=Paper= {{Paper |id=Vol-2962/paper01 |storemode=property |title=Brightness Levels in MRI Should Corresponds With Echogenicity Grade in Ultrasound B-images: A Pilot Study of Reproducibility Using ROI-based Measurement Between Two Blind Observers |pdfUrl=https://ceur-ws.org/Vol-2962/paper01.pdf |volume=Vol-2962 |authors=Jiří Blahuta,Tomáš Soukup |dblpUrl=https://dblp.org/rec/conf/itat/BlahutaS21 }} ==Brightness Levels in MRI Should Corresponds With Echogenicity Grade in Ultrasound B-images: A Pilot Study of Reproducibility Using ROI-based Measurement Between Two Blind Observers== https://ceur-ws.org/Vol-2962/paper01.pdf

Brightness Levels in MRI Should Correspond With Echogenicity Grade in
Ultrasound B-MODE images: A Pilot Study of Reproducibility Using
ROI-based Measurement Between Two Blind Observers

Jiří Blahuta, Tomáš Soukup

Silesian University in Opava, The Department of Computer Science, Opava, Czech Republic

Abstract: In 2011, we developed a software tool to anal- 1.1 Input MR Images
ysis of the echogenicity level in ultrasound B-MODE im-
In this study, we have three sets of T1 and T2 MR images
ages. This software is based on binary thresholding in a
(two basic types of MR images) [6] with different image
predefined Region of Interest (ROI).
resolution to analyze using the same approach as for ul-
The goal of this paper is to observe if the echogenic-
trasound images; using echogenicity index as a feature to
ity grade in B-MODE images corresponds with bright-
distinguish different brightness level. In comparison with
ness level in MR images using the echogenicity index.
ultrasound B-MODE images which we have used in previ-
Achieved results obtained by two, non-experienced ob-
ous studies, there is no native scale how to select a window
servers in radiology, shows the software can be used also
50 × 50 mm so we use the full width of the image, see Fig.
for MRI images. The reproducibility of the measurement
1.
evinces the high level of agreement.
We use three ROI areas for which the exact position in
MR image is not important at this moment. Totally of 52
images were analyzed.
Achieved results show the error between measurements
by two non-experienced observers does not exceed 5 %;
calculated based on the range of the measurements and
computed average difference for each image set. Thus,
the echogenicity index can be considered as reproducible
marker; a small shift of the ROI does not evince signifi-
cant change. Average range of the index is computed from
28.17 up to 67.95; minimal index value was < 20 and the
highest value was 101.2 due to different brightness level in
the examined ROI. The range for the same ROI are almost
equal, the difference does not exceed 2 %.

1 Motivation and Input Data
Our software has been developed for ultrasound B-
imaging [1] in neurology to detect hyperechogenicity of
the substantia nigra [2], [3] which is probably one of the
most common markers of Parkinson’s Disease detectable Figure 1: Input MR image with selected ROI
on transcranial (TCS) ultrasound B-MODE images. The
principle of the core algorithm based on binary threshold-
ing enables to load not only ultrasound images. Thus, MR
images could be also analyzed using this software tool. 1.2 Methodology of the Analysis
Clinical studies were published since 2014. The core of We analyze three ROI areas with different size and shape,
the software was improved, especially new ROI areas for see Fig. 2. For each image, all three ROI areas are placed
different diagnoses. in the same position in the image. In other words, each
In modern neurology and neurosurgery, MRI is one of image is analyzed thrice using three different ROI in the
the most progressive medical imaging for all perioperative same position.
phases [5]. MRI and diagnostic ultrasound are commonly The size and shape of the ROI were defined in the past
considered as complementary diagnostic modalities; also for B-MODE images. Originally, ROI1 was used for ncl.
for diagnosis confirmation. raphe analysis and ROI2 has been defined for substantia
Copyright ©2021 for this paper by its authors. Use permitted under nigra area; both in B-MODE images. Square-shaped ROI3
Creative Commons License Attribution 4.0 International (CC BY 4.0). has been used to analyze medial temporal lobe (MTL) in
different case; in measurement of the black/white pixel ra- images to judge general reproducibility between two non-
tio in the ROI 20 × 20 mm to judge a probability of MTL experienced observers.
atrophy as a marker for the dementia [7]. An example of achieved results for a selected image set
of 14 images, is stated in Table 1.

Table 1: Achieved differences of the echogenicity index
between 2 observers
ROI1 ROI1 ROI2 ROI2 ROI3 ROI3 diffROI1 diffROI2 diffROI3
86.99 85.92 71.45 72.22 57.55 58.36 1.07 -0.76 -0.81
38.69 33.97 26.12 24.10 22.08 21.14 4.72 2.02 0.94
55.71 62.77 50.43 53.18 41.61 45.33 -7.06 -2,75 -3.72
75.47 81.18 46.94 48.24 38.19 36.12 -5.71 -1.30 2.07
67.19 70.42 55.83 52.06 39.30 41.52 -3.23 3.77 -2.22
71.32 73.93 58.18 60.54 44.98 44.63 -2.61 -2,36 0.35
73.87 73.95 46.92 48.63 47.53 48.08 -0.07 -1,71 -0.55
83.47 84.27 58.22 58.45 52.00 51.06 -0.80 -0.23 0.94
74.30 74.85 47.44 47.33 53.23 54.15 -0.55 0.11 -0.92
79.27 82.01 52.18 53.58 51.89 52.36 -2.74 -1.40 -0.47
74.79 74.67 51.69 49.54 47.16 48.23 0.12 2,15 -1.07
73.57 70.13 45.88 42.32 40.60 40.22 3.44 3.56 0.38
69.43 62.74 45.01 45.98 42.20 41.78 6.69 -0.97 0.42
66.92 58.35 48.78 50.55 45.07 47.17 8.57 -1.77 -2.10
0.13 -0.12 -0.48

From achieved results we can judge the echogenicity
index could be well-applicable in general as a feature in
MRI analysis.
In Table 2 you can see the average differences for each
ROI in four image sets. It is closely related to judge level
Figure 2: Three different ROI in the same position are used of agreement which is almost perfect.

Table 2: Average computed differences of the echogenic-
ity index between 2 observers
2 Echogenicity Index Evaluation in MR image set / avg difference ROI1 ROI2 ROI3
Images SET 1 1.00 -1.00 -0.40
SET 2 0.13 -0.12 -0.48
In the case of B-MODE images, the echogenicity index SET 3 0.89 -0.90 0.21
should corresponds with echogenicity grade of the tissue. SET 4 -0.16 0.64 0.52
We can use the same index for MR images, in which the
index should corresponds with brightness level of the ex- The data in Table 2 shows that the differences between
amined part inside the ROI. The index is one numerical observers are minimal in the case of the same position
value computed using our software. of the ROI including a small shift. It seems that small
More information about the methodology, see [8]; the ROI position changes which are not recognizable visually,
paper focused on atherosclerotic plaques in B-MODE im- have no significant influence on resulted echogenicity in-
ages, in which we have defined the index and its purpose. dex. From achieved results, the range of the index is from
Simply, the index is computed as one number which can 28.17 up to 38.89; very similar for each image set. Ac-
describe visual brightness level (echogenicity grade in US cording to the range and computed average differences be-
imaging). Our software is based on computing the area of tween the observers, the difference between observers is
remaining pixels after binary thresholding in the ROI. Let smaller than 5 %.
we have 256 intensity levels Hi where i = 0, 1, ..., 255, the
area is computed for each level. After that, the all com-
puted areas are summed and the sum is divided by 100 to 2.1 Echogenicity Grade in US imaging vs MRI
obtain the index given by
In the case of US imaging, the image can be adjusted
dynamically during examination by ultrasound probe set-
∑255
H=0 AH
ECHOINDEX = (1) tings; we can increase or decrease the brightness level
100 according to examined tissue density. The echogenicity
Due to the principle of binary thresholding, for lower grade displayed on acquired digitized image can be differ-
echogenicity grade, the Echo-Index should be lower and ent visually for the same tissue density. Due to this fact we
for higher echogenicity the Echo-Index should be higher. need to analyze the image sets with same probe (image)
This is an assumption which proceeds from the principle settings to avoid incorrect echogenicity evaluation. See
of binary thresholding. We have used the index in MR Fig. 3 in which three TCS B-MODE images with different
global brightness level and corresponding histogram pro-
files are shown.

Figure 3: Three TCS B-MODE images with different
brightness global levels and the histogram profile

In MRI, the settings of the MR machine are deter-
mined by the manufacturer. All image enhancements are
set in post-processing phase in digitized MR images, like
histogram equalization using different algorithms (LHE,
GHE) [9]. In Fig. 5 you can see the example of three
MR images which are different by visual assessment but
the histogram is very similar. So, MR images should be
considered as more stable from the point of brightness set-
tings.
The brightness settings should not be affected by set-
tings during examination but there is other limitation in
MR images corresponding with ROI selection in MR
slices, see the following chapter.

2.2 A Limitation of the Echogenicity Index
Evaluation in MRI
Although it seems the echogenicity index is well repro-
ducible, there is one important limitation. In Fig. 5, there
is the example of using the same ROI size and shape to
select a structure (it is not important from medical point of
view at this moment). Due to weighted MRI, the exam-
ined structure can be smaller, larger, deformed or may not Figure 4: Four MR images in which the histogram is very
visible. In the case of ultrasound B-MODE images, like similar
the substantia nigra in TCS images, the position and size
is determined; only echogenicity grade is different corre-
sponding the gain settings, angle, etc.
In this case, totally different echogenicity grade can
be obtained for the same patient but in different MR im-
age. Thus, another ROI types will be defined in the future
which should be better adapted for different MR images to
examine structures. This limitation is also a barrier for au-
tomatic ROI selection discussed in the following chapter.

3 Possibility of Automatic Finding
Closed ROI of Examined Area Using
Figure 5: An equal ROI type is used for the structure
Convolutional Neural Network
which is different in MR images
In our previous study dedicated to atherosclerotic plaques
analysis in B-MODE images, we also have discussed the
possibility to automatic learning of the plaque detection
using ANN [10] and also a possibility to create a decision-
making expert system to evaluate the echogenicity as a
risk marker of the plaque [11]. Ultrasound imaging is
widely used in atherosclerosis recognition to early diag-
nosis [12]. We have presented a draft of back-propagation
ANN model to find a closed region of the plaque. In this
field, ANN based on deep learning approach are widely
used. In general, the ANN could be used to place ROI ac-
cording to learning of some structure in MR image like in
Fig. 1. However, the most important barrier is the fact that
examined structure may vary in weighted MR images in
each image due to intensity level [13]. See an example in
Fig. 6 how the weighted MR images are different for the
same examined patient.

Figure 7: The example of active contours for atheroscle-
rotic plaques boundary in B-MODE images

experience. There is difficult to learn any shape and size
of the ROI due to changing weighted MR images.
In this field, there is an interesting inspiration how to de-
velop an automatic segmentation using deep learning ap-
proach in T1 and T2 weighted images [16]. The desired
goal is to train the ANN to extract some features of the ex-
amined structure to place the ROI to the correct position.
To CNN training, the back-propagation algorithm is
used; similarly as in linear feed-forward ANN architec-
Figure 6: Weighted MR images example ture. Input image is represented as a single vector w × h ×
d where w and h represent image resolution and d to be
Thus, it could be hard to apply an automatic recognition color depth, in this case d = 1 (for RGB channels d = 3).
a ROI described by shape or size when is changing in the Each pixel is represented as intensity value in the range of
weighted MR imaging. 0 to 255. CNN uses ReLU (Rectified Linear Unit) activa-
The principle could be based on iterative learning using tion function instead of sigmoid or hyperbolic tangent in
a convolutional neural network (CNN) which uses filter- traditional multi-layer backpropagation networks. In gen-
ing to extract some features to recognize the region. CNN eral, CNN has the following layers and functions:
are designed to work with grid-structured inputs, like 2D
images. There are many advanced techniques using CNN 1. input layer (as a single vector w × h × d)
in medical imaging like in [14].
2. convolutional layer (3 × 3 or 5 × 5 convolutional
masks are commonly used) to extract feature map
3.1 From a Boundary to Learn a Feature
3. activation function like ReLU
In 2020, we presented an idea to automatic segmentation
based on boundary recognition of atherosclerotic plaques 4. pooling (sub-sampling) layer (to reduce dimensional-
in B-MODE images [15]. It could be realized using it- ity of feature maps using MaxPooling algorithm)
erative boundary recognition based on active contour al-
5. fully-connected layer
gorithm boosted with CNN to train corresponding pairs
input-output to learn the rules how to obtain the plaque 6. softmax activation function
border, see Fig. 7 in which the contours are shown and
segmented plaque shapes after 25 iterations. 7. output layer
In the case of MRI, there is a different task. There are
no exact borders to find the ROI. In Fig. 6, the weighted Convolutional layer with ReLU and pooling layer are
MR images are displayed; it could be hard to learn what to designed for feature extraction and the fully-connected
consider as a feature. Let to have a structure in MR image layer with softmax function is used to classification. In
which is probably located equally based on radiologist’s our case, we need to recognize a structure in MR image
which is defined by a radiologist, e.g. in Fig. 5 and/or in
Fig. 10. The process is illustrated in Fig. 9.
Deep learning paradigm is based on learning rules from
inputs and desired outputs. This is main difference from
traditional programming when we have inputs, rules and
we need to create outputs. Deep learning requires large
data amount to efficiency. In comparison with tradi-
tional neural networks and learning, deep learning should
achieve better accuracy related to increasing data amount
[18].

Figure 10: Six MR images with highlighted square-shaped
ROI

this task, deep learning could be applied to help to extract
the features to recognize the structure. The background of
the principle of the image convolution algorithm in CNN
Figure 8: Estimated deep learning accuracy vs. conven- you can find in [17] and also in [18] which is a compre-
tional paradigms hensive guide to deep learning paradigm. In 2021, a pa-
per focused on multi-classification of brain tumors in MRI
In a critical point, depending on data complexity and its using CNN, including deep performance evaluation, has
structure, conventional paradigms could be inefficient due been published [19]. In future, automatic finding of the
to overfitting so the learning rate is low or stopped. ROI could be one of the main goals in our long-term re-
In MR images, we can use deep learning approach to search.
learn the rules to recognize the ROI. In general, deep learn- There are many ways for practical implementation. One
ing is focused on training with pairs input-output from of the most known to be Keras, a high-level modular API
large datasets, e.g. thousands of images. Thus, when we developed for Python programming language using GPU
need to learn a specific structure in MR images, the train- acceleration. More information, code samples (including
ing is based on input-output training set to learn rules, i.e. using for CT scans) are available on Keras.io website.
features, to find an appropriate structure to place a prede-
fined ROI. The idea of deep learning using CNN is illus-
trated in Fig. 9. 4 Conclusions and Using Results in Clinical
Studies

The goal of the paper is to show how to use echogenicity
index, computed with ultrasound B-MODE images, in MR
images. To this purpose, we have analyzed sets of T1 and
T2 MR images. The principle of the analysis is equal as for
B-MODE images. The core of the algorithm is based on
binary thresholding of the images in grayscale. Within this
MR images analysis, the main idea is also applicable in
Figure 9: Deep learning idea with CNN MR images; the higher index value should correlate with
higher brightness intensity and vice versa.
Consider the following six MR images in Fig. 10. Achieved results show the principle of the echogenic-
Consider a task to find the highlighted anatomic structure ity index could be applied for B-MODE images and MR
(square-shaped ROI). It seems, there is really hard to learn images independently. It seems, the echogenicity index
the features of the structure because it is from small to big- is well applicable to observe different brightness in MRI
ger area in which the structure is located. equally as in the case of B-MODE images. The obtained
To effective training and learning the network, a large differences are not significant, but the software is more
set of images is needed to learn how to recognize the struc- sensitive than visual assessment in general.
ture from input-output training set. For example, we can Finally, we can recommend using this methodology in
learn the edge, the brightness difference, the shape, e.g. future clinical studies focused on the analysis of MRI us-
roundness, height/width ratio, etc. and another feature. In ing different ROI shapes and sizes according to examined
structure in MR image. In future, we will use a new ROI Scientific GeoConference, Proceedings SGEM, 2020 pp. 341-
areas, like a circle-shaped and/or free-hand closed area, 348. DOI: 10.5593/sgem2020/2.1/s07.044.
defined by an experienced sonographer. It is related to ex- [9] Senthilkumaran, N., Thimmiaraja, J. Histogram Equaliza-
amined structures in MR images, see Fig. 8 as the exam- tion for Image Enhancement Using MRI Brain Images," 2014
ple. World Congress on Computing and Communication Tech-
In parallel, we are working on analysis of the echogenic- nologies, IEEE, 2014, pp. 80-83, E-ISBN: 978-1-4799-2877-
ity index differences between a light area and a dark area 4, DOI: 10.1109/WCCCT.2014.45.
within the same ROI. [10] Blahuta, J., Soukup, T., Čermák, P. An Expert Sys-
tem Based on Using Artificial Neural Network and Region-
This work was supported by European Union un-
Based Image Processing to Recognition Substantia Nigra and
der European Structural and Investment Funds Opera-
Atherosclerotic Plaques in B-Images: A Prospective Study.
tional Programme Research, Development and Educa- 14th International Work-Conference on Artificial Neural Net-
tion project "Zvýšení kvality vzdělávání na Slezské uni- works, IWANN 2017, Cadiz, Spain, June 14-16, 2017, Pro-
verzitě v Opavě ve vazbě na potřeby Moravskoslezského ceedings, Part I. Lecture Notes in Computer Science 10305,
kraje" CZ.02.2.69/0.0/0.0/18-058/0010238 and project Springer 2017, pp. 236-245.
CZ.02.2.69/0.0/0.0/18-054/0014696 "Rozvoj VaV kapacit [11] Blahuta, J., Soukup, T., Skacel, J. Pilot Design of a Rule-
Slezské univerzity v Opavě, "Rozvoj metod teoretické a Based System and an Artificial Neural Network to Risk Eval-
aplikované informatiky" SGS/11/2019 and the image use uation of Atherosclerotic Plaques in Long-Range Clinical Re-
from grant No.16-28628A. search. ICANN 2018, Lecture Notes in Computer Science
book series (LNCS, volume 11140), Springer, 2018, pp. 90-
100, ISSN: 978-3-030-01420-9.
References [12] Steinl, D.C., Kaufmann B.A. Ultrasound Imaging for Risk
Assessment in Atherosclerosis. Int J Mol Sci. 2015 May;
[1] Blahuta, J., Čermák, P., Soukup, T., Vecerek, M. A repro- 16(5): 9749–9769. DOI: 10.3390/ijms16059749.
ducible method to transcranial B-MODE ultrasound images [13] Ito, S., Shirai, W., Hattori, T. Putaminal hyperintensity on
analysis based on echogenicity evaluation in selectable ROI. T1-weighted MR imaging in patients with the Parkinson vari-
(2014) International Journal of Biology and Biomedical En- ant of multiple system atrophy. AJNR. American Journal of
gineering, Vol. 8, pp. 98-106. ISSN: 1998-4510. Neuroradiology, 2009, 30(4), pp. 689–692. DOI: 10.3174/a-
[2] Blahuta, J., Soukup, T., Jelínková, M., Bártová, P., Čermák, jnr.A1443.
P., Herzig, R., Školoudík, D.:A new program for highly re- [14] Blahuta, J., Soukup, T., Sosík., P. Approach to Automatic
producible automatic evaluation of the substantia nigra from Segmentation of Atherosclerotic Plaque in B-MODE images
transcranial sonographic images. Biomedical Papers Vol. 158 Using Active Contour Algorithm Adapted by Convolutional
Issue 4, pp. 621-627 (2014). Neural Network to Echogenicity Index Computation. ITAT
[3] Školoudík, D., Jelinkova, M., Blahuta, J., Čermák, P., Conference 2020, CEUR Workshop Proceedings, 2020, pp.
Soukup, T., Bártová, P., Langová, K., Herzig, R. Transcra- 223-229, ISSN: 1613-0073.
nial Sonography of the Substantia Nigra: Digital Image Anal- [15] Hoogi, A., Subramaniam, A., Veerapaneni, R., Rubin, L.D.
ysis. American Journal of Neuroradiology Dec 2014, 35 (12) Adaptive Estimation of Active Contour Parameters Using
2273-2278; DOI: 10.3174/ajnr.A4049. Convolutional Neural Networks and Texture Analysis. IEEE
[4] Azar, R.N., Donaldson, C. Ultrasound Imaging (Rad- TRANSACTIONS ON MEDICAL IMAGING, Vol. 36, No.
cases) (1st Edition) Kindle Edition. Thieme, 2014, ASIN: 3, March 2017, pp. 781-791.
B00SRLKPOU. [16] Zhang, F., Breger, A., Cho, K. Ning, L., Westin, C.,
[5] Levin, C.M. Magnetic Resonance Imaging (MRI) in Neu- O’Donnell, L., Pasternak, O. Deep Learning Based Segmenta-
rologic Disorders. 2018, Merck Manual, Available online: tion of Brain Tissue from Diffusion MRI. NeuroImage, 2021,
https://www.merckmanuals.com/professional/neurologic- 233. 117934. DOI: 10.1016/j.neuroimage.2021.117934.
disorders/neurologic-tests-and-procedures/magnetic- [17] O’Shea, K., Nash, R. An Introduction to Convolutional
resonance-imaging-mri-in-neurologic-disorders Neural Networks. 2015, ArXiv e-prints.
[6] Mohan, G., Subashini, M. MRI based medical image analy- [18] Aggarwal, C.C. Convolutional Neural Networks. In:
sis: Survey on brain tumor grade classification. Biomed. Sig- Neural Networks and Deep Learning. Springer, Cham.
nal Process. Control., 2018, Vol. 39, pp. 139-161. Online-ISBN:978-3-319-94463-0. DOI:10.1007/978-3-319-
[7] Blahuta, J., Soukup, T., Pavlík, L. The Black-White Pixels 94463-0-3.
Ratio in Medial Temporal Lobe Brain Structure in Transcra- [19] Irmak, E. Multi-Classification of Brain Tumor MRI Im-
nial B-Images as a Measurable Marker of Alzheimer’s Dis- ages Using Deep Convolutional Neural Network with Fully
ease Probability: The Reproducibility Overview. International Optimized Framework. Iran J Sci Technol Trans Electr Eng
Conference on Software, Telecommunications and Computer (2021). DOI: 10.1007/s40998-021-00426-9.
Networks (SoftCOM), 2020, pp. 1-6, DOI: 10.23919/Soft-
COM50211.2020.9238214.
[8] Blahuta, J., Soukup, T., Pavlík, L. The classification of the
progression of atherosclerotic plaques in B-MODE images
between computer image analysis using echogenicity index
and visual assessment. 20th International Multidisciplinary