simple transformation of Eq. 10
reveals dark pixel intensity exactly same as what we obtained before.
- 200.9 = 2.5 . (/ s - 200.9) Eq. 11
[0052] Importantly the regression intercept 200.9 is very close to the dark
pixel intensity of 199. 12, as determined under the condition of no light.
[0053] One of the common tasks in quantitative fluorescence microscopy is to
measure the staining intensity at different ROIs such as nuclear or cytoplasmic
regions. Using ImageJ the whole images were segmented into the background (no
tissue/cells), nuclear area and membrane area, and plotted the respective signals
against exposure time (FIG. 3). There is a clear linear relationship between exposure
time and intensity at different ROIs and using different channels (DAPI, Cy3, and
Cy5). Intercepts/dark pixel intensity for all these lines varies between 199.9 to 202.6,
virtually same as 199 as we measured before. At the bottom of the figure, mean pixel
intensity for dark image at 0 exposure time were also shown. The slope for each line
is [(P +B) Q +D], representing normalized signal intensity. At background regions,
the slope becomes G[(B) Q +D\ .
[0054] Staining intensity is GPQj. It should be calculated by the difference
between signal and background (no dark pixel intensity). Since both signal intensity
and background intensity contain the same dark pixel intensity, they are canceled out.
Normalized staining intensity can be simply defined as
, Signallntensity - Backgroundlntensity
Normalizeabtaininglntensity = - - Eq. 12
[0055] To validate Eq. 12, different exposure times were used to capture
images of standard fluorescence intensity beads. The intensities of these beads vary
almost 150 fold, as a result no single exposure time can capture all these beads at their
optimal condition.
[0056] In one or more of the examples of the methods, dark pixel intensity is a
camera offset and an additive to the signal intensity in an image. It may be
determined by one or more of the readout noise, analog gain, digital gain, analog
offset, digital offset, and other settings. From the perspective of end users, dark pixel
intensity is constant as long as camera acquisition setting is fixed except for exposure
time. Dark pixel intensity may be determined by acquiring an image under the
conditions of no light and zero (minimum) exposure time followed by calculating
mean intensity of the image. As a non-limiting example, a dark pixel intensity of 200
may contribute at least 10% for a normal fluorescence image with a mean intensity of
2000. By predetermining the dark pixel intensity of a set up, an image acquired at one
exposure time may be scaled to another, for example, using Eq. 9 .
[0057] Dark pixel intensity and background intensity (or lowest pixel
intensity) are distinguishable. For example, background intensity may be determined
by G[ B) -Q + . As a result, background intensity is typically
greater than dark pixel intensity and increases with exposure time. When the staining
of biomarker is weak and a longer exposure time is required, the background intensity
may be, for example, as high as 1000-3000. In contrast, dark pixel intensity is a
constant (e.g. 200) as long as the camera acquisition settings are fixed except for the
exposure time.
[0058] Staining intensity of a biomarker is the difference between signal
intensity and background intensity (within an image). Subtraction of the background
intensity removes the contribution from scattered light from glass slide, dark current,
and dark pixel intensity. However, it may still be contaminated with
autofluorescence. Eq. 12 may be used to compare the staining intensity of a given
biomarker.
[0059] Temperature may also affect the dark pixel intensity. With increased
temperature, an electron is much easier to move, therefore, both dark current and
readout noise are function of temperature. In the Example, the temperature of the
camera was regulated at -30 °C as long as the environment temperature is in the range
of - 5 to 40 °C. This cooling temperature is generally associated with digital cameras.
Low temperatures will contribute to low dark current as well as readout noise.
[0060] It may be beneficial to set the offset to dark pixel intensity. Therefore,
this value will be subtracted from the final pixel intensities and pixel intensity will be
proportional to exposure time. One advantage of using an offset is to increase the
dynamic range of pixel intensity. The offset though should be the same as dark pixel
intensity to achieve the most benefit.
[0061] While only certain features of the invention have been illustrated and
described herein, many modifications and changes will occur to those skilled in the
art. It is, therefore, to be understood that the appended claims are intended to cover
all such modifications and changes as fall within the true spirit of the invention.
CLAIMS:
1. A method for scaling an image taken an optimal exposure time to a
selected exposure time, comprising,
determining a dark pixel intensity of an imaging device;
acquiring a first image at an optimal exposure time; and
adjusting a pixel intensity of one or more pixels in the first image, based at
least in part on the dark pixel intensity, for a second exposure time that is different
from the optimal exposure time.
2 . The method of claim 1, wherein determining the dark pixel intensity
comprises,
setting an exposure time to zero;
acquiring an intensity image; and
calculating a mean intensity for the entire intensity image.
3 . The method of claim 1, wherein the optimal exposure time is based at
least in part on one or more settings for the imaging device.
4 . The method of claim 1, wherein the first image comprises a
fluorescence microscopy image of a biological material.
5 . The method of claim 4, wherein the biological material comprises a
first biomarker.
6 . The method of claim 5, wherein the biomarker is a fluorescent
biomarker.
7 . The method of claim 4, wherein the first image of the biological
material exhibits a signal in a fluorescent channel.
8 . The method of claim 5, wherein the biological material comprises a
second biomarker, further comprising taking a second image at the same or a different
optimal exposure time.
9 . The method of claim 8, further comprising, adjusting a pixel intensity
of one or more pixels in the second image, based at least in part on the dark pixel
intensity, for a second exposure time that is different from the optimal exposure time.
10. The method of claim 9, further comprising registering the first and
second image to form a composite image.
11. The method of claim 4, further comprising acquiring an
autofluorescence image of the biological material.
12. The method of claim 11, further comprising subtracting the
autofluorescence image of the biological material from the first image.
13. The method of claim 12, further comprising applying a biomarker to
the biological material, wherein the first image is acquired after the biomarker is
applied to the biological material.
14. The method of claim 13, further comprising registering the
autofluorescence image and the first image.
15. The method of claim 12, further comprising applying another
biomarker to the biological material, and acquiring a second image of the biological
material.
16. The method of claim 15, further comprising registering the first image
and the second image.
17. The method of claim 15, further comprising applying one or more
additional biomarkers, each corresponding to a channel, to the biological material, and
acquiring one or more additional images for each of the biomarker channels.
18. The method of claim 17, further comprising registering the first,
second and additional images to form a composite image of the channels.
19. The method of claim 1, wherein adjusting the pixel intensity at least in
part uses the following equation:
wherein t i is the optimal exposure time, t2 is the second exposure time, Iti is a
signal intensity of a given pixel for exposure time ti, It2 is an estimated signal
intensity for exposure time t2 and Darkpixellntensity is the dark pixel intensity for the
imaging device.