A list of responses to the comments
Manuscript Number: PONE-D-21-13529
Manuscript Title:“A weighted region-based level set method for image segmentation
with intensity inhomogeneity”
Dear Reviewers and Editor,
We totally agree with the valuable comments, which is a great help not only to improve
the quality of this manuscript but also to instruct our research in future! We also
thank editor for giving us an opportunity to revise the manuscript!
According to reviewers’ comments, we have made a careful revision. In this response
letter, we will try our best to list the details how each section is revised so as
to illustrate the revision clearly.
Sincerely yours,
Haiping Yu, on behalf of the authors
Email: seapingyu@outlook.com
Legend
Black color: reviewers’ comments texts
Black color: original texts in original version
Blue color: our explanation texts to comments
Red color: the modified (or added) texts in revised version
COMMENTS FOR THE AUTHOR:
Reviewer #1: In this paper, an image segmentation method based on weighted region-based
level set method is proposed. It is based on the techniques of local statistical theory,
level set theory and curve evolution.
Thanks very much for kind comment!
Accordingly, based on your following detail comments, we improve the manuscript as
much as possible in revised version one by one as follows.
1- The proposed method is not clear enough, and needs more explanation.
Answer:
Thank you for your valuable comments! Your comments are very instructive and helpful
for our future work.
First, the author apologize that the proposed method is not clear enough!
Second, in revised version, we have carefully improved the quality of the proposed
method.
The detail changes in revised version are as follows:
� In the last paragraph of Introduction：
In this paper, we propose a weighted region-based level set method to segment images
with intensity inhomogeneity. In the model, first, a new weighted pressure force (WPF)
is proposed to adaptively modulate the contractility of the balloon forces inside
and outside of the closed contour by restricting the unevenness of the intensity mean
value inside and outside the closed curve; second, a faster and smoother regularization
term is utilized to ensure the stability of the curve evolution during the process
of solving; finally, the WPF is integrated into the level set framework to improve
the accuracy of segmentation images with intensity inhomogeneity.
� In Previous works, we added a new subsection to elaborate the idea of the proposed
segmentation model by comparing with other standard models.
Summary
In summary, the four segmentation models based on the level set method have made great
contributions to image segmentation with intensity inhomogeneity. As a typical segmentation
model based on the level set, the CV model has the merit of high convergence speed
if segmenting two phase images with intensity homogeneity. The DRLSE model normalizes
the movement of the curve during the curve evolution process and improves the accuracy
of the numerical solution. However, this model has poor performance in segmenting
weak boundaries and high noise images. In the LVC model, the SPF is used to control
the direction of the curve evolution which further improves the speed of iteration.
The model improves the efficiency of the segmentation algorithm. Although this model
is more efficient than the DRLSE model, this model loses its advantages when the image
to be segmented has high intensity inhomogeneity and is less efficient when segmenting
severe high-noise images. The LRFI model improves the efficiency of segmenting noisy
images; however, it does not perform well in segmenting fuzzy boundary images.
Actually, the above segmentation models have their own advantages and disadvantages.
How to make full use of the local information of an image is the focus of improving
the accuracy of the segmentation model. In the proposed model, we utilize the statistical
information of the local region to construct a weighted pressure force function, which
adaptively shrinks and expands the closed contour. In addition, an enhanced distance
regularized level set method is proposed to improve the speed of the segmentation
algorithm, and to avoid the process of reinitialization.
2- Previous work section needs more recent related work, and research gap should be
explained.
Answer:
Thank you very much for your valuable comments, which is a great help not only to
improve the quality of this manuscript but also to instruct our research in future!
First, we added two sections to the previous works section to introduce two recent
classic segmentation models.
Second, we added a summary to the previous works section to summarize the previous
work and explain the research gap.
The detail changes in revised version are as follows:
� In the “Previous works” section, two subsections are added to the first subsection
(CV model) and the fourth subsection (LRFI model), respectively.
Previous works
CV model
The CV model, as a classic typical energy-based segmentation model, is proposed to
segment two-phase images, based on the assumption that the target is segmented and
the background is intensity inhomogeneous. Let an image I(x) on the image domain �, the energy function is defined as:
……
The CV model can well segment images with intensity inhomogeneity, however, it has
more sensitivity to initialization information and low efficiency while segmenting
images with high noise and severe intensity inhomogeneity.
……
LRFI model
Liu et al. utilized local regional fitting information to propose an improved level
set method, which can differentiate the noise and boundary points of the image to
be segmented. In this model, two innovations are proposed: first, a controllable velocity
coefficient was proposed to accelerate the curve convergence speed, and second, a
new edge stop function was constructed to enhance the performance of the segmentation
model. The velocity function was shown as:
……
where fin(x) and fout(x) are local regional fitting means of image pixels inside and
outside of the closed contour, respectively, and � and k are two positive coefficients.
And, the edge stop function (ESF) was defined as:
……
According to the principle of the variational method, the curve iteration function
of the model is described as follows:
……
Compared with the DRLSE model, the function v(x) of the LRFI model can better make
the closed curve converge along the object boundaries, and the ESF improves the accuracy
of the numerical calculation. The LRFI model demonstrates good performance in segmenting
noisy images.
� In the last subsection, summary is added to summarize the previous work and explain
the research gap.
Summary
In summary, the four segmentation models based on level set method have made great
contributions to images segmentation with intensity inhomogeneity. As a typical segmentation
model based on level set, The CV model has the merit of high convergence speed if
segmenting two phase images with intensity homogeneity. The DRLSE model normalizes
the movement of the curve during the curve evolution process and improves the accuracy
of the numerical solution. However, this model has poor performance in segmenting
weak boundaries and high noise images. In the LVC model, the SPF is used to control
the direction of the curve evolution which further improves the speed of iteration.
The model improves the efficiency of the segmentation algorithm. Although this model
is more efficient than the DRLSE model, this model loses its advantages when the image
to be segmented has high intensity inhomogeneity and is less efficient when segmenting
severe high-noise images. The LRFI model improves the efficiency of segmenting noisy
images, however, it does not perform well in segmenting fuzzy boundary images.
Actually, the above segmentation models have their own advantages and disadvantages.
How to make full use of the local information of the image is the focus of improving
the accuracy of the segmentation model. In the proposed model, we utilize the statistical
information of the local region to construct a weighted pressure force function, which
adaptively shrink and expand the closed contour. In addition, an enhanced distance
regularized level set method is proposed to improve the speed of segmentation algorithm,
and to avoid the process of reinitialization.
3- Author must compare proposed method with more image segmentation methods such as
sematic segmentation.
Answer:
Thank you very much for your valuable comments, which is a great help not only to
improve the quality of this manuscript but also to instruct our research in future!
First, we have added two classic segmentation models to related works. These models
are the same type of segmentation method as the model proposed in this manuscript,
so there are comparable to the proposed model.
Second, in addition to the above method, we also added comparative experiments with
other classic methods in the experimental section.
The detail changes in revised version are as follows:
� In the section of “Previous works”, two classic segmentation models are added to
the related works.
Previous works
CV model
The CV model, as a classic typical energy-based segmentation model, is proposed to
segment two-phase images, based on the assumption that the target is segmented and
the background is intensity inhomogeneous. Let an image I(x) on the image domain �, the energy function is defined as:
……
According to the variational method, the evolution equation of the curve is expressed
as follows:
……
The CV model can segment images with intensity inhomogeneity; however, it has more
sensitivity to initialization information and low efficiency while segmenting images
with high noise and severe intensity inhomogeneity.
……
LRFI model
Liu et al. utilized local regional fitting information to propose an improved level
set method, which can differentiate the noise and boundary points of the image to
be segmented. In this model, two innovations are proposed: first, a controllable velocity
coefficient was proposed to accelerate the curve convergence speed, and second, a
new edge stop function was constructed to enhance the performance of the segmentation
model. The velocity function was shown as:
……
According to the principle of the variational method, the curve iteration function
of the model is described as follows:
……
Compared with the DRLSE model, the function v(x) of the LRFI model can better make
the closed curve converge along the object boundaries, and the ESF improves the accuracy
of the numerical calculation. The LRFI model demonstrates good performance in segmenting
noisy images.
� we also added comparative experiments with other classic methods in the experimental
section.
Figure 4. Comparisons of the proposed model with the CV, the LVC, the LRFI on heart
images
In order to further demonstrate the efficiency of the proposed model, we test the
proposed model for two images with severe intensity inhomogeneity shown as in Figure
4. The heart image has the characteristics of high noise and low contrast that brings
certain challenges to image segmentation. As shown in Figure 4, the CV model cannot
correctly obtain the boundary of the image to be segmented and it results in poor
segmentation results. Compared with the LCV model, this model has a higher segmentation
accuracy for multiple targets, and can quickly segment two parts of the heart based
on local information. The segmentation effect of the LVC and LRFI models is not as
good as that of the proposed model.
4- Experimental results should have more explanation.
Answer:
Thank you very much for your valuable comments, which is a great help not only to
improve the quality of this manuscript but also to instruct our research in future!
It's our fault not to describe it clearly in the experiment part. We have done a lot
of improvement work in revised version.
The detail changes in revised version are as follows:
� In the section of “Experimental results”, we have analyzed the experimental results
in detail to verify the advantages of this model over other models.
Experimental results
This section shows experiments to demonstrate the effectiveness of the proposed model
for both synthetic and real images. The proposed model is compared with the state-of-the
art segmentation model based on level set methods to validate the effectiveness and
robustness of our model. The proposed model is implemented in MATLAB R2018b on a 2.3
GHz Intel and 8.0GB RAM computer.
……
Qualitative evaluation
In the first experiment, we compare the proposed model with the LVC, GAC and DRLSE
models in segmenting a synthetic image with noise. As shown in Figure 1, the image
has the characteristics of fuzzy boundaries and multiple sharp corners, which brings
greater difficulty to segmentation. The GAC, as a typical representative of energy-based
models, has made a certain contribution to image segmentation. However, when the edges
of the image are blurry or sharp corners, the segmentation accuracy of GAC model is
significantly reduced. The regularization idea in The DRLSE model better standardizes
the iteration of the curve and improves the accuracy of the solution, but the segmentation
accuracy of the fuzzy boundary image is not high. As shown in Figure 1, compared with
the GAC and DRLSE models, the segmentation accuracy of this model is higher at the
fuzzy angles of the image. As a representative edge-based LSM, the GAC and DRLSE models
perform poorly, mainly because the synthetic image is affected by noise and blurred
boundaries.
……
Compared with the other models, the LVC model is the best in terms of segmentation
speed, and the segmentation results are better. However, the generalization ability
of this model is weak. For example, the segmentation effect of the model for the second
image is poorer than that of the proposed model. Although the proposed model is not
optimal in terms of segmentation speed, in terms of segmentation results, the segmentation
effect of the model is best as shown in Figure 2. In the proposed model, the statistical
information inside and outside the closed curve contour is utilized to construct the
weighted pressure force, which greatly improves the accuracy of image segmentation
with intensity inhomogeneity. Therefore, by comparing multiple models, the proposed
model performs better in the accuracy of segmenting medical images.
Brain tissue segmentation has always been a research hotspot and difficulty in medical
image segmentation. As shown in Figure 3, this experiment is mainly used to compare
the accuracy of segmented brain tissue images. The proposed model makes full use of
the local information of the image to construct the model and obtains a good segmentation
effect. Specifically, Experiment 3 applied segmentation models to real medical images
from slices of cerebral tissue taken via MRI. Figure 3 depicts the performance comparison
of the proposed model on the brain images with the standard models including the LIF
model, the LSACM model and the LVC model.
5- The presentation and writing of this work need major revision.
Answer:
Thank you very much for your valuable comments, which is a great help not only to
improve the quality of this manuscript but also to instruct our research in future!
� After we carefully revised the manuscript based on expert reviews, then the manuscript
was edited for proper English language, grammar, punctuation, spelling, and overall
style by one or more of the highly qualified native English-speaking editors at AJE.
6- Conclusion part needs more revision, and should be rewritten.
Answer:
Thank you very much for your valuable comments, which is a great help not only to
improve the quality of this manuscript but also to instruct our research in future!
The detail changes in revised version are as follows:
Conclusions and future work
In this paper, we proposed a weighted region-based active contour model. The model
exploits the statistical information inside and outside the closed curve contour of
the image to construct a new weighted pressure force (WPF) function. The WPF function
was utilized to modulate the signs of the balloon forces inside and outside of the
closed contour, aiming for contraction or expansion freely of the closed contour.
In addition, a regularization term was adopted to ensure the stability of the curve
evolution and reduce reinitialization in curve evolution. Extensive experiments on
medical and natural images demonstrate that the proposed model is suitable for segmenting
images with intensity inhomogeneity with high accuracy as compared to the other classic
models. Moreover, the model seems to be more robust to severe noise levels and has
less dependence on initialization information.
In terms of future work, we will continue our research at least three areas.
(1) We will try to improve the performance of the segmentation model using intelligent
optimization methods. (2) We will expand the application fields of the proposed model,
including semantic segmentation, edge detection and other related fields [31-39].
(3) We will study algorithm evaluation methods to objectively evaluate various algorithms
in multiple dimensions.
7- References need major revision, for example references 2, 5, 30, 31, 35.
Answer:
Thank you very much for your valuable comments, which is a great help not only to
improve the quality of this manuscript but also to instruct our research in future!
� First, the authors apologize that the reference format is incorrect.
� Second, in revised version, we have carefully improved the references in accordance
with the format requirements. We revised the references 2,5,30,31,35. We also have
made major revision for all the references (old and added) in accordance with the
format requirements.
The detail changes in revised version are as follows:
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8- The paper should be in the Plos one format not IEEE Access.
Answer:
Thank you for your advice and patiently reviewing of our manuscript! we are very
sorry for the irregular format, In the revised version, we have typeset in strict
accordance with the standard format of Plos One.
Thank you again for carefully and patiently reviewing of our manuscript!
9- I think the authors should spend some time in revision justifying why this work
is worthy of publication.
Answer:
Thank you for your advice and patiently reviewing of our manuscript! In the revised
version, we made a lot of changes to highlight our work.
� First, in Section of “Introduction”, by citing preliminary work, we describe the
new idea of the proposed the model, which is used to make up for the shortcomings
of the existing methods.
� Second, in Section of “Previous works”, Two subsections are added to the first subsection
(CV model) and the fourth subsection (LRFI model), which are mainly used for comparison
of segmentation models of the same type.
� Third, in Section of “The proposed model”, we elaborated on the construction of the
new model and the execution steps of the algorithm, and analyzed in detail the advantages
of this model compared with other classic models.
� Fourth, In Section of “Experimental results”, we have analyzed the experimental results
in detail to verify the advantages of this model over other models.
Reviewer #2: This manuscript proposes a level-set-based method for image segmentation.
The manuscript is clear, straightforward, easy to follow. However, I would like to
draw the author's attention to the following major concerns:
Thanks very much for kind comment!
Accordingly, based on your following detail comments, we improve the manuscript as
much as possible in revised version one by one as follows.
1)Overall, the motivation is not introduced well, where the challenges should be described
before the contributions. I recommend the authors to employ certain intuitive examples
to elaborate the novelties of the proposed work.
Answer:
Thank you for your valuable comments! Your comments are very instructive and helpful
for our future work. In revised version, we elaborate on our work to highlight the
innovation of the model, which is mainly reflected in Sections of “Introduction”,
“Previous works”, “The proposed Model”, and “Experimental results”.
The detail changes in revised version are as follows:
� In the latter of Section "Introduction", we have further expanded the content and
introduced our research model based on the shortcomings of the previous work.
In this paper, we propose a weighted region-based level set method to segment images
with intensity inhomogeneity. In the model, first, a new weighted pressure force (WPF)
is proposed to adaptively modulate the contractility of the balloon forces inside
and outside of the closed contour by restricting the unevenness of the intensity mean
value inside and outside the closed curve; second, a faster and smoother regularization
term is utilized to ensure the stability of the curve evolution during the process
of solving; finally, the WPF is integrated into the level set framework to improve
the accuracy of segmentation images with intensity inhomogeneity.
� In the section of “Previous works”, we added a new subsection to elaborate the idea
of the proposed segmentation model by comparing with other standard models.
Summary
In summary, the four segmentation models based on the level set method have made great
contributions to image segmentation with intensity inhomogeneity. As a typical segmentation
model based on the level set, the CV model has the merit of high convergence speed
if segmenting two phase images with intensity homogeneity. The DRLSE model normalizes
the movement of the curve during the curve evolution process and improves the accuracy
of the numerical solution. However, this model has poor performance in segmenting
weak boundaries and high noise images. In the LVC model, the SPF is used to control
the direction of the curve evolution which further improves the speed of iteration.
The model improves the efficiency of the segmentation algorithm. Although this model
is more efficient than the DRLSE model, this model loses its advantages when the image
to be segmented has high intensity inhomogeneity and is less efficient when segmenting
severe high-noise images. The LRFI model improves the efficiency of segmenting noisy
images; however, it does not perform well in segmenting fuzzy boundary images.
Actually, the above segmentation models have their own advantages and disadvantages.
How to make full use of the local information of an image is the focus of improving
the accuracy of the segmentation model. In the proposed model, we utilize the statistical
information of the local region to construct a weighted pressure force function, which
adaptively shrinks and expands the closed contour. In addition, an enhanced distance
regularized level set method is proposed to improve the speed of the segmentation
algorithm, and to avoid the process of reinitialization.
� In the section of “The proposed model”, we have expanded the content to better understand
the ideas of the model in this manuscript.
The proposed model
Through the analysis of the above model, two core issues need to be solved: first,
how to freely shrink and expand the closed contour by using the local information
of an image; second, how to improve the accuracy of the segmentation model solution.
Through in-depth research on the energy-based segmentation model in the previous work,
this paper proposes a novel segmentation model based on local energy. Specifically,
in this section, we present the details of the proposed weighted region-based ACM,
which is based on the techniques of local statistical theory, curve evolution and
the level set method…….
……
Advantage of the EDRLSM model over the other models
The advantage of the proposed model lies in the WPF function we constructed, which
not only suppresses the influence of noise, thereby improving the accuracy of segmentation,
but also increases the speed of the segmentation algorithm.
Compared with the classic CV model, the EDRLSM utilizes local information of the
image to construct the WPF, and the proposed model establishes an energy function
based on the local intensity information of the image, which greatly improves the
accuracy of image segmentation with intensity inhomogeneity…….
� In the section of “Experimental results”, We thoroughly analyzed the segmentation
results of different types of images with intensity inhomogeneity to verify the advantages
of the new model compared to other models.
Experimental results
This section shows experiments to demonstrate the effectiveness of the proposed model
for both synthetic and real images. The proposed model is compared with the state-of-the
art segmentation model based on level set methods to validate the effectiveness and
robustness of our model. The proposed model is implemented in MATLAB R2018b on a 2.3
GHz Intel and 8.0GB RAM computer.
……
Qualitative evaluation
In the first experiment, we compare the proposed model with the LVC, GAC and DRLSE
models in segmenting a synthetic image with noise. As shown in Figure 1, the image
has the characteristics of fuzzy boundaries and multiple sharp corners, which brings
greater difficulty to segmentation. The GAC, as a typical representative of energy-based
models, has made a certain contribution to image segmentation. However, when the edges
of the image are blurry or sharp corners, the segmentation accuracy of GAC model is
significantly reduced. The regularization idea in The DRLSE model better standardizes
the iteration of the curve and improves the accuracy of the solution, but the segmentation
accuracy of the fuzzy boundary image is not high…….
……
Compared with the other models, the LVC model is the best in terms of segmentation
speed, and the segmentation results are better. However, the generalization ability
of this model is weak. For example, the segmentation effect of the model for the second
image is poorer than that of the proposed model. Although the proposed model is not
optimal in terms of segmentation speed, in terms of segmentation results, the segmentation
effect of the model is best as shown in Figure 2. In the proposed model, the statistical
information inside and outside the closed curve contour is utilized to construct the
weighted pressure force, which greatly improves the accuracy of image segmentation
with intensity inhomogeneity. Therefore, by comparing multiple models, the proposed
model performs better in the accuracy of segmenting medical images.
Brain tissue segmentation has always been a research hotspot and difficulty in medical
image segmentation. As shown in Figure 3, this experiment is mainly used to compare
the accuracy of segmented brain tissue images. The proposed model makes full use of
the local information of the image to construct the model and obtains a good segmentation
effect. Specifically, Experiment 3 applied segmentation models to real medical images
from slices of cerebral tissue taken via MRI…….
……
2)The paper does not explain clearly its advantages with respect to recent deep-learning
literature: it is not clear what is the novelty and contributions of the proposed
work: does it propose a new method? Or does the novelty only consist in the application?
Answer:
Thank you for your valuable comments! Your question is very good, and we have elaborated
it in the section of “Introduction”. We proposed a new method which is based on active
contour model.
The detail changes in revised version are as follows:
� In the second paragraph of section of “introduction”, we added a detailed explanation
to expound clearly its advantages with respect to recent deep-learning literature.
In recent years, deep learning, such as convolutional neural networks (CNNs) are widely
used to segment images and is have achieved excellent results in the medical image
segmentation. However, these methods require considerable training dataset and have
a complex structure and low execution efficiency. Although some deep segmentation
models that support small sample data have recently appeared, these models often have
over-segmentation or cross-learning problem. Therefore, Therefore, model-based image
segmentation methods still have space for research, and these methods have higher
time and space efficiency.
3) What is the motivation of the proposed work? Research gaps, objectives of the proposed
work should be clearly justified. The authors should consider more recent research
done in the field of their study
Answer:
Thank you for your valuable comments! Your comments are very instructive and helpful
for our future work.
First, we added two sections to the previous works section to introduce two recent
classic segmentation models.
Second, we added a summary to the previous works section to summarize the previous
work and explain the research gap.
The detail changes in revised version are as follows:
� In the “Previous works” section, two subsections are added to the first subsection
(CV model) and the fourth subsection (LRFI model), respectively.
Previous works
CV model
The CV model, as a classic typical energy-based segmentation model, is proposed to
segment two-phase images, based on the assumption that the target is segmented and
the background is intensity inhomogeneous. Let an image I(x) on the image domain �, the energy function is defined as:
……
According to the method of variational method, the evolution equation of the curve
is expressed as follows:
……
The CV model can well segment images with intensity inhomogeneity, however, it has
more sensitivity to initialization information and low efficiency while segmenting
images with high noise and severe intensity inhomogeneity.
……
LRFI model
Liu et al. utilized local regional fitting information to propose an improved level
set method, which can differentiate the noise and boundary points of the image to
be segmented. In this model, two innovations are proposed: first, a controllable velocity
coefficient was proposed to accelerate the curve convergence speed, and second, a
new edge stop function was constructed to enhance the performance of the segmentation
model. The velocity function was shown as:
……
where fin(x) and f(x) are local regional fitting means of image pixels inside and
outside of the closed contour, respectively, and � and k are two positive coefficients.
And, the edge stop function (ESF) was defined as:
……
According to the principle of the variational method, the curve iteration function
of the model is described as follows:
……
Compared with the DRLSE model, the function v(x) of the LRFI model can better make
the closed curve converge along the object boundaries, and the ESF improves the accuracy
of the numerical calculation. The LRFI model demonstrates good performance in segmenting
noisy images.
� In the last subsection, summary is added to summarize the previous work and explain
the research gap.
Summary
In summary, the four segmentation models based on level set method have made great
contributions to images segmentation with intensity inhomogeneity. As a typical segmentation
model based on level set, The CV model has the merit of high convergence speed if
segmenting two phase images with intensity homogeneity. The DRLSE model normalizes
the movement of the curve during the curve evolution process and improves the accuracy
of the numerical solution. However, this model has poor performance in segmenting
weak boundaries and high noise images. In the LVC model, the SPF is used to control
the direction of the curve evolution which further improves the speed of iteration.
The model improves the efficiency of the segmentation algorithm. Although this model
is more efficient than the DRLSE model, this model loses its advantages when the image
to be segmented has high intensity inhomogeneity and is less efficient when segmenting
severe high-noise images. The LRFI model improves the efficiency of segmenting noisy
images, however, it does not perform well in segmenting fuzzy boundary images.
Actually, the above segmentation models have their own advantages and disadvantages.
How to make full use of the local information of the image is the focus of improving
the accuracy of the segmentation model. In the proposed model, we utilize the statistical
information of the local region to construct a weighted pressure force function, which
adaptively shrink and expand the closed contour. In addition, an enhanced distance
regularized level set method is proposed to improve the speed of segmentation algorithm,
and to avoid the process of reinitialization.
4) Authors should add more details about the implementation of the code to perform
the analysis and the dateset involved in this task.
Answer:
Thank you for your valuable comments! Your comments are very instructive and helpful
for our future work.
First, In the section of “Experimental results”, a more detailed description of
the experimental environment was added.
Second, we conducted a more comprehensive analysis of the experimental comparison
results.
The detail changes in revised version are as follows:
� In the first paragraph of “Experimental results”, a more detailed description is
added.
Experimental results
This section shows experiments to demonstrate the effectiveness of the proposed model
for both synthetic and real images. The proposed model is compared with the state-of-the
art segmentation model based on level set methods to validate the effectiveness and
robustness of our model. The proposed model is implemented in MATLAB R2018b on a 2.3
GHz Intel and 8.0GB RAM computer.
� A more comprehensive analysis of the experimental comparison results is involved
in this task.
Qualitative evaluation
In the first experiment, we compare the proposed model with the LVC, GAC and DRLSE
models in segmenting a synthetic image with noise. As shown in Figure 1, the image
has the characteristics of fuzzy boundaries and multiple sharp corners, which brings
greater difficulty to segmentation. The GAC, as a typical representative of energy-based
models, has made a certain contribution to image segmentation. However, when the edges
of the image are blurry or sharp corners, the segmentation accuracy of GAC model is
significantly reduced. The regularization idea in The DRLSE model better standardizes
the iteration of the curve and improves the accuracy of the solution, but the segmentation
accuracy of the fuzzy boundary image is not high. As shown in Figure 1, compared with
the GAC and DRLSE models, the segmentation accuracy of this model is higher at the
fuzzy angles of the image. As a representative edge-based LSM, the GAC and DRLSE models
perform poorly, mainly because the synthetic image is affected by noise and blurred
boundaries.
……
As shown in Figure 2,……Compared with the other models, the LVC model is the best in
terms of segmentation speed, and the segmentation results are better. However, the
generalization ability of this model is weak. For example, the segmentation effect
of the model for the second image is poorer than that of the proposed model. Although
the proposed model is not optimal in terms of segmentation speed, in terms of segmentation
results, the segmentation effect of the model is best as shown in Figure 2. In the
proposed model, the statistical information inside and outside the closed curve contour
is utilized to construct the weighted pressure force, which greatly improves the accuracy
of image segmentation with intensity inhomogeneity. Therefore, by comparing multiple
models, the proposed model performs better in the accuracy of segmenting medical images…….
Brain tissue segmentation has always been a research hotspot and difficulty in medical
image segmentation. As shown in Figure 3, this experiment is mainly used to compare
the accuracy of segmented brain tissue images. The proposed model makes full use of
the local information of the image to construct the model and obtains a good segmentation
effect. Specifically, ……
5) Considering that deep-learning-based methods (Such as Mask RCNN, Unet) have shown
impressive performance in image segmentation, the advantages and disadvantages of
the proposed method should be discussed more clearly, and presented in the experiment.
Answer:
Thank you for your valuable comments! Your comments are very instructive and helpful
for our future work.
Deep-learning-based methods have shown impressive performance in image segmentation,
since our segmentation model is for small sample data, the proposed model is based
on the energy-based model. We give an explanation in the Introduction section. Of
course, the learning-based method has its advantages, which is the direction of our
future research.
6) The authors should do a more thorough literature survey. Just to name a few:
- Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks
for biomedical image segmentation. In International Conference on Medical image computing
and computer-assisted intervention (pp. 234-241). Springer, Cham.
- Xu, C., Xu, L., Gao, Z., Zhao, S., Zhang, H., Zhang, Y., ... & Li, S. (2018). Direct
delineation of myocardial infarction without contrast agents using a joint motion
feature learning architecture. Medical image analysis, 50, 82-94.
- Minaee, S., Boykov, Y. Y., Porikli, F., Plaza, A. J., Kehtarnavaz, N., & Terzopoulos,
D. (2021). Image segmentation using deep learning: A survey. IEEE Transactions on
Pattern Analysis and Machine Intelligence.
- Sultana, F., Sufian, A., & Dutta, P. (2020). Evolution of image segmentation using
deep convolutional neural network: a survey. Knowledge-Based Systems, 201, 106062.
Answer:
Thank you very much for your valuable comments which improves the quality of this
manuscript.
First, we have added relevant references as needed;
Second, we add relevant references in accordance with the standard format required
by PLOS ONE for reference documents, as shown in references [16], [32],[33],[34].
The detail changes in revised version are as follows:
[16] Xu C, Lei X, Gao Z, et al. Direct delineation of myocardial infarction without
contrast agents using a joint motion feature learning architecture[J]. Medical Image
Analysis. 2018; S1361841518306960-. doi:10.1016/j.media.2018.09.001
……
[32] Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical
Image Segmentation. Springer, Cham. 2015; doi: 10.1007/978-3-662-54345-0_3
[33] Minaee S, YY Boykov, Porikli F, et al. Image Segmentation Using Deep Learning:
A Survey. IEEE Transactions on Software Engineering, 2021, (99). doi:10.1109/TPAMI.2021.3059968
[34] Sultana F, Sufian A, Dutta P. Evolution of Image Segmentation using Deep Convolutional
Neural Network: A Survey[J]. Knowledge-Based Systems, 2020, s 201–202.doi:10.1016/j.knosys.2020.106062
7) There are some grammar errors and typos. I suggest the authors make an solid, overall
proofreading.
Answer:
Thank you very much for your valuable comments, which is a great help not only to
improve the quality of this manuscript but also to instruct our research in future!
� After we carefully revised the manuscript based on expert reviews, then the manuscript
was edited for proper English language, grammar, punctuation, spelling, and overall
style by one or more of the highly qualified native English-speaking editors at AJE.
Thank you very much for your valuable comments, again!
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