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features that were improved the most were the completion, coarseness, and contrast
features. The completion feature of the image was increased by all filters.
Finally, in the last row of Table 2.2, the total score distance, Score_dis_T, for all
feature sets is shown. The best values were obtained by the homo, lsminsc, median,
lsmv, homog, and ad filters.
2.4.3 Texture Analysis: Univariate Statistical Analysis
Table 2.3 shows the results of the Wilcoxon rank sum test, which was performed on
the SGLDM range-of-values features set of Table 2.2 for the 10 despeckle filters.
The test was performed to check if significant differences exist between the features
computed on the 440 original and the 440 despeckled images. Filters that resulted in
the most significant number of features after despeckle filtering as listed in the
“Score” column of Table 2.3 were as follows: lsmv, gf4d, lsminsc, and nldif. The
rest of the filters gave a lower number of significantly different features. Features
that showed a significant difference after filtering were the inverse difference
moment (IDM) angular second moment (ASM), sum of entropy, contrast, correla-
tion, sum of squares variance (SOSV), and sum variance, ΣVar. These features were
mostly affected after despeckle filtering and they were significantly different.
2.4.4 Texture Analysis: kNN Classifier
Table 2.4 shows the percentage of correct classification scores for the kNN classifier
with k = 7 for classifying a subject as asymptomatic or symptomatic. The classifier
was evaluated using the leave-one-out method [46] on 220 asymptomatic and 220
symptomatic images of the original and despeckled images. The percentage of cor-
rect classifications score is given for the feature sets listed in the table and defined at
the bottom of the table. Filters that showed an improvement in classification success
scores compared to those of the original image set were the homo (3%), gf4d (1%),
and lsminsc (1%) filters (see the last row labeled “Average” in Table 2.4).
The feature sets thatbenefitted themost from thedespeckle filtering were theSF,
GLDS, NGTDM, and TEM sets when counting the number of cases in which the
correct classification score was improved. Less improvement was observed for the
FDTA, SFM, SGLDMm, FPS, and SGLDMr feature sets. For the SGLDMr feature
set better results are given for the lsminsc filter with an improvement of 2%. This is
the only filter that showed an improvement for this class of features. For the TEM
feature set the filter lsmv showed the best improvement at 9%, whereas for the FPS
feature set the filter lsminsc gave the best improvement at 5%. The lsminsc filter
showed improvements for theGLDS andNGTDM feature sets, whereas the lsmv fil-
ter showed improvements for the SF and TEM feature sets.
2.4.5 Image Quality Evaluation Metrics
Table 2.5 tabulates the image quality evaluation metrics presented in Section 4.3 for
the 220 asymptomatic and 220 symptomatic ultrasound images between the origi-
nal and the despeckled images, respectively. The best values were obtained for the
nldif, lsmv, and waveltc filters with lower MSE, RMSE, Err3 and Err4 and with
54 Despeckle Filtering in Ultrasound Imaging of the Carotid Artery
higher SNR and PSNR (see bottom of Table 2.5 for definitions). The GAE was 0.00
for all cases, and this can be attributed to the fact that the information between the
original and the despeckled images remains unchanged. Best values for the universal
quality index, Q, and the structural similarity index, SSIN, were obtained for the
lsmv and nldif filters.
2.4 Results 55
Table 2.3 Wilcoxon Rank Sum Test for the SGLDM Range-of-Values Texture Features Applied
on the 440 Ultrasound Images of Carotid Plaque Before and After Despeckle Filtering
Feature Local Statistics Median HF GF HM Diffusion Wavelet Score
lsmv lsminsc wiener median homog gf4d homo ad nldif waveltc
ASM S S NS NS S S NS S S S 7
Contrast SNS NSNS NSSNSNSSNS 3
Correlation S S NS NS NS S NS NS NS NS 3
SOSV S NS NS NS NS S NS NS NS NS 2
IDM S S NS S S S S NS S S 8
SAV NS NS NS NS NS NS NS NS NS NS 0
Var
S S NS NS NS NS NS NS NS NS 2
Entropy
S S NS NS NS S NS NS S S 5
Score 75 01 261143
Key: ASM: angular second moment; SOSV: sum of squares variance; IDM: inverse difference moment; SAV: sum average;
ΣVar: sum variance; HM: homomorphic; HF: homogeneity; GF: geometric; HM: homomorphic. Score: illustrates the num-
ber of S.
At p < 0.05, “S” means the feature is significantly different after filtering and “NS” means the feature is not significantly
different after filtering.
Source: [9].
Table 2.4 Percentage of Correct Classifications Score for the kNN Classifier with k = 7 for the Original and
the Filtered Image Sets
Feature Set
No. of
Features
Original Local Statistics Median HF GF HM Diffusion Wavelet
lsmv lsminsc wiener median homog gf4d homo ad nldif waveltc
SF 559
62 61 61
57
63
59
65 60
52
61
SGLDMm 13 65 63 64 62 63
69 67 68
61
66
63
SGLDMr 13 70 66
72
64 66 65 70 69 64 65 65
GLDS 46463
66
61
69
64
66 72
59 58 62
NGTDM 56463
68
60
69
63
65
57 60 61 62
SFM 4626260625855
65 68
59 56 55
TEM 659
68
52
60
59
66 60 65
53
60 60
FDTA 46463
66
53
68
53 62
73
55 54 62
FPS 25954
64
59 58 59 59 59 52 48 55
Average 63 63
64
60 63 62
64 66
58 58 61
SF: statistical features; SGLDMm: spatial gray-level dependence matrix mean values; SGLDMr: spatial gray-level dependence matrix range
of values; GLDS: gray-level difference statistics; NGTDM: neighborhood gray-tone difference matrix; SFM: statistical feature matrix; TEM:
laws texture energy measures; FDTA: fractal dimension texture analysis; FPS: Fourier power spectrum; HF: homogeneity; GF: geometric;
HM: homomorphic.
Bold values indicate improvement after despeckling.
Source: [9].
2.4.6 Visual Evaluation by Experts
Table 2.6 shows the results of the visual evaluation of the original and despeckled
images made by two experts, a cardiovascular surgeon and a neurovascular special-
ist. The last row of Table 2.6 presents the overall average percentage score assigned
by both experts for each filter.
For the cardiovascular surgeon, the average score showed that the best
despeckle filter was the lsmv filter with a score of 62%, followed by gf4d, median,
homog, and original with scores of 52%, 50%, 45%, and 41%, respectively. For the
neurovascular specialist, the average score showed that the best filter was the gf4d
with a score of 72%, followed by lsmv, original, lsminsc, and median with scores of
71%, 68%, 68%, and 66%, respectively. The overall average percentage score (last
row of Table 2.6) shows that the highest score was given to the lsmv filters (67%),
followed by gf4d (62%), median (58%), and original (54%). It should be empha-
sized that the lsmv despeckle filter is the only filter that was graded with a higher
score than the original by both experts for the asymptomatic and symptomatic
image sets.
56 Despeckle Filtering in Ultrasound Imaging of the Carotid Artery
Table 2.5 Image Quality Evaluation Metrics Computed for the 220 Asymptomatic
and 220 Symptomatic Images
Feature
Set
Local Statistics Median HF GF HM Diffusion Wavelet
lsmv lsminsc wiener median homog gf4d homo ad nldif waveltc
Asymptomatic Images
MSE 13 86 19 131 42 182 758 132 8 11
RMSE 3 9 4 10 6 13 27 11 2 3
Err3 7 17 5 25 14 25 38 21 5 4
Err4 11 26 7 41 24 40 49 32 10 5
GAE 00 0 0 0 00000
SNR 25 17 23 16 21 14 5 14 28 25
PSNR 39 29 36 29 34 27 20 28 41 39
Q 0.83 0.78 0.74 0.84 0.92 0.77 0.28 0.68 0.93 0.65
SSIN 0.97 0.88 0.92 0.94 0.97 0.88 0.43 0.87 0.97 0.9
Symptomatic Images
MSE 33 374 44 169 110 557 1452 374 8 23
RMSE 5 19 6 13 10 23 37 19 3 5
Err3 10 33 9 25 20 43 51 31 5 6
Err4 16 47 11 38 30 63 64 43 7 8
GAE 00 0 0 0 00000
SNR 24 13 22 16 17 12 5 12 29 25
PSNR 34 23 33 26 28 21 17 23 39 36
Q 0.82 0.77 0.7 0.79 0.87 0.75 0.24 0.63 0.87 0.49
SSIN 0.97 0.85 0.89 0.81 0.94 0.85 0.28 0.81 0.97 0.87
MSE: mean square error; RMSE: randomized MSE; Err3, Err4: Minowski metrics; GAE: geometric average
error; SNR: signal-to-noise radio; PSNR: peak SNR; Q: universal quality index; SSIN: structural similarity
index.
Source: [9].
The reader might observe a difference in the scorings between the two vascular
specialists. This is because the cardiovascular surgeon is primarily interested in the
plaque composition and texture evaluation, whereas the neurovascular specialist is
interested in evaluating the degree of stenosis and the lumen diameter in order to
identify the plaque contour. The lsmv and gf4d filters were identified as the best
despeckle filters by both specialists because they improved visual perception with
overall average scores of 67% and 62%, respectively. The filters waveltc and homo
were scored by both specialists with the lowest overall average scores of 28% and
29%, respectively.
By examining the visual results of Figure 2.2, the statistical results of Tables 2.2
to 2.5, and the visual evaluation of Table 2.6, we can conclude that the best filters
are the lsmv and gf4d filters, which can be used for both plaque composition
enhancement and plaque texture analysis, whereas the lsmv, gf4d, and lsminsc fil-
ters are more appropriate for identifying the degree of stenosis and, therefore, may
be used when the primary interest is in outlining the plaque borders.
2.5 Discussion
Despeckle filtering is an important operation in the enhancement of ultrasound
images of the carotid artery, both in the case of texture analysis and in the case of
image quality evaluation and visual evaluation by the experts. In this study 10
despeckle filters were comparatively evaluated on 440 ultrasound images of carotid
artery bifurcations, and the validation results are summarized in Table 2.7.
As given in Table 2.7, the lsmv, lsminsc, and homo filters improved the class
separation between the asymptomatic and the symptomatic classes (see also Table
2.2). The lsmv, lsminsc, and gf4d filters gave a high number of significantly different
features (see Table 2.3). The lsminsc, gf4d, and homo filters provided only a mar-
ginal improvement in the percentage of correct classifications (see Table 2.4). More-
over, the lsmv, nldif, and waveltc filters gave better image quality evaluation results
(see Table 2.5). The lsmv and gf4d filters improved the visual assessment carried out
by the experts (see Table 2.6). It is clearly shown that the lsmv filters gave the best
performance, followed by the lsminsc and gf4d filters (see Table 2.7). The lsmv or
2.5 Discussion 57
Table 2.6 Percentage Scoring for Visual Evaluations by Experts of the Original and Despeckled Images:
50 Asymptomatic (A) and 50 Symptomatic (S)
Experts A/S Original Local Statistics Median HF GF HM Diffusion Wavelet
lsmv Lsminsc median homog gf4d homo nldif waveltc
Cardiovascular surgeon A33 7533 43 47 6119 43 32
S48 4918 57 43 422033 22
Average % 41 62 26 50 45 52 19 38 27
Neurovascular specialist A70 7673 74 63 7923 52 29
S66 6763 58 45 655541 28
Average % 68 71 68 66 54 72 39 47 28
Overall Average % 54 67 47 58 50 62 29 43 28
HF: homogeneity; GF: geometric; HM: homomorphic.
Source: [9].
gf4d filter could be used for despeckling asymptomatic images when the expert is
interested mainly in the plaque composition and texture analysis. The lsmv, gf4d, or
lsmnsc filter couldbe usedfor despeckling of symptomatic images when the expert is
interested in identifying the degree of stenosis and the plaque borders. The homo,
nldif, and waveltc filters gave poorer performance.
The lsmv filters gave a very good performance with respect to: (1) preserving the
mean and the median as well as decreasing the variance and the speckle index of the
image; (2) increasing the distance of the texture features between the asymptomatic
and the symptomatic classes; (3) significantly changing the SGLDM range-of-values
texture features after filtering based on the Wilcoxon rank sum test; (4) marginally
improving the classification success rate of the kNN classifier for the classification
of asymptomatic and symptomatic images in the cases of the SF, SMF, and TEM fea-
ture sets; and (5) improving the quality of the image. The lsmv filter, which is a sim-
ple filter, is based on local image statistics. It was first introduced in [10, 15, 16] by
Jong-Sen Lee and coworkers and it was tested on a few SAR images with satisfactory
results. It was also used for SAR imaging in [14] and image restoration in [17], again
with satisfactory results.
The lsminsc filter gave the best performance with respect to: (1) preserving the
mean, as well as decreasing the variance and the speckle index and increasing the
contrast of the image; (2) increasing the distance of the texture features between the
asymptomatic and the symptomatic classes; (3) significantly changing the SGLDM
texture features after filtering based on the Wilcoxon rank sum test; and (4) improv-
ing the classification success rate of the kNN classifier for the classification of
asymptomatic and symptomatic images in the cases of the SF, SGLDMr, GLDS,
NGTDM, FDTA and FPS feature sets.
The lsminsc filter was originally introduced by Nagao in [34] and was tested on
an artificial and a SAR image with satisfactory performance. In this study the filter
was modified by using the speckle index instead of the variance value for each
subwindow [as described in (2.9) in Section 2.2.1.2].
58 Despeckle Filtering in Ultrasound Imaging of the Carotid Artery
Table 2.7 Summary of Despeckle Filtering Findings in Ultrasound Imaging of the Carotid Artery
Despeckle Filter
Statistical and
Texture
Features
(Table 2.2)
Statistical
Analysis
(Table 2.3)
kNN
Classifier
(Table 2.4)
Image
Quality
Evaluation
(Table 2.5)
Optical
Perception
Evaluation
(Table 2.6)
Local Statistics
lsmv üü üü
lsminsc üüü
Geometric Filtering
gf4d üü ü
Homomorphic Filtering
homo üü
Diffusion Filtering
nldif ü
Wavelet Filtering
waveltc ü
Source: [9].
The gf4d filter gave a very good performance with respect to: (1) decreasing the
speckle index; (2) marginally increasing the distance of the texture features between
the asymptomatic and the symptomatic classes; (3) significantly changing the
SGLDM range-of-values texture features after filtering based on the Wilcoxon rank
sum test; and (4) improving the classification success rate of the kNN classifier for
the classification of asymptomatic and symptomatic images in the cases of the
SGLDMm, GLDS, NGTDM, SFM, and TEM feature sets. The geometric filter gf4d
was introduced by Crimmins [11], and was tested visually on a few SAR images
with satisfactory results.
Filters used for speckle reduction in ultrasound imaging by other investigators
include median [35] wiener [14], homog [8], homo [18, 19], ad [5], and waveltc [30,
51]. However, these filters were evaluated on a small number of images, and their
performance was tested based only on the mean, median, standard deviation, and
speckle index of the image before and after filtering.
The median and wiener filters were originally used by many researchers for sup-
pressing the additive and later for suppressing the multiplicative noise in different
types of images [2–10, 14, 35]. The results of this study showed that the wiener and
median filters were not able to remove the speckle noise and producedblurred edges
in the filtered image (see Figure 2.2). In this study the median filter performed
poorer as shown in Tables 2.2, 2.3, and 2.4.
The homog [8] and homo [2, 18, 19] filters, were recently used by some
researchers for speckle reduction but our results in Tables 2.2, 2.3, and 2.5 and the
visual evaluation of the experts in Table 2.6 showed poor performance, especially
for the homo filter.
Anisotropic diffusion is an efficient nonlinear technique for simultaneously per-
forming contrast enhancement and noise reduction. It smooths homogeneous image
regions but retains image edges [24]. Anisotropic diffusion filters usually require
many iteration steps compared with the local statistic filters. In a recent study [5],
speckle-reducing anisotropic diffusion filtering was proposed as the most appropri-
ate filter for ultrasound images of the carotid artery. However, in this study, ad,as
shown in Tables 2.2, 2.3, 2.4, 2.5, and 2.6 did not perform as well as the lsmv, gf4d
and lsminsc filters.
Furthermore, wavelet filtering proposed by Donoho in [30], was investigated
for suppressing the speckle noise in SAR images [16, 37], real-world images [26],
and ultrasound images [27] with favorable results. This study showed that the
waveltc filter gave poorer performance for removing the speckle noise from the
ultrasound images of the carotid artery (Tables 2.2 and 2.3).
Anisotropic diffusion and wavelet filtering were also investigated in [32] on
carotid artery ultrasound images, where a simple preprocessing procedure based on
spectrum equalization and a nonlinear shrinkage procedure was applied, in order to
suppress the spiky component to the logarithmic transformed speckle and to modify
the noise component to approximately white Gaussian. Then anisotropic diffusion
and wavelet filtering, which are based on an additive noise model, were applied.
The preprocessing procedure does not modify the structures of a specific filtering
method, but rather alters the noise in such a way that it becomes very similar to
white Gaussian, so that existing powerful methods for additive noise reduction may
be applied. A wavelet multiscale normalized modulus-based wavelet diffusion
2.5 Discussion 59
method was also recently proposed in [51] that utilizes the properties of the wavelet
and the edge enhancement feature of the nonlinear diffusion. The performance of
this algorithm was tested on cardiac ultrasound images and showed superior perfor-
mance when compared to the speckle-reducing anisotropic method [5]. In [52] the
effect of despeckle filtering on lossy compression was investigated, where compres-
sion and then despeckle filtering were applied on ultrasound images of a renal cell
carcinoma. The images were simultaneously denoised and compressed in a single
step, and it was shown that the Laplacian distribution compression scheme
performed better.
In conclusion, despeckle filtering is an important operation in the enhancement
of ultrasonic imaging of the carotid artery. In this study it was shown that simple fil-
ters based on local statistics (lsmv and lsminsc) and geometric filtering (gf4d) could
be used successfully for the processing of these images. In this context, despeckle fil-
tering can be used as a preprocessing step for the automated segmentation of the
IMT [53] and the carotid plaque, followed by the carotid plaque texture analysis,
and classification. This field is currently being investigated by our group [54]. Initial
findings show promising results; however, further work is required to evaluate the
performance of the suggested despeckle filters at a larger scale as well as their impact
in clinical practice. In addition, the usefulness of the proposed despeckle filters in
portable ultrasound systems and in wireless telemedicine systems still has to be
investigated.
Acknowledgments
This work was partly funded through the Integrated System for the Support of the
Diagnosis for the Risk of Stroke (IASIS) project of the Fifth Annual Program for the
Financing of Research of the Research Promotion Foundation of Cyprus
2002–2005, as well as, through the Integrated System for the Evaluation of Ultra-
sound Imaging of the Carotid Artery (TALOS) project of the Program for Research
and Technological Development 2003–2005 of the Research Promotion Founda-
tion of Cyprus.
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