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International Journal on Scientific Research in Emerging TechnologieS, Vol. 2 Issue 5, August 2016
ISSN: 1934-2215
VESSEL SEGMENTATION GRAPH FOR IDENTIFY THE RETINAL
IMAGE FOR CARDIOVASCULAR DISEASES
BHUVANESWARI.S1
Department of ECE,
MeenakshiRamaswamy Engineering College,
Tamil Nadu
India,
SATHIYA PRIYA.T2
Assistant professor,Department of ECE
[email protected]
T.Thirumurugan3
Head of the Department,department of ECE
MeenakshiRamaswamy Engineering
College
Tamil Nadu,
India.
.
ABSTRACT
A Retinal image provides a good
diagnostic approach of what is happening
inside a human body. In this project, we
automatically extract the optic disc in
retinal images by pixel based segmentation
and optic cup by medial axis detection and
ellipse fitting method. In existing method
the optic cup and optic disc are segmented
by creating mask manually and from that
the glaucoma is detected. But its
experimental result doesn’t close to
clinical CDR value. But in our proposed
system, we don’t need to select the OD
and OC boundary by creating mask. The
OD is the high intensity part of an eye. So
we easily extract the disc boundary by
medial axis detection used in pixel based
segmentation. Cup segmentation is much
more challenging compared to disc
segmentation due to the presence of high
density vascular architecture in the region
of the optic cup traversing the cup
boundary and this project; and we address
the problem of automatically identifying
true vessels as a post processing step to
vascular structure segmentation. We model
the segmented vascular structure as a
vessel segment graph and formulate the
© 2016, IJSRETS All Rights Reserved
problem of identifying vessels as one of
finding the optimal forest in the graph
given a set of constraints. Then our
experimental
results
show
that
successfully diagnosis the glaucoma and
cardio vascular diseases.
Key words: CRVE, CRAE, IPACHI,
MIPAV, API, MFMK, OCT, CNV
INTRODUCTION
1.1 RETINAL IMAGE
Digital retinal imaging is a
relatively new technology that can be used
to assess patients for diabetic retinopathy.
Automated image processing has the
potential to assist in the early detection of
diabetes, by detecting changes in blood
vessel diameter and patterns in the retina.
An accurate identification of blood vessels
for the purpose of studying changes in the
vessel network that can be utilized for
detecting blood vessel diameter changes
associated with the patho-physiology of
diabetes. A retinal image provides a
snapshot of what is happening inside the
human body. In particular, the state of the
retinal vessels has been shown to reflect
the cardiovascular condition of the body.
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International Journal on Scientific Research in Emerging TechnologieS, Vol. 2 Issue 5, August 2016
ISSN: 1934-2215
Measurements to quantify retinal vascular
structure and properties have shown to
provide good diagnostic capabilities for
the risk of cardiovascular diseases. Retinal
image processing is an important tool in
diagnosing and curing the diseases that
influence the retina.
Diagnosis and treatment of several
disorders affecting the retina require
capturing a sequence of fundus images
using the fundus camera. These images are
to be processed for better diagnosis and
planning of treatment. Retinal image
segmentation is greatly required to extract
certain features that may help in diagnosis
and treatment. Also registration of retinal
images is very useful in extracting the
motion parameters that help in composing
a complete map for the retina as well as in
retinal tracking. Blood vessels in
ophthalmoscope images play an important
role in diagnosis of some serious
pathology on retinal images. Hence,
accurate extraction of vessels is becoming
a main topic of this research area.
Retinal images obtained using
Adaptive Optics have the potential to
facilitate early detection of retinal
pathologies. The retina is the only location
where blood vessels can be directly
visualized. Increasing technology leading
to the development of digital imaging
systems over the past two decades has
revolutionized fundal imaging. Whilst
digital imaging does not still have the
resolution of conventional photography,
modern digital imaging systems offer very
high-resolution images that are sufficient
for most clinical scenarios The retina is the
only location where blood vessels can be
directly visualized non-invasively in vivo.
Increasing technology leading to the
development of digital imaging systems
© 2016, IJSRETS All Rights Reserved
over the past two decades
revolutionized fundal imaging.
has
Fig 1.1.1 Retinal image
Thus, because of its architecture
dictated by its function both diseases of the
eye, as well as diseases that affect the
circulation and the brain can manifest
them in the retina. These include ocular
diseases, such as macular degeneration and
glaucoma, the first and third most
important causes of blindness in the
developed world. A number of systemic
diseases also affect the retina. blindness in
the developed world, hypertensive
retinopathy from cardiovascular disease,
and multiple sclerosis. Thus, on the one
hand, the retina is vulnerable to organspecific and systemic diseases, while on
the other hand, imaging the retina allows
diseases of the eye proper, as well as
complications of diabetes, hypertension
and other cardiovascular diseases, to be
detected, diagnosed and managed.
1.2 OPTIC DISC ANALYSIS
Diabetic retinopathy, hypertension,
glaucoma, and macular degeneration are
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International Journal on Scientific Research in Emerging TechnologieS, Vol. 2 Issue 5, August 2016
ISSN: 1934-2215
nowadays some of the most common
causes of visual impairment and blindness.
Early diagnosis and appropriate referral for
treatment of these diseases can prevent
visual loss. Usually, more than 80% of
global visual impairment is avoidable, and
in the case of diabetes by up to 98%. All of
these diseases can be detected through a
direct and regular ophthalmologic
examination of the risk population.
However, population growth, aging,
physical inactivity and rising levels of
obesity are contributing factors to the
increase of them, which causes the number
of ophthalmologists needed for evaluation
by direct examination is a limiting factor.
So, a system for automatic recognition of
the characteristic patterns of these
pathological cases would provide a great
benefit. Regarding this aspect, optic disc
(OD) segmentation is a key process in
many algorithms designed for the
automatic extraction of anatomical ocular
structures, the detection of retinal lesions,
and the identification of other fundus
features.First, the OD location helps to
avoid false positives in the detection of
exudates
associated
with
diabetic
retinopathy, since both of them are spots
with similar intensity.Secondly, the OD
margin can be used for establishing
standard and concentric areas in which
retinal vessel diameter measurements are
performed by calculating some important
diagnostic indexes for hypertensive
retinopathy, such as central retinal artery
equivalent (CRAE) and central vein
equivalent.
© 2016, IJSRETS All Rights Reserved
Fig 1.2.1 Optic Disc and Cup
The relation between the size of the
OD and the cup (cup-disc-ratio) has been
widely utilized for glaucoma diagnosis. In
addition, the relatively constant distance
between the OD and the fovea is useful for
estimating the location of the macula, area
of the retina related to fine vision.
1.3 BASIC CONCEPTS
1.3.1 BIOMEDICAL ENGINEERING
Biomedical engineering (BME) is
the application of engineering principles
and design concepts to medicine and
biology for healthcare purposes. This field
seeks to close the gap between engineering
and medicine: It combines the design and
problem solving skills of engineering with
medical and biological sciences to advance
healthcare treatment, including diagnosis,
monitoring, and therapy.
Much of the work in biomedical
engineering consists of research and
development, spanning a broad array of
subfields.
Prominent
biomedical
engineering applications include the
development of biocompatible prostheses,
various diagnostic and therapeutic medical
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International Journal on Scientific Research in Emerging TechnologieS, Vol. 2 Issue 5, August 2016
ISSN: 1934-2215
devices ranging from clinical equipment to
micro-implants,
common
imaging
equipment such as MRIs and EEGs,
regenerative tissue growth, pharmaceutical
drugs
and
therapeutic
biological.
Biomedical engineering can be viewed
from two angles, from the medical
applications side and from the engineering
side. A biomedical engineer must have
some view of both sides. As with many
medical specialties
some BME subdisciplines are identified by their
associations with particular systems of the
human body, such as:
 Cardiovascular technology - which
includes all drugs, biologics, and
devices related with diagnostics
and therapeutics of cardiovascular
systems
 Neural technology - which includes
all drugs, biologics, and devices
related with diagnostics and
therapeutics of the brain and
nervous systems
 Orthopaedic technology - which
includes all drugs, biologics, and
devices related with diagnostics
and therapeutics of skeletal
systems
 Cancer
technology - which
includes all drugs, biologics, and
devices related with diagnostics
and therapeutics of cancer
1.4 RETINAL OPHTHALMOLOGY
Generally an image is a twodimensional function f(x,y).The amplitude
of image at any point say f is called
intensity of the image. It is also called the
gray level of image at that point. We need
to convert these x and y values to finite
discrete values to form a digital image.
The input image is a fundus taken from
© 2016, IJSRETS All Rights Reserved
stare data base and drive data base. The
image of the retina is taken for processing
and to check the condition of the person
we need to convert the analog image to
digital image to process it through digital
computer. Each digital image composed of
a finite elements and each finite element is
called a pixel. We can also use filter to
improve selectivity. We can generate a 2D image using single sensor with a
displacement in both directions of plane.
The arrangement used here is for high
precision scanning where film negative is
mounted on to a drum which produces
mechanical rotation. This mechanical
rotation provides displacement in one
direction. A sensor mounted on a lead
screw is used as it provides motion in
perpendicular direction. Using his we can
control mechanical motion effectively and
images are obtained with high resolution.
researchers were working on retinal
images to perform various image
processing tasks for the beneficial of
health sector. Currently many researchers
have proved the automatic assessment of
quality of retinal images taken by fundal
camera with a reference image. Recently,
AO has been combined with scanning
laser
ophthalmoscope
and
optical
coherence tomography (OCT) to obtain
images of retinal microvasculature and
blood flow and three dimensional images
of living cone photoreceptors respectively.
The photoreceptors are one of the
key components of the retinal images,
which act as an indicator to detect or
monitor retinal diseases.Retinal vascular
imaging is currently used in 2 broad areas
of cardiovascular research. First, retinal
imaging is a novel, noninvasive research
tool to probe the role and pathophysiology
of the microvasculature, typically defined
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International Journal on Scientific Research in Emerging TechnologieS, Vol. 2 Issue 5, August 2016
ISSN: 1934-2215
as vessels between 100 and 300 μm in
size, in the development of clinical
cardiovascular disease. Second, retinal
vascular imaging is explored in clinical
settings as a risk stratification tool to aid
clinicians in identifying patients with
micro vascular signs who are at high risk
of future clinical cardiovascular and
cerebrovascular events. A third possibility
is under investigation: retinal vascular
imaging has potential as a surrogate
measure of the micro vascular benefits of
new therapeutic agents in early phase.
1.5 CARDIOVASCULAR DISEASE
Cardiovascular
disease (also
called heart disease) is a class of diseases
that
involve
the heart,
the blood
vessels (arteries, capillaries, and veins) or
both. Cardiovascular disease refers to any
disease that affects the cardiovascular
system,
principally cardiac
disease,
vascular diseases of the brain and kidney,
and peripheral arterial disease. The causes
of cardiovascular disease are diverse
but atherosclerosis and/or hypertension are
the most common. Additionally, with
aging come a number of physiological and
morphological
changes
that
alter
cardiovascular function and lead to
subsequently
increased
risk
of
cardiovascular disease, even in healthy
asymptomatic individuals.
Traditional risk factors such as
hypertension, hyperlipidemia, diabetes,
etc. allow physicians to treat high risk
patients, but a substantial proportion of
cardiovascular disease is not explained by
traditional risk factors alone. In the past
two decades, an increased awareness of the
contribution of coronary micro vascular
disease to the overall heart disease burden
© 2016, IJSRETS All Rights Reserved
has heightened interest in using the retinal
microvasculature as a marker for coronary
disease. The unique perspective offered by
retinal vascular image analysis is used by
an increasing number of researchers and
research groups to address scientific
questions that are difficult to answer
through other means. A key question in
cardiovascular physiology is whether
changes detected in the microvasculature
are early markers of vascular disease
secondary to the disease process or
whether
microvasculature
changes
represent the primary causes that
contribute
etiologically
and
mechanistically to the development of
vascular disease. However, one of the
central unresolved issues in understanding
the patho physiology of hypertension is
whether arteriolar narrowing is antecedent
to and contributes to the development of
hypertension,
or
whether
it
is
consequential to and represents a
secondary adaptation to elevated blood
pressure, or whether both processes occur.
Retinal vascular caliber (CRAE
and CRVE) was analyzed as continuous
variables. We used analysis of covariance
to estimate mean retinal vascular caliber
associated with the presence versus
absence of categorical variables or
increasing quartiles
of
continuous
variables adjusted for age and sex. We
constructed two multivariate models. First,
in model 1, we constructed a model for
CRAE initially including variables that
were significantly associated with CRAE
in age and sex analyses. Final variables
were selected based on a stepwise
backward approach, adjusting for age. We
then constructed a similar model for
CRVE based on variables significantly
associated with CRVE in age and sex
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International Journal on Scientific Research in Emerging TechnologieS, Vol. 2 Issue 5, August 2016
ISSN: 1934-2215
analyses. Second, in model 2, we
preformed supplementary analysis by
additional adjustment for the fellow retinal
vessel caliber (i.e., CRVE was included as
an independent variable in the model for
CRAE, and vice versa), as previously
described to
control
for
potential
confounding from fellow vessel diameter.
We calculated sequential R2 to indicate the
contribution of each independent variable
to the model. Data presented here are
based on results from model 1, as results
from model 2 were largely similar.
1.6 GLAUCOMA
Glaucoma is one of the common
cause of blindness with about 79 million
people in the world is likely to be affected
by glaucoma by the year 2020. It is a
complicated disease in which damage to
the optic nerve leads to progressive,
irreversible vision loss. The optic nerve
head carries from 1 to 1.2 million neurons
which carries visual information from eye
towards the brain.
The ratio of optic disc cup and
neuroretinal rim. It is an important
structural indicator for accessing the
presence and progression of glaucoma.
Various parameters are estimated and
recorded to detect the glaucoma stage
which include cup and OD diameter, area
of OD and rim area etc. Efforts have been
made to automatically detect glaucoma
from 3-D images, but high cost makes
them not appropriate for large scale
screening. Colour fundus imaging (CFI) is
one of the method that is used for
glaucoma assessment.
It has emerged as a preferred
method for large-scale retinal disease
screening and is established for screening
of large-scale diabetic retinopathy. The
work published on automated detection
can be divided in three main
strategies.without disk parameterization. In
this a set of features are computed from
CFI and two-class classification is
employed to find a image as normal or
glaucomatous.
These
features
are
computed without performing OD and cup
segmentation.with disk parameterisation
with monocular CFI, and with disk
parameterization using stereo CFI. In these
strategies which are based on disk
parameterisation, OD and cup regions are
segmented to estimate disk parameters. A
stereo CFI gives better characterisation
compared to monocular CFI.
2.1 SYSTEM DESCRIPTION
© 2016, IJSRETS All Rights Reserved
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International Journal on Scientific Research in Emerging TechnologieS, Vol. 2 Issue 5, August 2016
ISSN: 1934-2215
Preprocessing
Image Acquisition
Gray scale
conversion
Upload Retinal
images
Noise Removal
Sharpen filter
Preprocessed retinal image
Vessel segmentation
Features
extraction
Ellipse fitting
method
Graph theoretical
approach
Optic disc and
cup
segmentation
Similar vessel
pixels
Vessel Segmentation
Map
Retinal diseases
© 2016, IJSRETS All Rights Reserved
Perimeter
Calculation
Vessel
Classification
SVM
classification
Artery-Vein
Classified image
Disease prediction
CRAE and CRVE
measurements
Optic disk and cup
ratio
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International Journal on Scientific Research in Emerging TechnologieS, Vol. 2 Issue 5, August 2016
ISSN: 1934-2215
Fig 2.1.1 Block diagram
inside a human body. By analyzing a
retinal image one can identify cardio
vascular condition of the body.
The risk of cardio vascular diseases
such
as
stroke, hypertension, etc.., can be
3.1 SYSTEM DESIGN
 Upload datasets
found by detailed quantification of a
 Preprocessing
retinal vascular structure and its properties.
 Optic disc and Cup Segmentation
In existing system the identification of true
 Vessel Tracking
vessel becomes difficult due to
 Performance Evaluation
Bifurcations
and
Crossovers.
The
identification of wrong vessel will lead to
wrong clinical diagnose.
4.1 MODULES DESCRIPTION
4.1.1 UPLOAD DATASETS
In this module, we upload the
4.1.2 PREPROCESSING
retinal images. The fundus of the eye is the
In this module, we perform the
interior surface of the eye, opposite
gray scale conversion operation to identify
the lens, and includes the retina, optic
black and white illumination and to
disc, macula and fovea, and posterior pole.
perform the filtering operations using the
The
fundus
can
be
examined
Median filter is to filter out noise that has
by ophthalmoscopyand/or fundus
corrupted image. It is based on a statistical
photography.The retina is a layered
approach. Typical filters are designed for a
structure
with
several
layers
desired frequency response. Median
of neurons interconnected by synapses. In
filtering is a nonlinear operation often used
retina we can identify the vessels.
in image processing to reduce "salt and
The retina is a layered tissue lining
pepper" noise. A median filter is more
the interior of the eye that enables the
effective than convolution when the goal is
conversion of incoming light into a neural
to simultaneously reduce noise and
signal that is suitable for further
preserve edges. Color fundus images often
processing in the visual cortex of the brain.
show important lighting variations, poor
It is thus an extension of the brain.
contrast and noise. In order to reduce these
The ability to image the retina and
imperfections and generate images more
develop techniques for analyzing the
suitable for extracting the pixel features
images is of great interest. As its function
demanded in the classification step.
requires the retina to see the outside world,
the involved ocular structures have to be
4.1.3 OPTIC DISC AND CUP
optically transparent for image formation.
SEGMENTATION
Thus, with proper techniques, the retina is
This module we propose a novel
visible from the outside, making the retinal
OD segmentation method based on regiontissue, and thereby brain tissue, accessible
based active contour model to improve the
for imaging noninvasively.
segmentation on the range of OD
A Retinal image provides a good
instances. The scope of this model is
diagnostic approach of what is happening
enhanced by including image information
© 2016, IJSRETS All Rights Reserved
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International Journal on Scientific Research in Emerging TechnologieS, Vol. 2 Issue 5, August 2016
ISSN: 1934-2215
at a support domain around each point of
interest. The first step is to localize the OD
region and extract a region of interest for
further processing. Next, the edges near
the identified center location in the image
domain are used to estimate the radius of
the circle.
The circle points are identified
using estimated radius and used to
initialize the active contour.The detection
of OD manually by experts is a standard
procedure for this. There have been efforts
for OD segmentation but very few
methods for the cup segmentation. Finding
the cup region helps in finding the cup-todisk (CDR) which is also an important
property for identifying the disease. In this
project, we present an automatic cup
region segmentation method based on
medial axis method.
Cup segmentation: In ellipse
fitting method, the green channel of the
extracted optic disc was processed using
histogram analysis to determine a
threshold value, which segments out the
pixels corresponding to the top 1/3 of the
grayscale intensity, was used to define the
initial contour in the ROI. In order to fit
ellipses specifically while retaining the
efficiency of solution of the linear leastsquares problem.
repeatedly finding the next vessel point
with a scoring function that considers the
pixel intensity and orientation in the
vicinity of the current point in the image.
Bifurcations and crossovers are detected
using some intensity profile.
Tracking for the same vessel then
continues along the most likely path. This
approach of tracking vessels one-at-a-time
does not provide sufficient information for
disambiguating vessels at bifurcations and
crossovers.We can perform the vessel
segmentation using the fuzzy segmentation
algorithm. Fuzzy segmentation is an
effective way of segmenting out objects in
pictures containing both random noise and
shading.
This is illustrated both on
mathematically created pictures and on
some obtained from medical imaging. We
segment vessels in retina with several
widths. Vessels are extracted from retinal
images. In this module we apply the
method aims to identify vessels from
vessel segmentation and represent them in
the form of binary trees for subsequent
vessel measurements. It has two main
steps: 1) identify crossovers, and 2) search
for the optimal forest (set of vessel
trees).A crossover segment occurs when
two different vessels share a segment.
4.1.4 VESSEL TRACKING
Retinal vessel extraction involves
segmentation of vascular structure and
identification of distinct vessels by linking
up segments in the vascular structure to
give complete vessels. One branch of
works, termed vessel tracking, performs
vessel segmentation and identification at
the same time. These methods require the
start points of vessels to be predetermined.
Each vessel is tracked individually by
4.1.5 PERFORMANCE EVALUATION
In this module we evaluate the
performance using graph tracer algorithm
by using the measurements using the
precision and recall measurement. Our
graph tracer algorithm better performance
than the existing approaches. We conclude
that tracing all vessels simultaneously is
better than tracing vessels individually
without current knowledge of other
vessels. Then our algorithm extracts the
© 2016, IJSRETS All Rights Reserved
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International Journal on Scientific Research in Emerging TechnologieS, Vol. 2 Issue 5, August 2016
ISSN: 1934-2215
true blood vessels simultaneously other
than one vessel per time. The Normal cup
to disc ratio range is from 0.1 to 0.3. If the
cup to disc ratio exceeds 0.3 then it
indicates the abnormal condition that is the
presence of glaucoma.
4.1.6
RETINAL
IMAGE
ACQUISITION
Retinal images of humans play an
important role in the detection and
diagnosis of cardio vascular diseases that
including stroke, diabetes, arteriosclerosis,
cardiovascular diseases and hypertension,
to name only the most obvious. Vascular
diseases are often life-critical for
individuals, and present a challenging
public health problem for society.
Therefore, the detection for retinal images
is necessary, and among them the
detection of blood vessels is most
important.
Fig 4.1.6.1 Retinal Image
Acquisition
The alterations about blood vessels,
such as length, width and branching
pattern, can not only provide information
on pathological changes but can also help
to grade diseases severity or automatically
© 2016, IJSRETS All Rights Reserved
diagnose the diseases. In this module, we
upload the retinal images. The fundus of
the eye is the interior surface of the eye,
opposite
the lens,
and
includes
the retina, optic
disc, macula and fovea,
and posterior pole. The fundus can be
examined
by ophthalmoscopyand/or fundus
photography.
The retina is a layered structure
with
several
layers
of neurons interconnected by synapses. In
retina we can identify the vessels. Blood
vessels show abnormalities at early stages
also blood vessel alterations. Generalized
arteriolar and venular narrowing which is
related to the higher blood pressure levels,
which is generally expressed by the
Arteriolar-to-Venular diameter ratio. In
this work, we have constructed a dataset of
images for the training and evaluation of
our proposed method.
This image dataset was acquired
from publically available datasets such as
DRIVE and STAR. Each image was
captured using 24 bit per pixel at 760 x
570 pixels. First, proposed method has
only been tested against normal images
which are easier to distinguish. Second,
some level of success with abnormal
vessel appearances must be established to
recommend clinical usage. As can be seen,
a normal image consists of blood vessels,
optic disc, fovea and the background, but
the abnormal image also has multiple
artifacts of distinct shapes and colors
caused by different diseases. (a) normal
image and (b) diseased image. The nodes
are extracted from the centerline image by
finding the bifurcation points which are
detected by considering pixels with more
than two neighbors and the endpoints or
terminal points by pixels having just one
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ISSN: 1934-2215
neighbor. In order to find the links
between nodes all the bifurcation points
and their neighbors are removed from the
centerline image and as result we get an
image with separate components which are
the vessel segments. On the other hand,
any given link can only connect two.
Vessels segmentation binary mask
is created by detecting vessels edges from
sharpened image. The blood vessels are
marked by the masking procedure which
assigns one to all those pixels which
belong to blood vessels and zero to non
vessels pixels. Final refined vessel
segmentation mask is created by active
contour model. Active contour model, also
called snakes, is a framework in computer
vision for delineating an object outline
from a possibly noisy 2D image. A snake
is
an
energy
minimizing,
deformable spline influenced by constraint
and image forces that pull it towards object
contours and internal forces that resist
deformation. Snakes may be understood as
a special case of the general technique of
matching a deformable model to an image
by means of energy minimization. In two
dimensions,
the active
shape
model represents a discrete version of this
approach, taking advantage of the point
distribution model to restrict the shape
range to an explicit domain learned from a
training set. Finally we provide the
segmentation mask for preprocessed
retinal images.
DISCUSSIONS:
SYSTEM ANALYSIS
5.1 EXISTING SYSTEM
 The
existing
algorithm
is
implemented
in
MATLAB
environment.
© 2016, IJSRETS All Rights Reserved







The
existing
system
was
constructed and tested using
DRIVE datasets.
IPACHI segmentation is applied in
order to extract features that
contain vessel width.
SVM classifiers to improve the
accuracy
of
the
retinal
classification. .
Examination of blood vessels in the
eye allows detection of eye
diseases such as glaucoma and
diabetic retinopathy.
Traditionally, the vascular network
is mapped by hand in a timeconsuming process that requires
both training and skill.
Automating the process allows
consistency, and most importantly,
frees up the time that a skilled
technician or doctor would
normally use for manual screening.
So we can implement automatic
process to examine the blood
vessels to identify the cardio
vascular diseases in retinal images.
The existing method utilizes the
concept of active contours to
remove noise, enhance the image,
track the edges of the vessels,
calculate the perimeter of vessels
and identify the cardio diseases.
Implement infinite perimeter active
contour with hybrid region
information (IPACHI) model to
segment blood vessels and
calculate perimeter of the blood
vessels.
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5.1.1 DISADVANTAGES
 The existing method cannot
provide segmentation accuracy in
retinal and vessel segmentation.
 Wrong identification in bifurcation
and crossover points.
 Existing system only follow the
preprocessing steps but difficult to
identify the vessels with noises in
images.
 Track one vessel at time and also
segment the one route per time.
 Only analyze the risk in
cardiovascular diseases and not to
identify the heart diseases.
5.2 PROPOSED SYSTEM




In proposed system we implement
the sharpen filtering technique to
remove noise.
This process is known as
preprocessing.
The objective is to segment the
cup region by
using both vessel
bends and pallor information.
Only a subset of these points
defines the cup boundary. We refer
to this as relevant vessel bends or rbends. A second problem is that the
anatomy of the OD region is such
that the r-bends are non-uniformly
distributed across a cup boundary
with more points on the top and
bottom they are mostly absent in
the nasal side and very few in
number in the temporal side.
We propose a local interpolating
spline to naturally approximate the
cup boundary in regions where rbends are absent.
The OD region has both thick and
thin vessels. Detecting both
© 2016, IJSRETS All Rights Reserved



reliably is difficult in the presence
of inter-image intensity variations.
During the tracking process, vessel
edge points are detected iteratively
using local grey level statistics and
vessel's continuity properties.
A
method
for
automatic
segmentation of blood vessels in
retinal images. The method is
based on vessel tracking technique.
The fit the crossover profile, the
split is treated as a crossover;
otherwise, it is a bifurcation and
the tracer will follow both paths. It
is greedy because unless a
crossover is identified, it will add
all the connected pixels to the same
vessel.
5.2.1 ADVANTAGES




Efficient post processing step for
tracking cross over points.
Simultaneously identifying the
blood vessels.
Advanced approach for vessel
structure segmentation.
Easily identify the diseases with
improved accuracy rate.
.
ABBREVIATIONS
CRAE
Central
Retinal
Artery
Equivalent
CRVE
Central Retinal Vein Equivalent
MIPAV Medical
Image
Processing
Analysis And Visualization
IPACHI Infinite
Perimeter
Active
Contour With Hybrid Region
Information
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International Journal on Scientific Research in Emerging TechnologieS, Vol. 2 Issue 5, August 2016
ISSN: 1934-2215
SVM
Support Vector Machine
API
CNV
OCT
Application Program Interface
Choroidal Neo Vascularization
Optical Coherence Tomography
MF
Matched Filter
FDOG
First-Order-Derivative Of The
Gaussian
Insight Segmentation And
Registration Toolkit
ITK
MFMK
OD
FABC
Matched Filters With
wavelet Kernels
Optic Disc
Feature-Based Ad boost
Classifier
measurements. And we implement the post
processing step to vessel segmentation.
This step is used to track all true vessels
and find the optimal forest. We can
overcome wrong diagnosis of crossovers
by using simultaneous identification of
blood vessels from retina. The final goal of
the proposed method is to make easier the
early detection of diseases related to the
blood vessels of retina.
Multi
RESULTS AND CONCULSION
We conclude that, our proposed
system implemented successfully with
accurate identification of true vessels to
obtain correct retinal ophthalmology
measurements. This project presented a
solution for glaucoma assessment which
allows derivation of various geometric
parameters of the OD. This is in contrast to
earlier approaches which have largely
focused on the estimation of CDR which
varies considerably within normals. It is
also well recognized that there is
significant intra and inter observer error in
manual assessment with this parameter.
The presented work enables more
comprehensive evaluation of the OD and
performing glaucoma detection using
multiple disk parameters. And our
proposed system implemented successfully
with accurate identification of true vessels
to obtain correct retinal ophthalmology
© 2016, IJSRETS All Rights Reserved
REFERENCE
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