standardization of line-scan nir imaging systems

JOURNAL OF CHEMOMETRICSJ. Chemometrics 2007; 21: 88–95Published online 2 August 2007 in Wiley InterScience
(www.interscience.wiley.com) DOI: 10.1002/cem.1038
Standardization of line-scan NIR imaging systems

Zheng Liu1y, Honglu Yu2 and John F. MacGregor1*1Department of Chemical Engineering, McMaster University, Hamilton, Ontario, Canada L8S 4L72ProSensus Inc., 175 Longwood Road South, Hamilton, Ontario, Canada L8P 0A1

Received 15 September 2006; Revised 6 February 2007; Accepted 13 March 2007

*CorrespoEngineeriL8S 4L7.E-mail: my PresentHamilton

A simple and easy to use method is proposed for standardizing NIR imaging systems for differences

among detectors in the charge-coupled device (CCD) array and illumination unevenness. The

standardization equations are then used to pre-treat NIR image data to reduce the systematic errors

introduced by a line-scan NIR imaging system. The method requires only easily available homo-

geneous standards with relatively uniform spectral response. The effectiveness of the standardiz-

ation in reducing the pixel-to-pixel biases and other systematic effects is illustrated with examples,

and the improved sensitivity in results obtained from a multivariate image analysis (MIA) based on

multi-way principal component analysis (MPCA) is demonstrated. Copyright # 2007 John Wiley &

Sons, Ltd.

KEYWORDS: line-scan NIR imaging; hyperspectral images; instrument standardization

1. INTRODUCTION

NIR chemical imaging technology greatly extends the

capability of the conventional probe-based NIR spectroscopy

by adding a completely new dimension, the spatial

dimension. It enables one to obtain not only spectral but

also spatial information characterizing samples with unpre-

cedented ease, speed, and spatial and spectral resolution.

Through the fusion of multivariate calibration methods and

multivariate (or hyperspectral) image analysis, it answers the

questions about the sample such as what chemical species are

in the sample, how much of each is present, and most

importantly, where they are located. Its demonstrated utility

in solving real-world problems has encouraged its rapid

acceptance and been increasingly used in practice, for

example, in the pharmaceutical industry [1–2].

Two types of NIR imaging spectrometers are generally

used in practice according to the application situation. For a

moving sample (e.g., the sample on a moving web or a

conveyor belt), the line-scan NIR imaging spectrometer is

usually used. It builds an image cube (y� x� l dimensions, y

and x are the spatial dimensions and l is the spectral

dimension) by continuously capturing multiple lines along

the y dimension with a spatial–spectral (x� l) intensity

image for each scanned line. For a stationary sample (e.g., a

sample under a microscope), the filter-based imaging

ndence to: J. F. MacGregor, Department of Chemicalng, McMaster University, Hamilton, Ontario, Canada

[email protected]: ProSensus Inc., 175 Longwood Road South,, Ontario, Canada L8P 0A1

spectrometer is employed. It builds the image cubes by

joining the spatial–spatial (x� y) intensity images taken at

different wavelength bands (l). The wavelength bands are

selected by tuning the filter to permit the light from only a

certain wavelength band to enter the camera lens. The

charge-coupled device (CCD) area array, which is the

detector of each spatial–spectral (x� l) intensity image

recorded by the line-scan imaging spectrometer or each

spatial–spatial (x� y) intensity image recorded by the filter

based imaging spectrometer, can be considered as many

thousands of individual infrared sensors. The full spectrum

of a pixel in the image taken by the line-scan imaging

spectrometer is captured by a column of sensors on the CCD

array. On the other hand, the full spectrum of a pixel in the

image taken by the filter-based imaging spectrometer is

captured by a single sensor responding to the reflectance at

different wavelength bands.

NIR spectroscopic instruments, both the probe-based

spectrometer and the imaging spectrometer, are prone to

biases between instruments or among different pixels in the

CCD array. Each pixel in the CCD array in a NIR imaging

spectrometer can be thought of as a different instrument.

Therefore, it is important to standardize the pixels in the

CCD array to achieve consistent spectral responses across

the spatial dimension of the array. Furthermore, for multiple

instruments, differences among instruments introduce the

calibration model transfer problem [3]. Much work has been

published regarding the standardization of probe-based NIR

spectroscopic instruments and the transfer of multivariate

calibration models [3–6]. The general idea of standardization

is to model the instrumental differences. The spectral

response of a subset of samples measured on the primary

Copyright # 2007 John Wiley & Sons, Ltd.

Line-scan NIR imaging systems 89

instrument is regressed against the same subset measured on

the secondary instrument. Thus, changes in the response

variables between the two instruments can be corrected and

the original model can be used for prediction on the

secondary instrument without having to compute new

regression coefficients [4].

Standardization of NIR imaging spectrometers is subject to

all the error contributions of conventional one-dimensional

probe-based spectroscopy (noise, drift, non-linear response

of detectors, wavelength-dependent errors) as well as the

two-dimensional or spatial error components associated

with camera devices and illumination (readout errors,

inconsistent detector responses, quantization errors, and

non-uniform lighting) [7]. This requires that the standard-

ization of the imaging spectrometer must be done for each

spatial or pixel position and also for each wavelength if there

are wavelength-dependent errors.

Standardization of tunable filter-based NIR imaging

spectrometers has received attention recently. Geladi et al.

[8] published one paper addressing the standardization of a

MatrixNIRTM chemical imaging system using a liquid crystal

tunable filter (LCTF) in combination with an InGaAs diode

array detector. The results are based on recalibration against

known reference standards. Standard NIR reflectance

materials, the calibration surfaces made of Spectrolon with

different levels of reflectance (99%, 75%, 50%, and 2%

reflectance) and known ‘true’ reflectance spectra in units of

reflectance percentage, were employed in their method. Each

calibration surface was imaged several times and averaged

so that the influence from the non-uniformity on the surface

was eliminated and thus the noise in the image data is only

from the imaging instrument and illumination. Then a linear

or a quadratic regression model was fitted between the ‘true’

reflectance spectral value of the standard materials at each

wavelength band and the measurement value (in units of

signal intensity counts read out from the A/D converter of

the spectrometer) at specific spatial positions at the same

wavelength in the hyperspectral images. Thus, a linear or

quadratic calibration model cube with the same dimension as

the hyperspectral image was obtained and used to transform

the readout from the spectrometer to the reflectance image in

units of reflectance percentage. It is able to compensate for

both the sensitivity difference of the InGaAs detectors at

different wavelengths and the illumination unevenness and

detector inhomogeneities in the spatial dimensions.

Recently, Burger and Geladi [7] addressed further calibration

issues of this instrument. External standards (i.e., the

standard reflectance materials that are imaged separately

from the images to be corrected) are used to correct

pixel-to-pixel variances due to camera inconsistencies and

variation in sample illumination, and internal standards (i.e.,

a mosaic of different standard reflectance materials imaged

together with the objects of which the images need to be

corrected) are used to compensate for signal drift over time

due to changes in power or temperature effects.

A difficulty with the methods in Geladi et al. [8] and Burger

and Geladi [7] is that standard reflectance materials must be

used. The ideal standard material for their methods is a

material with spatial and spectral uniformity. However,

such standard materials are not easily found for the NIR


region [7]. The Spectralon materials with different reflectance

values used in their papers are created by adding different

amount of carbon black to a white Teflon-based material and

appear inhomogeneous and textured at high resolution [7].

Although the material was imaged several times and the

images were averaged, it is not easy to guarantee the

uniformity of the material. Maintenance of such material is

another problem because their physical properties may

deteriorate with time due to scratches on the surface and

shape change affecting precision. Any errors in the reference

standards will ultimately compromise the standardization

result.

However, it can be argued that one does not need to

standardize the measurement of the imaging spectrometer to

the ‘true’ reflectance. Calibration does not require that the

reflectance is ‘true’ with respect to the absolute value since it

is not an absolute method. All that is needed is to correct

among pixels for differences in reflectance, and for spatial

variations due to non-uniform lighting, etc.

In this paper, we develop a simple method for standardiz-

ing the line-scan imaging spectrometer to reduce the

systematic errors along the spatial axis x and the spectral

axis l in the image data without using the expensive uniform

reflectance standard materials with known spectra. We

further demonstrate the effectiveness of this standardization

for improving the detection of subtle features in NIR images.

2. LINE-SCAN NIR IMAGING SYSTEMSAND THEIR ERROR SOURCES

The line-scan NIR imaging spectrometer used in this paper is

converted from a monochrome area NIR camera by adding

an ImSpector imaging spectrograph [9] between the front

optics lens and the back InGaAs area CCD array of the

camera. A graphic representation of the imaging system is

shown in Figure 1. For each scanned line across the sample,

the reflected light is vertically dispersed into its continuous

spectral distribution by the ImSpector spectrogragh and is

captured by the area CCD array detector as a spatial–spectral

(x� l) intensity image with the resolution 126� 110 pixels.

The 110 wavelength bands are from 933 to 1663 nm. By

moving the sample at a constant velocity in a perpendicular

direction to the scan, multiple lines are recorded by the CCD

array and a hyperspectral image (y� x� l) of the sample is

obtained.

The spectral data collected from the A/D converter of the

imaging spectrometer represents the signal intensity counts,

not actual reflectance values. The raw spectral data are

mainly influenced by the light intensity of the lamp. As the

lamp is used, the values may decrease due to decreasing light

intensity. On the other hand, the raw data do not reflect the

true intensity of the reflected light because the CCD detector

generates charges even though there is no light exposure on

the detector. These temperature-generated charges cause a

small signal, called dark current [8], typically varying from

pixel to pixel. In precise measurements, this offset must be

measured and deducted from the A/D converter counts.

In practice, the raw spectral data are transformed into

reflectance (or absorbance) units by comparing with

spectra of standard materials. The usual transformation to

J. Chemometrics 2007; 21: 88–95DOI: 10.1002/cem

Figure 1. Schematic picture of the line-scan NIR imaging spectrometer [9].

90 Z. Liu, H. Yu and J. F. MacGregor

reflectance values is obtained by correcting sample spectra

for dark current and dividing by a similarly corrected total

reflectance spectrum. This is also the inherent correction

mechanism integrated into the data acquiring software of

the NIR spectrometer, and completed at the start of an

imaging run. The procedure is described as follows: first, a

spatial–spectral (x� l) image for a scanned line of the dark

current is recorded with the lens cap in place to block light

from entering the spectrometer; second, a spatial–spectral

(x� l) image for a scanned line of a white total reflectance

standard is recorded. An unglazed white ceramic title is used

for this purpose. The sample NIR reflectance image R

captured by the spectrometer is calculated from the system

response by taking, pixel by pixel, the ratio of each sample

corrected signal to the corrected white image signal using the

following equation:

ryxl ¼syxl � dxlwxl � dxl

(1)

where ryxl is an element of the hyperspectral reflectance

image cube R in the unit of reflectance percentage, wxl is an

Figure 2. (a) The monochromatic image of

and thickness at the wavelength around 1200

the image at the locations as marked in su


element of the raw spatial–spectral image W of a scanned line

of the total reflectance standard (i.e., the white tile) and

dxlis an element of the raw spatial–spectral image D of a

scanned line of the dark current imaged by blocking the lens.

Equation (1) is a linear standardization where the

coefficient 1= wxl � dxlð Þ is found from one standard

reference value only and therefore is often termed one-point

calibration [7]. It compensates for much of the spatial

non-uniformity across the scene line due to both lighting and

detector differences.

Figure 2(a) shows the monospectral NIR image of a red

plastic shim with uniform surface and thickness at the

wavelength band around 1200 nm. The reflectance intensity

is calculated with Equation (1). The contrast of the image is

enhanced by histogram stretching, thereby displaying the

minimum value in the momospectral image with black and

the maximum value with white.

There are evident streak lines along the direction of motion

(y) in Figure 2(a). This is caused by the differences among the

sensors in the CCD array along the spatial axis x. It can also

be seen that there is contrast difference (e.g., shadowy trends)

a red plastic shim with uniform surface

nm. (b) The spectra of the two pixels in

bpart (a).



on the right side across the x–y plane of the image. The

baseline difference between the spectra (Figure 2(b)) of the

two pixels highlighted in Figure 2(a) is caused by the

illumination difference and the CCD array sensor difference

between the two positions. Both the streak lines and the

shadow trend indicate that Equation (1) cannot totally

eliminate the spatial non-uniformity due to the detector

differences and lighting unevenness. Further standardiz-

ation is needed.

Furthermore, for a given pixel position in the x plane, if the

corresponding sensors along the l axis, giving the multi-

band spectrum of this pixel, have different sensitivity to the

light at different wavelength bands, its reflectance spectrum

measured by the spectrometer would be different from its

true reflectance spectrum. Standard illuminating sources

with peaks at precisely known wavelengths are usually used

to correct this error in the spectrum [9].

Figure 3. Average line image of the red plastic shim. This

figure is available in colour online at www.interscience.wiley.

com/journal/cem.

3. METHODOLOGY

The standardization method in this paper is a further

standardization based on the reflectance image calculated

with Equation (1). It will effectively correct the multivariate

NIR images for all of the effects discussed above. The method

is based on the use of standardization samples having

spatially uniform spectral response. Color-coded plastic

shims were used in this work. Each shim has a relatively

uniform spectral response and an even thickness at different

spatial positions. For the NIR image of a plastic shim taken by

the line-scan imaging spectrometer, each scanned line is

measured by the same sensors in the InGaAs CCD array of

the camera. Due to the uniformity of the plastic shim, it is

reasonable to assume that the variation within the scanned

lines (in the y direction) is random noise. Calculating the

average along the dimension of y in an image, we obtain the

average spatial–spectral intensity image of the scanned lines

(called average line image for the purpose of this paper, with

the spatial–spectral resolution of 126� 110 pixels.). Based on

the ‘uniformity’ assumption, the noise from the physical

variation in the plastic shim is almost eliminated by this

averaging and thereby the noise in the average line image

arises only from the difference between the sensors in the

InGaAs CCD array and the unevenness of the illumination.

Then, by calculating the average along the dimension of x in

the average line image, we obtain the average multi-band

spectrum (called average spectrum for the purpose of this

paper, with the spectral resolution 110� 1) of the 126 spectra

in the average line image. This average spectrum is the average

spectrum of all the pixels in the NIR image. The influence of

the variation between the sensors in the CCD array and the

influence of the illumination unevenness along the spatial

axis of x, which are not eliminated by Equation (1), are both

reduced by the second averaging. Thus, the average spectrum

is almost free from the influence of variations in the imaging

system and will be used as the reference spectrum for the 126

spectra in the spatial–spectral average line image. The objective

of our method is to get a correction factor for each element in

the spatial–spectral average line image, which also means

getting a correction factor for each sensor in the InGaAs CCD


array. Each scanned line forming the hyperspectral image

will be corrected by the factors and thereby each element in

the hyperspectral image cube is standardized.

Clearly one needs standardization samples with relative

spatial uniformity. The more spatial variation one has, the

poorer will be the result. However, because of averaging over

the two spatial axes, the method will be quite insensitive to

small spatial variations. Therefore, any samples with

relatively uniform spectral response, a flat surface, and

uniform thickness can be used to perform the new

standardization method. The spatial uniformity of a sample

can be checked by looking at the consistency of several

spectra measured by a single point NIR spectrometer at

randomly selected locations on the sample.

Four different plastic shims with the colors of coral, pink,

white, and yellow were imaged. Six images were taken at six

different locations on each shim. In total 24 images were

taken. Each image had 200 scanned lines in the y direction.

Therefore, the NIR hyperspectral image had a y� x� ldimension of 200� 126� 110. The images were in the units of

% reflectance ratio to the white reference calculated with

Equation (1). The spatial–spectral average line image and the

average spectrum of each image are calculated. Figure 3

illustrates the average line image from one image of the red

shim using a false color intensity image. Figure 4 shows the

plot of two spectra on the average line image at different

locations, and Figure 5 shows the average spectrum of the

image. For visual enhancement the intensity image has been

color-coded using the color scheme shown in the color bar

toward the right of the image. It is observed that the average

spectrum in Figure 5 is less noisy than the spectra in Figure 4.

The average line images and the average spectra of the 24

images are used to estimate the correction factor for each

sensor in the InGaAs CCD array.

Figure 6(a) shows the relationship between the elements of

the 24 average line images at the spatial–spectral position

x¼ 30 and l¼ 90 and their reference, the average spectral

values at l¼ 90. Figure 6(b) shows the relationship between

the elements of the 24 average line images at the position

x¼ 100 and l¼ 70 and their reference values. Both plots


Figure 5. Average spectrum of all the pixels in the NIR image

of the red plastic shim.

Figure 4. Two spectra from the average line image of the red

plastic shim at x¼ 59 (solid line) and x¼ 60 (dotted line).Figure 7. Image presentation of the slope matrix b. This


com/journal/cem.


indicate that there is a linear relationship between them as

denoted by the straight line in each figure. The same

relationship is also observed between the elements of the

average line images at other positions and their reference

Figure 6. The elements in the average line

(a) The elements in the 24 average line ima

l¼ 90 versus their reference, the averag

elements in the 24 average line images at t

versus their reference, the average spectr


values. That relationship can be expressed as

sl ¼ axl þ bxllxl (2)

Where sl is the average spectral value at the wavelength

band l, lxl is the value of the average line image at the

spatial–spectral coordinate position x and l, axl is the

intercept coefficient, and bxl is the slope coefficient.

The coefficients for all the elements in the average line image

are obtained by fitting a line regression model by least

squares between the average line images and the average spectra

of the 24 images. Figures 7 and 8 illustrate the slope matrix b

and the intercept matrix a by visualizing them as false color

intensity images. It can be observed that the streak lines and

shadow trend also appear in a and b. The streak lines

indicates that the difference among the sensors along the axis

x will be corrected and the shadow trend means that the

baseline offsets in the original image caused by illumination

difference or stray light along the axis x will be compensated.

It is also seen that there are variations (although smaller

variations) among the coefficients between the factors at

different wavelength bands. That means the standardization

method also corrects the sensitivity variations between the

detectors along the spectral axis l.

images versus their reference values.

ges at the spatial location x¼ 30 and

e spectral values at l¼ 90. (b) The

he spatial location x¼ 100 and l¼ 70

al values at l¼ 70.


Figure 8. Image presentation of the intercept matrix a. This


com/journal/cem.


The slope matrix b and the intercept matrix a then can be

used to filter each scanned line in the NIR image R to

counteract systematic errors from the imaging system using

the following equation:

ryxl;corr ¼ axl þ bxlryxl (3)

This method is clearly related to the general class of

Multiplicative Scatter Correction (MSC) methods with the

exception that MSC uses the residuals following regression

on the mean, but this method uses the projection onto the

mean. It is an extension of the idea to imaging spectrometers.

4. RESULTS

The standardization method is evaluated in this section

using two examples. The first illustrates the qualitative

(visual) and quantitative (variance reduction) results

obtained by applying the standardization method to the

uniform plastic shim images. The second example illustrates

the greatly increased sensitivity, as a result of standardiz-

ation, obtained upon performing multivariate image analysis

(MIA) on a shim containing subtle surface features.

Figure 9. Correction result. (a) Corrected m

shim at wavelength band 1200nm. (b) Th

marked in Figure 2(a).


4.1. Evaluation using the uniform shimimagesFigure 9(a) shows the corrected result, on the image in

Figure 2(a), after applying standardization to the NIR

spectrometer. It is observed that both the streaks caused

by the non-uniformity of the CCD array and the shadow

caused by the unevenness of illumination across the spatial

axis x are reduced remarkably. The small thickness variation

in the sample, which is submerged by the systematic noise in

the original images, is also shown more clearly after the

correction. Figure 9(b) shows the corrected results of the

spectra for the same two pixels shown in Figure 2. Compared

with the plots in Figure 2(b), the baseline shift is remarkably

reduced and the two spectra look more consistent with each

other. The two plots in Figure 9(b) also look less noisy than

the plots in Figure 2(b). That means that the noise along the

axis l in the image cube is also reduced by applying the

standardization (3).

Figure 10 quantifies the reduced variation in the spectra at

all the pixel locations achieved by standardization. The

standard deviation of the 126 pixels across the x axis in the

spatial–spectral intensity image for each scanned line of the

red plastic shim is shown for each wavelength band, with

and without applying the standardization. The standard

deviation across the x axis at each wavelength band is

reduced substantially after standardization, as is the

structured behavior of the standard deviation with wave-

length. The same results are also observed for the images of

the other plastic shims which are not shown here. This

illustrates that much of the variation due to pixel differences,

both in the spatial direction and in the wavelength direction

has been eliminated. This also provides a way of checking the

validity of the standardization periodically by scanning a

standard shim and computing the standard deviation plot in

Figure 10 to see that it is consistent with the lower set of

curves.

4.2. Improved detection of subtlespectral featuresThis example is provided to illustrate how the standardiz-

ation can improve the ability of MIA to uncover subtle

onochromatic image of the red plastic

e corrected spectra of the two pixels


Figure 10. Standard deviation at each wavelength band of

the 126 pixels across the x axis in the spatial–spectral inten-

sity image for each scanned line of the red plastic shim. Dark

plots: standard deviations before standardization; gray plots:

standard deviations after standardization.


features in the NIR image. A yellow plastic shim with a

fingerprint superimposed on top of some glue residual left

from a removed piece of adhesive tape was imaged. Since the

spectral channels are highly correlated, MIA using a

multi-way principal component analysis (MPCA) decompo-

sition [10] was used to extract the variations in the

hyperspectral image. Two score images explained 99.99%

of the sum of squares of the spectral intensity variation in the

image. Figure 11(a) shows a false color composite image

obtained by combining the first two score images from the

NIR reflectance image (Equation (1)) without using the

standardization. It can be observed that the streak lines blur

Figure 11. (a) and (b) MIA result based on t

with a finger print and some glue residual a

color score image; (b) t1–t2 scattering plot,

background. (c) and (d) MIA result based on

plastic shim: (c) Combined t1þ t2 false colo

the ellipse highlights the pixels of the backg

online at www.interscience.wiley.com/journa


the details of the fingerprint and the glue residual and make

them barely visible. This indicates that the spatial variation in

the sensors at different pixel locations is greater than the

signal arising from subtle effects of the fingerprint and the

glue residual. The corresponding t1 –t2 score plot for this

uncorrected image is shown below in Figure 11(b). It is

observed that the pixels of the background scatter over a

wide area in the t1 –t2 score space as highlighted by the ellipse

in Figure 11(b). This implies a sizable variation in the spectral

response of the background pixels of the uniform shim.

Figure 11(c) and (d) shows the result of the same

MPCA-based MIA procedure after pretreating the NIR

image by the standardization model, Equation (3). It is

shown that the fingerprint and the glue residual (vertical

band from the removed tape lying under the finger print) are

more clearly distinguished from the background and from

each other in the composite false color score image. It is also

observed that the background pixels of the uniform plastic

shim now cluster much more tightly in the scattered t1 –t2score plot as highlighted by the smaller ellipse in

Figure 11(d).This tightness of the distribution of the back-

ground pixels in the t1 –t2 score plot implies that, as expected,

the spectral response of the shim is reasonably uniform.

5. CONCLUSIONS AND DISCUSSION

A simple and easily applied standardization method is

proposed for NIR line-scan imaging spectroscopes to correct

for pixel-to-pixel differences in the CCD array and for

systematic biases due to uneven lighting, etc. No special

standards with known reflectance are needed, only homo-

geneous samples with spatially uniform reflectance spectra.

The method is an extension of the similar concept of MSC

methodology to NIR imaging.

he original NIR image of a plastic shim

t the center: (a) Combined t1þ t2 false

the ellipse highlights the pixels of the

the corrected NIR image of the same

r score image; (d) t1–t2 scattering plot,

round. This figure is available in colour

l/cem.



The performance of the method is illustrated using two

examples. From both the visual difference between the

images before and after the correction and the standard

deviation of the spectral responses across a uniform

standard, it can be seen that the instrument standardization

method proposed is an effective way to reduce the systematic

errors arising from base-line offsets and scaling differences

that cannot be eliminated using the standard single reference

reflectance calculation. Both the inconsistencies along the

spatial x axis and the spectral l axis in the imaging system are

corrected. The benefit to the result of subsequent image

analysis is demonstrated with a MIA example. It is shown

that subtle effects in the image become much more clearly

apparent after the application of standardization.

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standardization of line-scan nir imaging systems

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