NguyenVan-Giang1
-
(Department of Information Systems, Le Quy Don Technical University, Hanoi, Vietnam
giangnv@mta.edu.vn)
Copyright © The Institute of Electronics and Information Engineers(IEIE)
Keywords
X-ray imaging, Digital radiography, Deblurring, Denoising, Detector blur, Deconvolution
1. Introduction
Digital radiography (DR) is an X-ray radiographic imaging technology acquiring
digital projection images using photo-stimulable storage phosphor (computed radiography,
or CR), amorphous selenium, amorphous silicon, charge coupled devices (CCDs), or a
metal-oxide semiconductor field effect transistor. Among many medical imaging modalities
in digital radiography, the two most popular ones are computed radiography and flat-panel
digital radiography [1].
In CR, a photo-stimulable phosphor is coated on the plate, referred to as an imaging
plate (IP), which is housed in a cassette to protect it. The IP is then considered
the digital detector in CR. However, this technology has some limitations. The spatial
resolution is less than that from DR, and the total acquisition time and image reading
time are much higher than DR. The durability of the IP is less than with a DR image.
Flat-panel digital radiography systems have been developed to overcome the shortcomings
of CR systems. In DR, the digital detector is designed with a flat panel. Currently,
there are two categories of flat-panel digital radiography imaging systems (based
on the type of detector used [2]), and they have been popularly referred to as (i) indirect conversion digital radiography
systems, and (ii) direct conversion digital radiography systems. The latter use detectors
that convert X-rays directly to electronic signals, but in the former, X-rays are
first converted into light using a phosphor. The emitted light from the phosphor falls
upon a matrix array of electronic elements to create and store electrical charges
in direct proportion to the X-ray exposure. The charges produce electrical signals
that are subsequently digitized and processed by a computer to produce an image. These
two types of modalities have high sensitivity but are expensive due to the involvement
of a thin film transistor (TFT) array in the detector. In a second type of indirect
conversion detector, charge coupled devices (CCDs) are coupled to a phosphor cesium
iodide scintillator. X-rays hit the phosphor to produce light, which then hits the
CCD array that converts the light into electrical signals, which are digitized and
processed by a computer to produce an image.
While the above detector technologies result in superior DR image quality, the
high price prevents use in rural areas and when imaging a large object. To reduce
the price of the DR detector and increase its lifetime, attempts have been made to
use a lens-coupled X-ray detector [3,4]. That approach photographs a screen (usually an X-ray scintillator) exposed to X-rays.
This collection of X-ray photons can be done by coupling a scintillating screen and
a camera, which captures the scintillating screen to create an X-ray image. However,
since the optical sensors used in digital cameras are much smaller than the objects
to be imaged, the camera must be moved far away from the illuminating scintillator
so that the entire X-ray image fits on the sensor.
Inspired by that idea, in this paper, we built and investigated an imaging system
that is likely to be cheaper than existing methods of X-ray imaging. Compared to the
conventional indirect flat-panel system, the proposed imaging system uses a mirror
to reflect the light from the scintillating screen to the camera. This mirror is used
to prevent the incoming X-rays from hitting the camera sensor. This camera design
is similar to the one used in high-resolution micro-CT [5], but not in digital radiography, as in this work.
On the one side, since the proposed imaging system shares some similarities with
existing systems, the captured image has to go through an extensive image processing
pipeline (described in Section 2) to produce the display image as closely as possible
to currently used DR imaging techniques. One the other side, since the acquiring process
is different from ordinary DR with existing flat-panel detectors, the acquired images
are degraded by two factors: unwanted impulsive noise and blurring. The noise here
is not only Poisson noise, but also $\textit{impulsive-style noise}$ that occurs during
DSLR acquisition under dark conditions where the inner space containing the camera
(including the mirror and the one side of the scintillator) has to be kept in total
darkness to increase the sensitivity of the acquisition. The impulsive-style noise
is caused by failures in sensors, readout circuits, A/D converters, or communication
channels. This impulsive-style noise is not only found in our imaging system with
a lens-coupled X-ray detector where the consumer camera takes the picture of the scintillating
screen to create an X-ray image, but is also found in standard digital radiography,
as reported in [6]. This impulsive-style noise, even at a low rate, leads to a poor visualization result
(after the raw image goes through contrast enhancement). Furthermore, the acquired
image in DR is also blurred due to source blur, detector blur, and motion blur. These
two issues, especially the $\textit{impulsive-style noise}$, were not addressed in
previous work [3-5].
To address the noise issue, we use a statistical-based impulsive noise removal
method and make it work in separate channels of the color image captured by the DSLR
camera. This is to ensure that only impulsive-style noise is removed, while the detail
is kept perfectly in the acquired image. To deblur the image, rather than using the
blind deblurring method, which might result in an artificial image (with unwanted
nonrealistic patterns), we use non-blind deblurring with a systematically derived
blur kernel. Our deblurring framework consists of two steps: (i) estimating the X-ray
source and detector point spread functions (PSFs) via the radiographs of a pre-designed
metal plate, and (ii) using the estimated PSFs (the blur kernel) to perform deblurring
via a non-blind deconvolution method. Our deblurring process takes into account the
distance to the detector plane from the object to be imaged.
In Section 2, we describe the imaging system used to acquire the DR image, as
well as the image processing pipeline to produce the final ready-to-display DR image.
The section also describes a method to remove impulsive-style noise, and a method
to deblur DR images. Section 3 presents results from using experimental data with
our prototype DR imaging system. Section 4 concludes the paper.
2. Methods
2.1 Imaging Systems and Image Processing Pipelines
The working principle of the proposed imaging system is summarized in Fig. 1. It consists of an X-ray tube, a scintillating screen, a mirror, and a camera. When
the X-rays are emitted, they come through the object being imaged. Some are attenuated
by the object, and the remaining hit the scintillation screen. Depending on the number
of X-rays that hit it, the scintillation screen will light up. At that moment, the
camera opens its lens to capture an image of the scintillating screen. Note that,
to protect the lens from being hit by X-rays passing through the scintillator, the
camera takes an image of the mirrored scintillating screen. The captured image will
be processed to generate a ready-to-display image.
Fig. 1. Diagram of the proposed imaging system.
To realize the idea, we built the prototype imaging system in Fig. 2. The system consists of the following main parts: (i) the X-ray source is a Toshiba
Rotanode E7239X; (ii) the scintillator is a Hamamatsu GPXS J10666-100; and (iii) the
consumer camera is a Canon EOS 750D. The X-ray source is synchronized with the camera
so the camera will open its lens to acquire the light from the scintillating screen
when it reaches its brightest state. The settings of the DSLR camera are as follows:
shutter speed = 1/3 sec, ISO=800, and AV=10. The lens is locked in focus by taking
an image of a resolution test chart placed behind the scintillating screen.
Fig. 2. Our prototype DR with an indirect DSLR-based detector.
In digital radiography, regardless of the imaging system (the DR scanner), radiologists
always want to view the image in the display device (for example, on an LCD monitor),
and images (from different scanners) of the same patient should be the same. Therefore,
we need to adopt an image processing pipeline to generate standardized, ready-to-display
images. Regarding our proposed imaging system (with a DSLR-based detector), we use
an image processing pipeline that consists of the following steps.
(i) Remove radiographic impulsive noise.
(ii) Use flat-field correction to correct the varying flat field in the image [7].
(iii) Image scaling determines the actual object region in the DR image.
(iv) Image deblurring (or edge restoration) handles blurring due to the detector and
the X-ray source.
(v) Apply dynamic range reduction and latitude reduction [8].
(vi) Use multiscale contrast enhancement [9,10]
The aim of image processing here is to eliminate all factors contributing to
unwanted degradation in the acquired image, and to generate the final ready-to-display
image (described in Section 3). Note that in our imaging system, steps (i) to (iv)
are performed with a color image, while in steps (v) and (vi), the processing image
is grayscale to match the standard in digital radiography.
Most factors that degrade the standard DR image quality (with an actual flat-panel
detector) have been addressed in previous work [7-10]. However, regarding our DR imaging system with DSLR-based detector, there are two
new degrading factors that have not been addressed in previous DR imaging systems.
Those are impulsive-style noise, and blur in the image when capturing an object that
is a little far from the scintillating screen.
2.2 Impulsive Noise Removal
In our imaging system, the acquired images are usually corrupted by impulsive
noise, as shown in Figs. 3(a), 4(a) and 5(a). By analyzing the raw image (in our case,
it is the CR2 file), which is a color sample output from an image sensor with a color
filter array (CFA), we found that the noise shows up in the raw data (before demosaicing)
and appears differently in different color filters and in nearby pixels. (See Figs.
3(b) to 3(e)). That eventually results in complex impulsive noise patches in the acquired
RGB image in Fig. 3(a). We also found that the location of the impulsive noise here is random for each acquisition.
Fig. 3. Impulsive noise in the acquired RGB image and its raw images: (b)-(e) are
the raw images for red, green, green, and blue before demosaicing to generate full-color
image (a). Note that for a CR2 raw file, the output from the color filter array has
the pattern RGGB.
When analyzing the acquired image in Fig. 4(a) in the $\textit{HSV}$ color space shown in Figs. 4(b)-(d), we realized that different
noisy pixels in a color image have different contributions from the corresponding
pixels in the image from each $\textit{H}$, $\textit{S}$, and $\textit{V}$ channel.
Note that modeling in $\textit{HSV}$ in our case is from the fact that the acquisition
process takes an image from a scintillating screen. For the full information, we configured
the camera to take a color image. The $\textit{HSV}$ color space is selected since
an $\textit{HSV}$ representation models how colors appear under light, and the $\textit{HSV}$
model has a better color description for human interpretation.
Furthermore, the region contaminated by impulsive noise varies in each channel,
as seen in Figs. 4(b)-(d). The size of the region also changes in each channel (the
noise patch gets larger in the $\textit{H}$ channel, and smaller in $\textit{S}$ and
$\textit{V}$ channels). This fact is found not only in the illustrated samples in
Fig. 4, but also in all images acquired using our proposed imaging system (such as the one
of a chest region in Fig. 5).
Based on this observation, we performed denoising on each channel ($\textit{H}$,
$\textit{S}$, and $\textit{V}$) separately. Since each channel was closed to noise
that occurs in standard digital radiography, we employed a statistical-based impulsive
noise filter [6] that was designed for standard digital radiography. (Note that in the image from
our imaging system, the noise is not salt-and-pepper noise, since the region of degradation
might occupy multiple pixels, and therefore, the standard salt-and-pepper noise filter
cannot be used.)
Fig. 4. Illustration of denoising an electronic circuit board image: (a) the noisy
image; (b)-(d) $\textit{H}$, $\textit{S}$, $\textit{V}$ channels, respectively, of
the noisy image; (e)-(g) $\textit{H}$, $\textit{S}$, $\textit{V}$ channels, respectively,
of the denoised image; (h) the denoised image.
Fig. 5. Illustration of denoising a chest region: (a) the noisy image; (b)-(d) $\textit{H}$,
$\textit{S}$, $\textit{V}$ channels, respectively, of the noisy image; (e)-(g) $\textit{H}$,
$\textit{S}$, $\textit{V}$ channels, respectively, of the denoised image; (h) the
denoised image.
The statistical-based impulsive noise filter [6] is based on a switching scheme where all pulses are first detected by a pulse detector.
The detector assumes that the major contribution to image noise is made by the photon-counting
process, with some pixels corrupted by impulsive noise. This assumption is generally
true when acquiring an image in a controlled environment, as in our imaging system.
Having the estimated pulses, the noisy image is filtered with a standard median filter.
In the images acquired by our proposed system, the area of pulses in the image
of the $\textit{H}$ channel is usually larger than in the $\textit{S}$ and $\textit{V}$
channels. Therefore, depending on the conditions of X-ray acquisition, we need to
apply the statistical-based impulsive noise filter more than one time to the image
of the $\textit{H}$ channel, rather than just one time in the images of the $\textit{S}$
and $\textit{V}$ channels.
It is important to note that the median filter (and its median root prior in
a Bayesian framework [11]) is very popular in radiographic imaging, since it does not alter the valuable information
in the image. This is different from data-driven, state-of-the-art Gaussian or Poisson
denoising methods, which tend to remove noise and promote non-realistic details in
radiographic imaging.
Finally, note that in practice, since the radiologist has no difficulties detecting
a signal in the presence of Poisson noise, we did not try to eliminate Poisson noise.
Here, we aim to get rid of the unwanted impulsive noise that leads to a poor visualization
result.
2.3 Model-based Image Deblurring
Unlike consumer imaging where there are many uncertainties during image acquisition,
especially camera motion and object motion, the acquisition in digital radiography
is performed in a controlled environment. Therefore, to quantify radiographic blur,
researchers in the field usually use a parametric mathematical model to describe the
blurring process. The general blurring processes are usually modeled as
where $y$ is the observed blurry image, $x$ is the latent sharp image, $k$ is
the approximated blur kernel, and $n$ models noise.
In our particular X-ray imaging system, we denote$p_{i}^{\left(s\right)}$ as
the PSF of the X-ray source blur on the detector plane at a source-to-object distance
(SOD) of $SOD_{i}$ and an object-to-detector distance (ODD) of $ODD_{i}$, where $p^{\left(d\right)}$
is the PSF of the detector. Different from source blur, detector blur does not vary
with SOD and ODD. We simplified the model by ignoring motion blur on the assumption
the patient does not move during data acquisition. Then, the blurring process is modeled
as follows:
where
Fig. 6. (a) Image of the metal plate used to estimate the blur kernel; (b) extracted
samples from an image when the distance from the object to the detector = 5 cm; (c)
extracted samples from an image when the distance from the object to the detector
= 40 cm.
is the blur kernel on the imaging plane, $y^{i}$ is the observed blurry image,
and $x^{i}$ is the latent sharp image at $SOD_{i}$ and $ODD_{i}$.
The PSFs ($p_{i}^{\left(s\right)}$ and $p^{\left(d\right)}$) are usually derived
using data-driven approaches where it estimates PSF directly from radiographs of known
objects [12,13]. And in this work, we used a recently proposed systems approach to blur estimation
and reduction (SABER) [12] for modeling and reducing the blur in our proposed X-ray DR imaging system. SABER
consists of two steps: (i) estimation of the blur kernel; and (ii) deblurring using
the estimated blur kernel.
To estimate the blur kernel, we illuminated a metal plate so it was perpendicular
to the direction of the X-rays, and its edge was nearly horizontal and vertical. We
acquire data when the object is placed in two different positions. In our experiment,
one position was 5~cm from the detector (the scintillation screen) and the other was
40 cm (see Fig. 6). Having the two datasets, each with two projection images, the estimation involves
the use of the L-BFGS-B algorithm (Details on blur kernel estimation can be found
in [12]). Having the blur kernel, we can use either a Wiener filter [14] or a regularized least squares deconvolution (RLSD) algorithm to find sharp image
$x^{i}$ in (2). However, since the DR image has a huge size (with a resolution of about $4000\times
6000$), we use a fast high-quality nonblind deconvolution based on adaptive-prior
regularization [15], since it can find a state-of-the-art and stable deconvolution result in a relatively
short time.
3. Results
To validate the performance of the proposed imaging system, as well as the denoising
and deblurring methods, we acquired images from two objects: a human hand and an electronic
circuit board. These samples were acquired by setting the following parameters in
the scanner: accelerating voltage was 60 kV, the tube current was 160~mA, the distance
from source to detector was 120 cm, and the distance from the scanning object to the
detector was 2 cm.
Fig. 7 shows the acquired images with severe impulsive noise and the denoising results (using
the standard median filter and the statistical-based impulsive noise filter). Using
the statistical-based impulsive noise filter, almost all impulses were found, and
the associated noise eliminated. Since the other image pixels remain untouched, the
denoised image does not have any degradation (in comparison with the acquired noisy
image). Meanwhile, the ordinary median filter could not reduce noise and preserve
the radiographic features within the same time; the detail degraded while the noise
remained, as indicated by the arrows at the top of Fig. 7(c). We conducted an independent image quality assessment with a radiologist, and the
result confirmed the effectiveness of the proposed method in removing noise while
retaining the radiographic information in the human hand. Note that the degree of
noise in the acquired image is inversely proportional to the X-ray dose level passing
through the patient—the lower the dose, the higher the noise. Therefore, our method
to reduce noise is more important when reducing the X-ray dose.
Fig. 8 shows the results for the image of the electronic circuit board. Fig. 8(b) shows that in the denoised image, there was no loss of detail. Meanwhile, the standard
median filter suffered loss of detail in the denoised images, as indicated by the
arrows at the top of Fig. 8(c).
Fig. 7. Denoising result for a human hand: (a) the noisy image; (b) the image denoised
by our method; (c) the image denoised by a median filter. Images on top of parts (a)
to (c) are zoomed-in regions.
Fig. 8. Denoising results for an electronic circuit board: (a) the noisy image; (b)
the image denoised by our method; (c) the image denoised by a median filter. Images
on top of parts (a) to (c) are zoomed-in regions.
To check the effectiveness of the proposed method in eliminating noise while retaining
radiographic detail, we show the results from chest-region imaging where the proposed
method effectively removed impulsive noise while keeping most of the detail. See Fig. 9(b). The median filter removed the noise at the cost of degraded detail, as seen in Figs.
9(c) and (d). By using our method, the chest detail is preserved, as seen in Fig. 9(b), while it was degraded in Figs. 9(c) and (d). Note also that, by increasing the window
size in the median filter, the noise might be controlled in Fig. 9(d), but the detail is smoothed out.
To quantitatively evaluate the performance of the denoising approach, we selected
regions of interest (ROIs) containing detail but no impulsive noise, as indicated
in Fig. 9(a), and we then computed the percentage error (PE) between the denoised image and the
noisy image. (The ROI without impulsive noise in the noisy image is considered ground
truth, since it contains most of the information required by a radiologist.) The PE
is given by:
Fig. 9. Image denoising results for the chest region: (a) the noisy image; (b) the image
denoised by our method; (c) images denoised by a median filter with a window size
of 5${\times}$5; (d) images denoised by a median filter with a window size of 7${\times}$7.
The highlighted red boxes in Fig. 9(a) indicate the ROI in order to calculate the error between the denoised images and
the original noisy image.
Table 1. Percentage error measured on the denoised images.
|
ROI
|
Proposed method
|
Median filter with window size 5×5
|
Median filter with window size 7×7
|
|
1
|
0.44
|
2.52
|
3.03
|
|
2
|
0.45
|
2.56
|
3.02
|
|
3
|
0.26
|
2.26
|
2.68
|
|
4
|
0.27
|
2.34
|
2.83
|
|
5
|
0.35
|
2.42
|
2.86
|
where $\textit{x}$ is the noisy image, $\hat{x}$ is the denoised image, and $\left\|
.\right\| $ denotes the $L_{2}$norm.
Fig. 10. Estimated blur kernel at a distance of 2~cm from the scintillating screen.
Fig. 11. Deblurring results for (a) the acquired image; (b) the deblurred image. Images
in the top row are zoomed-in regions of the images in the bottom row. The global contrast
factor for the acquired image is 1.52, while the value for the deblurred image is
1.73.
Table 1 lists the percentage error, showing that the proposed method achieved the smallest
error with all designated ROIs.
The problem in our case is significantly different from standard image denoising,
where the image might be altered to eliminate noise. Therefore, we compared only the
standard median filter against our method. (The other well-known impulsive image denoising
methods tend to smooth out the detail and the shadow detail in the radiographic image.)
Regarding deblurring, the estimated blur kernel was found, and the one for the
image plane at the distance of 2 cm from the detector is shown in Fig. 10. (Note that the final blur kernel was computed from two PSFs: source and detector.)
That blur kernel was then used to deblur the acquired image, and the deblurred result
is shown in Fig. 11. To evaluate the quality of the deblurring method, we measured the global contrast
factor (GCF) [16] for the original and deblurred images. GCF is considered a global focus measure,
and is strongly correlated to human assessment in terms of the degree of focus of
the image, where the higher the value, the better the degree of focus. The measured
GCF value for the original image was 1.52, and the one for the deblurred image was
1.73. (The results for the zoomed-in region were 2.47 for the original image and 2.71
for the deblurred image).
As shown in Fig. 11(b), the deblurred image has sharper edges and is richer in detail. More importantly,
it does not generate an artificial pattern in the deblurred image (as with the standard
image-sharpening algorithm) since the PSF was systematically acquired. Since the deconvolution
algorithm is a regularization method, we can control the degree of smoothness in the
deblurred image with a special hyperparameter. The image in Fig. 11 was restored with a manually, specifically selected value of that hyperparameter.
Finally, to show the overall performance of the proposed imaging system in radiography,
Fig. 12 is a final image that went through the image processing pipeline. Compared to the
acquired image, the radiological features were preserved and are clearly displayed.
In this particular case, the bone contrast was adjusted to satisfy the requirements
of the radiologist.
Fig. 12. (a) The acquired image (in grayscale); (b) the post-processed image.
4. Conclusion
In this paper, we proposed and tested an imaging system to acquire digital radiography
images using a consumer camera. To improve the image quality, we used a statistical-based
impulsive noise removal method to get rid of impulsive noise where the rate is low
but it significantly degrades the visualization results. We also used parametric modeled
non-blind deconvolution to deblur the image.
Experiments show that the denoising method is capable of efficiently eliminating
impulsive noise while preserving detail in the digital radiography images. Meanwhile,
for deblurring, by combining a systems approach to blur estimation with fast non-blind
deconvolution, we can accurately and quickly recover a sharp image from a blurred
image.
The proposed imaging system, and denoising and deblurring methods, when plugged
into an image processing pipeline, can quickly provide DR images with fine detail,
and can be useful for radiography.
Though we tested our imaging system with only one particular type of camera, our
denoising and deblurring methods are expected to work well with other cameras. An
investigation on the use of other cameras in our imaging system is beyond the scope
of this paper. However, we expect using our imaging system with different cameras
will work well, since similar studies have been conducted in the field [4,17].
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Author
Van-Giang Nguyen received the B.S. degree in computer science from Le Quy Don Technical
University, Hanoi, Vietnam, in 2005, and the M.S. and Ph.D. degrees in electronic
engineering from Paichai University, Daejeon, Korea, in 2009 and 2012, respectively.
Since 2013, he has been with the Department of Information Systems, Le Quy Don Technical
University, Hanoi, Vietnam. His current research interests include image processing,
computer vision, and their applications to medical imaging and medical physics.