ACCURACY
OF PROSTATE VOLUME MEASUREMENT ON 2D TRANSABDOMINAL USG MODALITY USING THE
GRADIENT VECTOR FLOW (GVF) SEGMENTATION APPLICATION MEASUREMENT TECHNIQUE
Any Maryani1, M.Choiroel Anwar2, Bagus Abimanyu3
Politeknik Kesehatan Kemenkes Semarang, Central Java, Indonesia
anymaryanimid@gmail.com
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KEYWORDS |
ABSTRACT |
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Keywords: Prostate Volume, Transabdominal Ultrasound, Gradient
Vector Flow (GVF), Ultrasound Image |
In the
field of radiology, supporting examinations to evaluate the prostate include
examination with X-rays, Magnetic Resonance Imaging (MRI), and
ultrasonography (USG).
Of all imaging examinations that
have been carried out throughout the world, it is estimated that around 25%
use ultrasound.
The general objective of this
research is to analyze the design of the Gradient Vector Flow (GVF)
segmentation algorithm on 2D ultrasound images to measure prostate volume in
the adult age group. This study
created a Gradient Vector Flow (GVF) segmentation application for the
prostate organ to calculate the prostate volume calculation. RnD research
with an experimental approach was carried out by creating a Gradient Vector
Flow (GVF) segmentation design application, tested on 15 DICOM-based
ultrasound images, measuring and calculating prostate volume using caliper
measurement on ultrasound equipment and Gradient Vector Flow segmentation
application. Measurements were made by an expert (sonographer) as an observer
with data analysis using SPSS statistics. Analysis of ultrasound image of
prostate volume using caliper measurement technique and Gradient Vector Flow
(GVF) segmentation is similar to a p-value of 0.950 (> 0.05). Measurement
with caliper measurements has similarities shown by the mean rank value of
Gradient Vector Flow (GVF) of 15.60 with a mean rank value of caliper measurement
of 15.40. GVF application measurement on transabdominal ultrasound prostate
volume measurement is similar to caliper measurement and can be used to get
more accurate results on objects with less clear edge characteristics.
However, the caliper measurement method can be used more efficiently. |
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DOI: 10.58860/ijsh.v2i10.107 |
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Corresponding Author: Any Maryani
Email: anymaryanimid@gmail.com
INTRODUCTION
In the field of
radiology, supporting examinations to evaluate the prostate include examination
with X-rays, Magnetic Resonance Imaging (MRI), and ultrasonography (USG) (Soeprijanto,
2017). Of all imaging examinations that
have been carried out throughout the world, it is estimated that around 25% use
ultrasound (Alam,
2021). This is because using ultrasound
modality has several advantages, including ultrasound examination is a
non-invasive examination (does not use needles or injections). Ultrasound is a
safe examination, the equipment is cheap, and the imaging results are fast.
Ultrasound examination is painless and easily tolerated by most patients.
Ultrasound is widely available, easy to use, and costs less than the MRI
imaging method. Also, imaging with ultrasound modality is very safe and does
not use ionizing radiation, which is harmful to the body compared to X-ray (Damanik
et al., 2021). This is what causes examination of
the prostate organ using ultrasound modality to become more popular compared to
MRI. Ultrasound has several other advantages; apart from being low cost and
easy to use, ultrasound can also demonstrate the same capabilities as other
modalities, such as MRI and CT, in differentiating tissue class levels (Alam,
2021).
Ultrasound examination of
the prostate is usually performed to evaluate the lower abdominal organs
(transabdominal ultrasound). It is quite well-accepted as the first modality to
show the size of the prostate in various conditions (Djojodimedjo
& Sigumonrong, 2016). A prostate ultrasound usually uses
various inspection techniques and acoustic windows to increase visibility and
improve image quality. In the ultrasound image, the echo pattern structure of
the prostate appears more echogenic compared to the texture of the urinary
bladder.
Based on the Ministry of
Health (2019), the age division in adulthood is divided starting from the age
of 26-35 years, which is referred to as early adulthood; the age of 36-45 years
is referred to as late adulthood, the early elderly period begins at the age of
46-55 years, the late elderly period is in the age range 56-65 years, and old
age begins at the age of 65 years and above. The overall prevalence of an
increase in prostate volume size is 10.3%, with an overall annual incidence
rate of 15 per 1000 person-years, increasing with age (3 per 1000 at age 45-49
years, to 38 per 1000 at age 75-79 years). For men without symptoms at the age
of 46 years, it is 45%. It is important to know that Lower Urinary Tract
Syndrome LUTS can present without complaint and can be caused by variations in
sympathetic nerve stimulation of prostatic smooth muscle, variability in
prostate anatomy, and variable effects on bladder physiology of obstruction and
aging. (Lawrentschuk
et al., 2021).
The enlargement of the
prostate volume that occurs in patients examined using ultrasound modality can
be caused by various pathological causes. BPH (Benign Prostate Hyperplasia) is almost ubiquitous in
older men, increasing from age 40-45 years, reaching 60% at age 60 and 80% at
age 80 (Lerner et al., 2021). (Lerner
et al., 2021)can cause benign prostate enlargement
and urinary tract obstruction. Apart from that, the prostate ultrasound image's
shape looks irregular due to obstruction in the urinary tract (Foo,
2017). This can lead to inaccuracy in
measuring organ volume, resulting in inaccurate decisions in selecting medical
treatment. This is the main basis for conducting research (Rudiansyah,
2018).
This research has
benefits that can be described as follows: In terms of theoretical benefits,
this research will contribute to increasing the insight and knowledge of
readers in general, as well as the author himself in particular, especially in
the application of the Gradient Vector Flow (GVF) segmentation technique to
images of the prostate organ with transabdominal 2D ultrasound modality. This
research will also be a useful reference for the Semarang Health Polytechnic
academic community, especially the Diagnostic Imaging Department, in developing
digital image processing methods.
In terms of practical
benefits, this research will help understand the effectiveness of the design
and use of Gradient Vector Flow (GVF) on prostate ultrasound image quality in a
group of adult men. In addition, the results of this study will provide
valuable guidance for radiographers and sonographers in determining prostate
examination techniques and can be implemented as a prostate ultrasound scanning
examination protocol in the next group of adult men. Thus, this research has
theoretical value and significant practical implications in the world of health
examinations.
This research has the
objectives detailed as follows: The general objective of this research is to
analyze the design of the Gradient Vector Flow (GVF) segmentation algorithm on
2D ultrasound images to measure prostate volume in the adult age group. Apart from
that, this research has more specific objectives: The first objective is to
develop a Gradient Vector Flow (GVF) segmentation algorithm design on 2D
ultrasound images to calculate prostate volume in the adult age group. The
second objective was to measure prostate volume in a group of adult men using a
2D ultrasound modality by applying the GVF algorithm. The third objective was
to compare the results of prostate volume measurements in a group of adult men
using 2D imaging modalities with the results obtained using the GVF snakes
segmentation device.
METHOD
This research is a Research and
Development study (R&D) that focuses on creating and testing designs for
GVF (Gradient et al.) based segmentation program applications to quickly and
precisely calculate prostate volume. This R&D research design is expected to produce products and test their performance, in
this case in the form of a Gradient Vector Flow (GVF) segmentation application
in the prostate organ. The data collection method is carried out directly (prospectively ),
and the prostate image is taken from data on the ultrasound machine in the form
of DICOM data. The population in this study was
ultrasound images of the prostate organ MMN Hospital Bekasi and PT MMS clinic, whereas
population affordable is the
entire population target Which fulfills criteria inclusion.Research uses a numerical measurement scale
in one paired group. It is said to be paired because the data is measured twice
on the same individual or sample. The sample selection method in this research
is included in the sample selection group non-probability sampling,
using a convenient sampling technique where the researcher takes samples research based on existing sampling with objective considerations and practically appropriate desired research objectives.
RESULTS AND DISCUSSION
This study's results show
similarities in measuring prostate volume using the manual caliper measurement
technique with the Gradient Vector Flow (GVF) segmentation technique using
lower abdominal ultrasound patient subjects. This research was conducted on
DICOM ultrasound images from June to July 2023 at hospitals and clinics in
Bekasi, and then the prostate volume was measured using the manual caliper
measurement technique on the Mindray 2D transabdominal ultrasound modality. On
the same image, measurements were also carried out using the Gradient Vector
Flow (GVF) segmentation technique with Mathlab as the software using 15 lower
abdominal ultrasound patient subjects. Image taking and measurements were
carried out by three experts (sonographers) as observers who worked in the
Radiology Unit for nine years, five years, and three years, respectively. Data
collection by observers on patients undergoing lower abdominal ultrasound using
routine protocols without modification, including taking transverse/ axial,
longitudinal/sagittal, and craniocaudal images. For the measurement method with
the application, transverse scanning is made in 1 image plane, and two image
cuts follow the scanning movement of the prostate organ for image segmentation
purposes.
The image segmentation process is a
stage in image analysis where the main goal is to separate or partition images
into groups with the same characteristics or attributes. In this context,
segmentation is carried out to separate certain objects of interest from the
background or other objects in the image.
This automatic segmentation process
of ultrasound images has its uniqueness in its creation, and this can be caused
by, among other things, because ultrasound images produce a lower contrast
appearance when compared to X-ray, CT-Scan, and MRI images. Other challenges
include artifacts in ultrasound images, low gray level variations, and unclear
object contours. In some literature, it is also stated that the ultrasound
modality is operator-dependent, meaning that anyone who operates the ultrasound
device needs to have a good mastery of scanning techniques, anatomy, and
pathology. To display a good ultrasound image of the prostate organ requires
good patient preparation (full blast) and focus parameters, proper depth, and
dynamic range.
The author has not found any
research on the topic of ultrasound image volume segmentation; the only
research available is the segmentation method aimed at determining the area of
the kidney organ, liver mass, knowing the flow of blood vessels, and the area
of the prostate organ with a transrectal transducer.
Gradient Vector Flow (GVF) is a
segmentation method used in image processing to identify object boundaries or
areas with high contrast. This method is based on a gradient vector flow
generated from the image.
The following are some important points
regarding GVF segmentation.
1. GVF Segmentation applies the GVF
segmentation method to prostate ultrasound images to produce precise contours
or boundaries. This segmentation helps separate the prostate area from the
surrounding structures or tissues.
2. Region-of-Interest (ROI): Select a relevant
area or region-of-interest (ROI) that covers the entire prostate in the image
segmented with GVF.
3. Gradient Vector Flow: GVF generates a
gradient vector flow using gradient information from the image. This flow
describes the direction in which the image intensity changes rapidly. This
helps determine the boundaries of objects in the image that may be less clear
or disturbed by other factors such as noise or texture.
4. Solving Differential Equations: The GVF
method involves solving partial differential equations to produce a gradient
vector flow. In this process, the gradient vector is obtained by offsetting the
local components and the non-linear integral of the gradient vector.
5. External Energy: GVF utilizes external
energy associated with the line or contour of interest. This energy can be
determined based on gradient strength measurements or other methods, such as
feature mapping.
6. Pulling and Blocking: GVF gradient vector
flow functions to pull contours into areas of high intensity and maintain the
relative distance between contours so that objects remain well segmented. An
energy barrier can sometimes prevent the contour from moving into undesired
areas.
7. Optimization: GVF applies an optimization
process to produce accurate segmentation results. This method involves
iteration to update the flow gradient vector based on external energy. Changes
in the flow gradient vector are calculated until convergence to achieve stable
segmentation.
The results of descriptive statistical
tests shown by SPSS show that in the group of ultrasound images measured using
caliper measurements, the average value was 14.8813, while in the Gradient
Vector Flow (GVF) segmentation application, it was 15.1375. The middle value
(median) in the ultrasound image group with caliper measurements was 13.4800,
while in the Gradient Vector Flow (GVF) segmentation application group, it was
13.7500. The standard deviation obtained in the ultrasound image group with
caliper measurements was 38.859, while in the Gradient Vector Flow (GVF)
segmentation application group, it was 42.376.
From the caliper measurement data,
it can be stated that the lowest prostate volume value in the caliper
measurement was in the subject with the initials NN8 with a volume value of
9.7900; The highest score was in the subject with the initials NN5 with a
volume value of 22.3400.
From the data above, it can be
stated that the lowest prostate volume value in the Gradient Vector Flow (GVF)
segmentation method measurement was in the subject with the initials NN8 with a
volume value of 9.7951; The highest scores were for subjects with the initials
NN5 and NN6 with a volume value of 23.1664.
Based on
research a normal prostate in adults is
under 20 cm3. Pathology Prostate hyperplasia increases incidence by 20% in men
over 40, reaching 50% in men over 50 and 70% in men over 60 (Biddulth, n.d.). According to the data above, patient subjects with the initials
NN5 NN6 have above-average prostate volumes and are over 40 years old. NN5 is
53 years old, and NN6 is 70 years old.
The criteria for taking a good
transabdominal ultrasound image of the prostate is if the urinary bladder is
filled with approximately 400ml of water (Biddulth, n.d.). To get prostate volume measurement results using caliper
measurement: 1) select the distance tool in the measure view menu and start measuring by how to draw a vertical line
from one edge to another in the area containing information on the prostate
organ to obtain the dist1 size (ap diameter or p dimension); 2) then draw a
straight horizontal line from one edge to the other to obtain the size dist2
(lateral diameter or dimension l); 3) move to the layout on the right which
displays the results of longitudinal scanning, select the distance tool in the
measure view menu and measure by drawing a straight line from one edge to the
other obliquely in the area containing prostate volume information so that the
size dist3 (diameter) is obtained. Craniocaudal or t dimension); 4) The
computer automatically calculates the resulting dimensions of length x width x
height on the ultrasound machine multiplied by the constant п/6 or 0.52 to
obtain the total volume of the prostate organ. This is by research where organ
volume calculations in ultrasound are calculated using an ellipsoid formula or
a volume-based measurement.
The steps for measuring and calculating
prostate volume using the Gradient Vector Flow (GVF) segmentation technique
begin with 1) Click the select folder tool to select the patient's DICOM data;
2). Select two ultrasound images of the prostate organ with maximum image
information. 3) Copy the image data folder containing the GVF application
software; 4) Open the Matlab application program; 5) Open the GVF snakes
application software; 6) Run the program by clicking change folder towards the
image data previously saved in the folder; 7). Run the ultrasound image; 8).
Make an initiation on the border that shows the prostate organ information. 9).
Click in the middle of the initiation site; 10) The computer will automatically
calculate and calculate prostate volume organ results.
Figure
1 Initial stages of the Matlab file
Figure
2 The second stage of the Gradient Vector Flow (GVF) application
Figure
3 Stage of selecting two images of the prostate organ
Figure
4 Process of initializing the prostate organ image
Figure 5 Stages of creating an ROI to
obtain prostate volume
Figure 6 Volume of images 1 and 2, and the
total of the two images along
with their creation time
the Mann-Whitney test, which was carried
out for information on the group of ultrasound images that were measured with a
caliper and the ultrasound images in the group that was measured with the GVF
application had an assigned value (p-value) > 0.05, shows that there was no
difference in measurements in the two groups. Besides that, the mean rank values obtained in the caliper and
GVF application measurement groups were 15.40 and 15.60, respectively. Mean Rank is the average ranking of each
group used in the Mann-Whitney test context, which compares two independent
samples. The test results above indicate that the method " Application of
the method GVF " has a slightly
higher mean rank than the " Caliper
measurement " method. In the context of the Mann-Whitney test, the mean
rank reflects the average rank of the data in each group being compared. This
shows that statistically, the measurement results in the application have
similar results (Yang et al., 2015).
Based
on the results of the presentation above, it can be concluded that this
research is the same, showing that the measurement of the GVF application, when
compared manually in the form of a caliper which is still used so far, the
results are similar, thus the conclusion obtained is that this GVF
segmentation-based application can be used as an alternative replacement, has
good performance. It is good and similar to the results of calculating prostate
volume on a 2D ultrasound machine.
The
measurement technique using a caliper can get an estimate of prostate volume,
often used in clinical practice. However, this method is based on the
assumption of approximation of the prostate as an ellipsoid or ellipsoid-like
shape. In real conditions, the prostate has a more complex arrangement and
shape and is not symmetrical (matthew-hoffman,
2020). Also, the condition of "human error" could occur in
the process of taking manual measurements of the plot prostate organ; another
possibility besides that is that the ultrasound modality is one of the
modalities that are "operator dependent" (Alamelumangai,
2013). The phrase "operator dependent" is used in the medical
field to describe how much the knowledge, experience, and ability of the
operator performing the examination influences the results or interpretation of
the procedure or examination. "Operator dependency" in the ultrasound
context refers to the operator's ability and skill to perform the examination,
which may impact the accuracy and quality of the ultrasound images and
resulting interpretation (Mamun et al., 2013).
Meanwhile,
calculations using the Gradient Vector Flow (GVF) method will potentially have
higher accuracy in prostate segmentation because it uses a computational
algorithm to identify prostate boundaries more precisely. Meanwhile, the level
of segmentation accuracy is influenced by factors such as image quality,
segmentation setting parameters, and how the prostate is structured in the
image (Zhang et al., 2013).
In
calculating prostate volume using the Gradient Vector Flow (GVF) method, it is
explained that it uses ultrasound image segmentation of the prostate organ to
identify and separate the prostate area from related structures (Li et al., 2016). Ultrasound images generally have diffusion and noise properties,
which can interfere with object segmentation. GVF segmentation can help
overcome this problem by relying on gradient vector flows obtained from images
to identify and maintain precise contours. This helps find object boundaries
that are less clear or disturbed by noise in the ultrasound image. After segmentation,
the prostate volume is calculated based on the number of voxels included in the
prostate segmentation. This method can provide more accurate and detailed
segmentation and a more accurate volume estimate. Appropriate (Jazirian et al., 2023).
If
the purpose of measuring prostate volume is for a rough measurement or as a
general indicator of the prostate organ as a whole, then the measurement method
with a caliper could be a solution, but if the purpose of measuring prostate
volume requires a higher level of accuracy for clinical purposes or more
in-depth research, then this measurement method using the GVF application could
be a solution (Zheng et al., 2018).
In conducting research regarding
the effectiveness of creating automatic segmentation applications using the GVF
method, there are several limitations, some of which are: 1). The research time
carried out by the author was limited by the time adjusted to the graduation
time so that the prostate organ image data set that could be collected was
limited, this had an impact on the amount of image data that was processed. 2)
The amount of image data used. The prostate image is a common image found in
routine whole abdomen ultrasound examinations; however, collecting image data
with the pathology of the prostate, in this case BPH, takes quite a long time.
3). DICOM data is limited. Only ultrasound equipment of certain brands can be
processed in the GVF segmentation application, in this case, Matlab. This is
because the DICOM numbering information can differ for several ultrasound
equipment brands. Not all image formats on the ultrasound machine can be opened
with the Matlab application.
CONCLUSION
Based on the research
results, the effectiveness of volume measurements using 2D ultrasound
techniques with the Gradient Vector Flow (GVF) segmentation application can be
drawn as follows: First, the research results show that there is no significant
difference in the results of calculating prostate volume between manual
measurements and the GVF segmentation method. This indicates that the GVF
segmentation method can produce prostate volume data that is not statistically
significantly different from manual measurements. Second, the GVF segmentation
method applied to prostate ultrasound images can measure prostate volume with
results close to manual measurements using a caliper. In this case, the GVF
segmentation method has proven to be an effective alternative in calculating
prostate volume with high accuracy. Thus, the results of this study indicate
that the use of the GVF segmentation method on 2D ultrasound images can provide
results comparable to manual measurements and can be considered an effective
method for measuring prostate volume accurately.
REFERENCES
Alam, D. N.
(2021). Perbandingan Gambaran
Ultrasonography Gray Scale Dan Doppler Parenkim Hepar Berdasarkan Scoring
System Dengan Pemeriksaan Fibroscan Pada Pasien Hepatitis B Kronik.
Universitas Hasanuddin.
Alamelumangai, N. (2013).
Automated Segmentation Of Breast Cancer Lesions In Ultrasound Images Using
Modified Fuzzy Possibilistic C-Means With Replusions Clustering And Generalized
Gradient Vector Flow Snake Algorithm. Life
Science Journal, 10,
360–367.
Biddulth, B. (N.D.).
Pemilihan Modalitas Pemeriksaan Radiologi Untuk Diagnosis Benign Prostatic
Hyperplasia. Cermin Dunia Kedokteran,
43(6), 469–472.
Damanik, M., Simanjuntak, J.
N. D., & Daulay, E. R. (2021). Studi
Paparan Radiasi Pada Pekerja Radiasi Cathlab Dengan Menggunakan My Dose Mini
Sebagai Upaya Keselamatan Radiasi Di Rsup Adam Malik Medan.
Djojodimedjo, T., &
Sigumonrong, Y. (2016). Ureterokel Dan
Ureter Ektopik.
Foo, K. T. (2017). Pathophysiology
Of Clinical Benign Prostatic Hyperplasia. Asian Journal Of Urology, 4(3),
152–157.
Jazirian, H., Jafarkazemi,
F., & Rabieefar, H. (2023). A Numerical Model For Simulating Separated
Gas-Liquid Two-Phase Flow With Low Gvf In A V-Cone Flowmeter. Flow Measurement And Instrumentation,
90, 102329.
Lawrentschuk, N., Ptasznik,
G., & Ong, S. (2021). Benign Prostate Disorders. Endotext [Internet].
Lerner, L. B., Mcvary, K.
T., Barry, M. J., Bixler, B. R., Dahm, P., Das, A. K., Gandhi, M. C., Kaplan,
S. A., Kohler, T. S., & Martin, L. (2021). Management Of Lower Urinary
Tract Symptoms Attributed To Benign Prostatic Hyperplasia: Aua Guideline Part
Ii—Surgical Evaluation And Treatment. The
Journal Of Urology, 206(4),
818–826.
Li, Q., Deng, T., & Xie,
W. (2016). Active Contours Driven By Divergence Of Gradient Vector Flow. Signal Processing, 120, 185–199.
Https://Doi.Org/Https://Doi.Org/10.1016/J.Sigpro.2015.08.020
Mamun, M., Al-Kadi, M.,
& Marufuzzaman, M. (2013). Effectiveness Of Wavelet Denoising On
Electroencephalogram Signals. Journal
Of Applied Research And Technology, 11(1), 156–160.
Matthew-Hoffman, M. (2020).
Picture Of The Prostate. Https://Www.Webmd.Com/Men/Picture-Of-The-Prostate.
Rudiansyah, M. (2018).
Pengukuran Volume Pendarahan Otak Pasien Stroke Hemoragik Intraserebral Hasil
Multi Slice Ct Scan (Msct) Menggunakan Gradient Vector Flow (Gvf). Institut Teknologi Sepuluh Nopember.
Soeprijanto, B. (2017). Inovasi Radiologi Di Era Molekuler Dan
Digital: Disampaikan Pada Pengukuhan Jabatan Guru Besar Dalam Bidang Radiologi
Pada Fakultas Kedokteran Universitas Airlangga Di Surabaya Pada Hari Sabtu.
Tanggal 8 Juli 2017.
Yang, S.-C., Yu, C.-Y., Lin,
C.-J., Lin, H.-Y., & Lin, C.-Y. (2015). Reconstruction Of Three-Dimensional
Breast-Tumor Model Using Multispectral Gradient Vector Flow Snake Method. Journal Of Applied Research And Technology,
13(2), 279–290.
Zhang, Y., Zhou, Y., Yang,
X., Tang, P., Qiu, Q., Liang, Y., & Jiang, J. (2013). Thin Slice Three
Dimentional (3d) Reconstruction Versus Ct 3d Reconstruction Of Human Breast
Cancer. The Indian Journal Of Medical
Research, 137(1), 57.
Zheng, X., Sun, X., &
Bai, B. (2018). Flow Rate Measurement Of Low Gvf Gas-Liquid Two-Phase Flow With
A V-Cone Meter. Experimental Thermal
And Fluid Science, 91,
175–183.
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