Retinal Vessel Segmentation in U-Net Using Deep Learning

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 05 | May 2022 www.irjet.net p-ISSN: 2395-0072
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Retinal Vessel Segmentation in U-Net Using Deep Learning
Shifa Dadan1, Anagha Jagtap2
1B.E Graduate(IV year) , Department of Computer Engineering , MGMCET , Maharashtra ,India
2B.E Graduate(IV year) , Department of Computer Engineering , MGMCET , Maharashtra , India
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Abstract - Vessel segmentation could be a key step for
varied medical applications, it's wide utilized in watching
the unwellness progression, and analysis of assorted
ophthalmologic diseases. However, manual vessel
segmentation by trained specialists could be a repetitive and
long task. within the last 20 years, several approaches are
introduced to section the retinal vessels mechanically With
the more moderen advances within the field of neural
networks and deep learning, multiple strategies are
enforced with specialize in the segmentation and
delineation of the blood vessels. This project applies deep
learning techniques to the retinal blood vessels
segmentations supported spectral body structure pictures. It
presents a network and coaching strategy that depends on
the info augmentation to use the offered annotated samples
additional with efficiency. Thus, the shape, size, and blood
vessel crossing sorts are often accustomed get the proof
regarding the many eye diseases. Additionally, we tend to
apply deep learning supported U-Net convolutional network
for real patients’ body structure pictures. As results of this,
we tend to succeed high performance and its results square
measure far better than the manual approach of a talented
specialist.
Key Words: U-Net convolutional neural network; deep
learning; image segmentation; blood vessels
segmentation.
1.INTRODUCTION
1.1 Problem Statement
The retinal system provides wealthy data regarding
the state of the attention and is that the solely non-
invasive imaging methodology to get visible blood
vessels from the anatomy. Retinal vascular segmentation
is of nice significance for the identification of complex
body part diseases . As a result, retinal pictures are wide
wont to find early signs of general vascular malady. so as
to facilitate the identification of general vascular
diseases, vessels have to be compelled to be accurately
segmental. Therefore, the automated segmentation of
retinal blood vessels from complex body part pictures
has become a preferred analysis topic within the medical
imaging field.
1.2 Scope
The retinal system provides made data regarding the
state of the attention and is that the solely non-invasive
imaging technique to get visible blood vessels from the
significance for the designation of bodily structure
al pictures are wide wont to
notice early signs of general tube illness. so as to facilitate
the designation of general tube diseases, vessels have to be
compelled to be accurately divided. Therefore, the
automated segmentation of retinal blood vessels from
bodily structure pictures has become a preferred analysis
diseases within the physical body will be detected through
changes within the morphology and morphology of retinal
vessels. Therefore, the condition of the retinal vessels is a
vital indicator for the designation of some retinal diseases.
as an example, the progression of diabetic retinopathy is
that the most severe as a result of it ends up in vision loss
because of high sugar levels and high blood pressure in
body before by examining some eye diseases Associate in
Nursingd build an early designation of those diseases to
hold out the corresponding treatment before. in step with
reports, early detection, timely treatment, and applicable
follow-up procedures will forestall regarding ninety fifth
of visual disorder.
1.3 Objectives
The main goal for this project is to review and analyze
completely different approaches supported deep learning
techniques for the segmentation of retinal blood vessels.
so as to try to to thus, completely different style and
architectures of CNN’s are going to be studied and
analyzed, as their results and performance ar evaluated
and compared with the offered algorithms. One alternative
necessary objective of this project is to review and value
the various techniques that are used for vessel
segmentation, supported machine learning, and the way
these is combined with the deep learning approaches: by
analyzing the options that the learned models ar
victimisation to perform classification and mixing them

with completely different machine learning techniques,
another objective is to propose an answer or set of
solutions to perform the retinal vessel segmentation,
victimization this system of labor.
2. LITERATURE REVIEW
2.1 Existing systems
.1 Matched Filter Approach
The gray-level pages of the cross-sections of retinal boats
have a strength page which can be approximated by way
of a Gaussian. Vessel sections at numerous orientations
are found by convolving the image with spun forms of the
coordinated filtration kernel and maintaining just the
utmost response. At an angular solution of 15◦, an entire of
12 convolutions is required.
2 Scale-Space Analysis and Region Growing Approach
It employs the Mixture of degree place evaluation and site
rising to portion the vasculature. Two characteristics are
acquainted with characterize the body ships, the gradient
magnitude of the image depth |∇I| and also the shape
energy equally at various scales. the form energy is ready
by calculating the utter greatest eigenvalue |λ1| of the
matrix of 2nd buy derivatives of the image depth (the
Hessian). to require into consideration the large difference
in vessel breadth over the retina equally these
characteristics are normalized by the degree s on the
scale-space while keeping just the world maxima.
3 Mathematical Morphology and Curvature Estimation
Approach
This is a general vessel segmentation technique counting
on the using exact morphology. Your algorithm criteria as
such consists of various morphological businesses and
also may be split into quite an few methods: 1. Reputation
involving line elements just by computing the particular
supremum involving open positions with a line
constructing aspect on various orientations. 2. Racket
elimination just by employing a geodesic renovation of
your supremum involving open positions within the very
first image. 3. Elimination of a spread of unfavorable styles
by using the particular Laplacian upon the top results of
the sooner move then an exclusively created switching
filter. Your outcome may be build up a tolerance to create
a segmentation of your vasculature.
4 Verification-based Local Thresholding Approach
This versatile regional thresholding construction is
decided by confirmation dependent multithreshold
probing scheme. Retinal boats cannot be segmented
through the employment of international ceiling as a
consequence of gradients device within the image. Rather
Jiang et al. give probe the image by using numerous
thresholds. During each one of the probed thresholds most
binary items from the ceiling impression are usually
extracted. By utilizing some form of class process on the
things, solely individuals’ vessel-like options may be
retained. the various maintained binary items could also
be put together to provide binary ship pine segmentation.
the particular level of responsiveness related to the
technique could also be inflated by simply adjusting the
particular variables within the class process, i.e. in order
that it's significantly less or even more strict.
5 Pixel Classification Approach
An easy vessel segmentation procedure is betting on pixel
classification. for each single pixel from the impression,
part vector are going to be produced and also a classifier
are experienced with such aspect vectors. Options usually
are taken from saving money aircraft of your retinal
photographs only. Around first tests many people when
put next 2 sorts of functions: a production of filter systems
and also the pixel prices during a neighborhood.
2.3 Disadvantages of existing systems
It has been observed that the majority of existing vessel
segmentation techniques suffers from the following issues.
Neighborhood Estimator before Filling
1. The issue of noise in fundus images is ignored in the
majority of existing literature.
2. Although Neighborhood Estimator before Filling has
shown significant results over available techniques, but it
is poor in its speed.
3. The Neighborhood Estimator before Filling is rich in
preserving the edges but not so efficient for high density of
multiple noises.
3 PROPOSED SYSTEM
The primary focus in this work is to extract the blood
vessels from retinal images obtained from fundus camera
images. Retinal vasculature is made up of hollow pipes of
different sizes. It comprises of arteries, capillaries, veins
and venules. The proposed model in this study (Figure 1)
is a modified version of the original U-NET architecture
that has become a benchmark in biomedical image
segmentation. The U-NET model classifies the pixels of an
input image into either a vessel pixel or a non-vessel pixel
and therefore extracts the retinal blood vessels from the
input image. The original U-NET architecture consists of
two main components. The left half is the first component
of the model that is called the contraction path or the
encoder path while the right half is the second component
of the model that is called the expansion path or the
decoder path. The encoder comprises of convolution

layers and max pooling layers. The decoder comprises of
up-sampling layers along with convolution layers.
Figure 3.1: Proposed network architecture for retinal
image segmentation
In the proposed architecture, the input images are
firstly cropped to a size of 512X512. Next, each image is
divided into 64 patches of resolution 64X64 before feeding
in to the main architecture. The depth of the proposed
architecture is 4. As the input image moves through the
network, it is reduced from 64X64 to 4X4 before the up-
sampling phase begins. The proposed architecture
produces [16, 32, 64, 128, 256]-sized feature maps at
successive layers that is significantly lower than original
U-NET architecture. This reduces the number of trainable
parameters remarkably while also providing
improvements in the performance. A succession of 2
convolutions are performed at every layer. Throughout
this network, 3X3 convolutions are performed. Therefore,
we have an initial image of 64X64. From this image, 16
filters are extracted twice, according to the convolutional
step. Let us call this output C1 (output of convolution layer
at depth 1). Since we use padding in the input image, the
size of the image after the convolution steps does not
change. Next, max pooling is performed and the size of the
image is reduced from 64X64 to 32X32. Let us call this
output P1. C1 gets passed from this layer to the decoder
part at the same depth. P1 is passed to the next layer and
the same process of successive convolutions is repeated at
each level of depth. Now, C1, C2, C3, C4 & C5 are the
outputs after the successive convolution operation and P1,
P2, P3, P4 & P5 are the outputs after the max pooling
operation.For up-sampling, the image size gets double at
every step, going from 4X4 to 64X64. Up-sampling
performs 2X2 transposed convolutions with stride value 2
and padding setting ‘same’ with number of feature maps
being halved each time. This operation doubles the input
map dimensions. C1, C2, C3, C4 gets concatenated from
encoder part to the symmetrically equivalent layer on the
decoder part. Skip connections helps the network to
extract features in various levels of detail and provides
output of higher quality with localization information. Skip
connections combine low level high resolution features
and high level low resolution features. Finally, an output is
produced according to binary classification of pixels: a
pixel being either a vessel pixel or a non-vessel pixel.
Similarly, predictions are generated for all the pixels in the
image and thereby creating the retinal blood vessel
structure.
The choice of activation function to be used in the
network may have serious implications on the
performance of the network. In this proposed model, ‘ELU’
activation function has been employed. It stands for
Exponential Linear Unit. In contrast to the ‘RELU’
activation function, that is a popular choice among the
community, the ‘ELU’ also takes negative values. Another
activation function that was applied in the experiments is
sigmoid. It takes real values as input and generates an
output value between 0 and 1. It fulfils all the properties
desirable of an activation function while also providing
output in form of a probability.
4. RESULT
Figure 4.1 : Training Dataset
Figure 4.2 : Mask Manual (Contains 20 images)

Figure 4.3 : Pre-processing training and test dataset
Figure 4.4 : Pre-processed Training dataset
Figure 5.5 : Generated 50 Epochs
Figure 6.6 : Output screen 2 of Epoch
Figure 4.7 : Output Screen (Jaccard , F-score , Recall ,
Precision , Accuracy)
Figure 4.8 : Result on DRIVE dataset (a)RGB image
(b)Ground truth (c) Prediction
Figure 4.9 : Result on DRIVE dataset 2

5. FUTURE SCOPE
1. Regarding the patch classification based approaches,
these can be improved with the additional study of
tuning techniques and parameters such as the
optimizers used and weight initialization. In addition
to this, the use of augmented data to train these
implementations can also be applied to improve their
final results.
2. Since the u-net based approach is the one that yielded
the best results, in the future it would be interesting
to use this on different databases such as CHASE and
ARIA, in order to assess the performance of the
solution when different data are used.
3. Considering now the work done regarding the use of
CNN for deep features extraction, the process of
feature extraction can be improved using a more
complex and robust network such as VGG16. Also the
use of one of the available pre-trained networks for
this task can save time and improve the performance.
4. As mentioned before, the classification process
required a considerable amount of time, one key
aspect to improve this is the use of feature selection
mechanism applied to all the implementations.
Regarding the SVM based implementations; the
results obtained were not the expected, as future
work, repeating these implementations and applying
fine tuning techniques should be considered. The
feature selection mechanism can also be enhanced by
increasing the number of folds used for cross
validation (to 5 or at least 3), in the case of the
recursive feature selection with cross-validation.
6. CONCLUSIONS
In this project, new convolutional network architecture
was proposed for retinal image vessel segmentation. It
achieved a better outcome in the DRIVE database and
performed better than a skilled ophthalmologist. From
Table 2 and comparing to the different methods for image
vessel segmentation, the accuracy of the proposed method
in this paper on DRIVE is 0.9790. That is to say that our
method is on the top of these compared methods While in
the practical fundus images, the results is not as good as
DRIVE database. The reason is that the images
photographed by ophthalmologist would have some
noises. If the preprocessing to remove the noise is
performed based on the raw images, we could guarantee
the results could be much better.
REFERENCES
[1] Research Section, Digital Retinal Image for Vessel
Extraction (DRIVE) Database. Utrecht, The Netherlands,
Univ. Med. Center Utrecht, Image
Sci.Inst.[Online].Available:http://www.isi.uu.nl/Research/
Databases/DR IVE.
[2] STARE Project Website. Clemson, SC, Clemson
[3] S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson and M.
Goldbaum, “Detection of Blood Vessels in Retinal Images
using Two-Dimensional Matched Filters”, IEEE Trans.
Medical Imaging, vol. 8, No.3, 1989.
[4] R. M. Haralick, J.S. J. Lee, and L.G.Shapiro,
”Morphological edge detection”, IEEE J. Robotics Automat.,
vol. RA-3, pp. 142-155, 1987.
[5] Mohammed Al-Rawi, Munib Qutaishat and Mohammed
Arrar, “An improved matched filter for blood vessel
detection of digital retinal images”, Elsevier Computers is
Biology and Medicine, vol. 37, pp. 262- 267, 2006.
[6] A. Hoover, V. Kouzntesova, M. Goldbaum, “ Locating
blood vessels in retinal images by piecewise threshold
probing of matched filter responses”, IEEE Trans. Med.
Imaging, 19 (3), pp. 203-210, 2000.
[7] V. B. Soares, Jorge J. G. Leandro, Roberto M. Cesar Jr.,
Herbert F. Jelinek and Michael J. Cree “Retinal Vessel
Segmentation Using the 2-D Morlet Wavelet and
Supervised Classification”, IEEE Trans. Medical Imaging,
Vol. 25, no. 9, pp. 1214- 1222, Sep. 2006
[8] C. Chang, C. Lin, P. Pai and Y. Chen, “A Novel Retinal
Blood Vessel Segmentation Method Based on Line
Operator and Edge Operator”, IEEE 5th International
Conference on Intelligent Information Hiding and
Multimedia Signal Processing, 2009
[9] E. Ricci, R. Perfetti, “Retinal Blood Vessel Segmentation
Using Line Operators and Support Vector Classification”,
IEEE Trans. Medical Imaging, Vol. 26, no. 10, pp. 1357-
1365, Oct. 2007.
[10] M. Akram, A. Atzaz, S. F. Aneeque and S. A. Khan,
“Blood Vessel Enhancement and Segmentation Using
Wavelet Transform”, IEEE International Conference on
Digital Image Processing, 2009.
[11] Diego Marín, Arturo Aquino*, Manuel Emilio
Gegúndez-Arias, and José Manuel Bravo, “A New
Supervised Method for Blood Vessel Segmentation in
Retinal Images by Using Gray-Level and Moment
Invariants-Based Features”, IEEE Trans. Medical Imaging,
Vol. 30, no. 1,Jan. 2011.

[12] Danu inkaew, Rashmi Turior, Bunyarit Uyyanonvara,
Toshiaki Kondo,” Automatic Extraction of Retinal Vessels
Based on Gradient Orientation Analysis”, IEEE Eighth
International Joint Conference on Computer Science and
Software Engineering (JCSSE), pp. 102-107, 2011
[13] J. Staal, M. D. Abramoff, M. Niemeijer, M. A. Viergever
and B. Van Ginneken, “Ridge-based vessel segmentation in
color images of the retina”, IEEE Trans. Medical Imaging,
Vol. 23, no. 4, pp. 501-509,Apr. 2004.
[14] M. U. Akram, I. Jamal, A. Tariq and J. Imtiaz,
“Automated Segmentation of Blood Vessels for Detection
of Proliferative Diabetic Retinopathy”, IEEE-EMBS
International Conference on Biomedical and Health
Informatics, China, Jan. 2012
[15] I. Lazar and A. Hajdu, “Segmentation of Vessels in
Retinal Images Based on Directional Height Statistics”,
34th Annual International Conference of the IEEE EMBS,
USA, Sep. 2012.
[16] H. Ocbagabir, I. Hameed, S. Abdulmalik and D. B.
Buket, “A Novel Vessel Segmentation Algorithm in Color
Images of the Retina”, IEEE Applications and Technology
Conference in Systems, pp. 1-6, May. 2013.
[17] M. E. G. Arias, A. Aquino, J.M. Bravo and D. Marin, ”A
Function for Quality Evaluation of Retinal Vessel
Segmentations”, IEEE Trans. Medical Imaging, Vol. 31, no.
2, pp. 231-239, Feb. 2012
[18] http://www.sc.chula.ac.th/courseware/2309507/Lec
ture/remote18.htm dated 30th Oct 2014.
[19] M.I Khan et. al, “A Review of Retinal Vessel
Segmentation Techniques And Algorithms”,Int. J. Comp.
Tech. Appl., Vol 2, no. 5, pp 1140-1144, Sep-Oct, 2011.
[20] S.S Honale et. al, “A Review of Methods for Blood
Vessel Segmentation in Retinal images”, IJERT, Vol. 1, Dec.
2012
[21] Edward James and Antonio Francisco, “On Supervised
Methods for Segmentation of Blood Vessels in Ocular
Fundus Images”. Advances in Image and Video Processing,
Vol 1, no. 1, pp. 21-37, Dec. 2013.
[22] M.M Fraz et. al, “Blood Vessel Segmentation
Methodologies in Retinal Images – A Survey”, Elsevier
Computer Methods and Programs in Biomedicine pp 407-
433,Mar. 2012.
[23] A. Mehrotra et. al,,”Blood Vessel Extraction For
Retinal Images Using Morphological Operator and KCN
Clustering”, IEEE International Advance Computing
Conference (IACC), 2014.

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