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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_3592_Библиотеки_им_академика_М_И_Перельмана
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Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
https://t.me/medicina_free
method [15] and after logarithmic transformation are shown in figure 7.1(c) and
7.1(d). For a pixel loss of
, the observed image and estimate using the proposed
70%
method are shown in figure 7.1(e) and 7.1(f). Blind inpainting with Poisson noise for
the Cameraman image with
0%
and
of its pixels missing is illustrated in
70%
figure 7.2.
Figure 7.3 demonstrates blind inpainting with the Rayleigh multiplicative noise
model for a transversal ultrasound (US) image of the carotid artery. The noisy RF
envelope image is shown in figure 7.3(a). The observed image with 75% of the pixels
missing is shown in figure 7.3(b). The estimate using the proposed method is shown in
figure 7.3(c), with the diagonal profiles shown in figure 7.3(e). We can see from the result
of the binary XOR operation between the sampling mask and its estimate shown in
figure 7.3(d), that the incorrectly estimated bits are in the region of low pixel values in
the RF image. The computation time was 119.3 s for an image of size 201 × 201.
7.3.2 Outlier maps for lumen segmentation
We applied our blind inpainting method on an RF ultrasound image, assuming that
it was already masked, to determine which pixels were statistically relevant and
which ones had values that did not fit the distribution. The results obtained with the
transversal and longitudinal images of the carotid artery are presented in figure 7.4
and 7.5, respectively. The values of the parameters used were the same as those used
in the previous section.
Figure 7.2. Blind inpainting with Rayleigh noise with the Cameraman image: (a) original image (cropped);
(b) observed image with Rayleigh noise and
(d) estimate using inpainting with the additive model after logarithmic transformation; (e) observed image with
Rayleigh noise and
70%
of its pixels missing; (c) estimate using the proposed method;
50%
of its pixels missing; and (f) estimate from (e) using the proposed method [5] (© 2015 IEEE).
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Figure 7.3. Blind inpainting with (Rayleigh) transversal ultrasound image of the carotid artery: (a) noisy radio frequency
(RF) image, (b) observed image with 75% of the pixels missing, (c) estimate, (d) result of the binary XOR operation
between the mask and its estimate, and (e) diagonal profiles of the noisy and estimated images [
5] (© 2015 IEEE).
Figure 7.4. Blind inpainting applied on an ultrasound image to detect outliers: (a) transversal RF image of carotid
artery, (b) denoised image, (c) positions of detected outliers, (d) histogram of speckle at valid pixels, compared with the
Rayleigh distribution, and (e) histogram of speckle at outlier positions, compared with the Rayleigh distribution.
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Figure 7.5. Blind inpainting applied on an ultrasound image to detect outliers: (a) longitudinal RF image of
carotid artery, (b) denoised image, (c) positions of detected outliers, (d) histogram of speckle at valid pixels,
compared with the Rayleigh distribution, and (e) histogram of speckle at outlier positions, compared with the
Rayleigh distribution.
Table 7.2. KL divergence of inliers and outliers from ultrasound images.
Image Fraction outliers KL div (inliers) KL div (outliers)
Carotid transversal 0.139 0.074 2.49
Carotid longitudinal 0.21 0.132 3.67
IVUS frame no. 5 0.274 0.0065 4.79
IVUS frame no. 100 0.268 0.0102 5.22
In the estimated outlier maps in figure 7.4(c) and 7.5(c), the white pixels represent
the mask pixels estimated incorrectly (the reference mask is assumed to be all ones,
i.e. all pixels are observed). Comparing these outlier maps with the respective RF
and denoised images, it can be seen that the greatest concentration of outlier values
is in the regions that correspond to the lumen. We also present the histograms for the
speckle noise field computed by element-wise division of the observed image, by the
denoised image
η =ˆyx/
ˆ
. The Kullback–Leibler divergences between the observed
histograms and the analytical Rayleigh distributions for the inliers and outliers from
the ultrasound images are summarized in table 7.2.
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In figure 7.4(d) and 7.5(d), we present the histograms for the noise field over the
pixel locations that are considered statistically valid, compared with the analytical
probability density function for the Rayleigh distribution with parameter equal to
one. For the transversal image, the Kullback–Leibler (KL) divergence between the
histogram and the analytical distributions was found to be 0.074, with
3.92%
of
pixels labeled as outliers. Over the pixels marked as outliers, the KL divergence with
respect to the analytical Rayleigh distribution increased to 2.49. For the longitudinal
image,
of the pixels were marked as outliers, and the KL divergences with
1%
respect to the analytical Rayleigh PDF were 0.132 over the set of statistically valid
pixels, and 3.67 over the outliers. The difference from the analytical curves can be
seen in figure 7.4(e) and 7.5(e).
We also ran the proposed method on two images from the intravascular
ultrasound dataset from [7], and the results are presented in figures 7.6 and 7.7.
Figures 7.6(a) and 7.7(a) show the RF images, figures 7.6(b) and 7.7(b) show the
respective denoised images, and figures 7.6(c) and 7.7(c) show the respective
estimated outlier masks. It can be seen from the outlier masks that the outliers
are concentrated in the regions corresponding to the lumen in figures 7.6 and 7.7.
The histograms of the observed inliers and outliers, along with the analytical
Rayleigh distribution are shown in figure 7.6(d) and (e), and in figure 7.7(d) and (e).
Figure 7.6. Blind inpainting applied on intravascular ultrasound image number 5 to detect outliers: (a)
longitudinal RF image of carotid artery, (b) denoised image, (c) positions of detected outliers, (d) histogram of
speckle at valid pixels, compared with the Rayleigh distribution, and (e) histogram of speckle at outlier
positions, compared with the Rayleigh distribution.
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Figure 7.7. Blind inpainting applied on intravascular ultrasound image number 100 to detect outliers: (a)
longitudinal RF image of carotid artery, (b) denoised image, (c) positions of detected outliers, (d) histogram of
speckle at valid pixels, compared with the Rayleigh distribution, and (e) histogram of speckle at outlier
positions, compared with the Rayleigh distribution.
7.4 Conclusions and future work
We have presented an iterative method for image inpainting without knowing the
locations of the missing pixels, based on alternating minimization to simultaneously
estimate the image and observation mask. The proposed method has been extended
to the Rayleigh speckle noise model as well, and was found to be more accurate than
transforming the model into an additive one without taking into account the
respective statistics. It was experimentally found that applying the inpainting
method to ultrasound images of the carotid artery and intravascular ultrasound
images without loss of pixels, produced an outlier map which indicated which pixel
values are reliable and which pixels are outliers. It was found that the pixels detected
as outliers corresponded roughly to the lumen.
Based on the results obtained with real ultrasound images, current and future
research includes using the estimation of masks to help in obtaining optimal
sampling patterns. A robust mathematical formulation for the segmentation
problem is the subject of current and future research.
Acknowledgments
This work was supported by Fundação para a Ciência e Tecnologia (FCT),
Portuguese Ministry of Science and Higher Education, through a post-doctoral
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fellowship (contract no. SFRH/BPD/79011/2011) and FCT project (UID/EEA/
50009/2013). The authors thank the authors of [24, 46, 62] for sharing the
implementations of their algorithms with us.
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Vascular and Intravascular Imaging Trends, Analysis, and
Challenges, Volume 1
Stent applications
Petia Radeva and Jasjit S Suri
Chapter 8
Differential imaging for the detection of extra-
luminal blood perfusion due to the vasa vasorum
E Gerardo Mendizabal-Ruiz and Ioannis A Kakadiaris
The inflammation and disruption of coronary atherosclerotic plaques is the primary
cause of acute coronary events such as heart attacks. Several studies have shown that
the proliferation of the vasa vasorum (VV) in atherosclerotic plaques is strongly
correlated with the degree of plaque inflammation, and it is related to the processes
that lead to plaque destabilization and rupture. Intravascular ultrasound (IVUS) is a
catheter-based medical imaging system that is capable of providing cross-sectional
images of arteries. Contrast agents are injected into the bloodstream during IVUS
interventions to trace the blood flow and find any evidence of extra-luminal
perfusion, which may be an indication of the VV. The detection of extra-luminal
perfusion is performed by comparing the echogenicity of localized regions of a vessel
wall and plaque before and after the injection of contrast agents. However, manually
performing temporal analysis of variations in the echogenicity is not feasible for
more than a handful of IVUS frames due to the amount of labor and concentration
involved in assessing changes that may be subtle to the human eye. Computer-aided
techniques may be a natural solution to this problem, although they present their
own difficulties, which include overcoming the variety of motion artifacts present in
IVUS sequences. In this chapter, we present an imaging protocol for contrast
imaging in IVUS along with the computational techniques for image stabilization,
which allow the detection and localization of changes in echogenicity by differential
imaging. We present results on five human cases and four animal cases for which
histology was available. These studies illustrate the method’s ability to quantify VV
density in vivo.
doi:10.1088/2053-2563/ab01fach8 8-1 ª IOP Publishing Ltd 2019
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