Glaucoma Screening Test By Segmentation of Optical Disc& Cup Segmentation Usi...IJERA Editor
Glaucoma is one of the most common causes of blindness and it is becoming even more important considering
the ageing society. Because healing of died retinal nerve fibers is not possible early detection and prevention is
essential. Robust, automated mass-screening will help to extend the symptom-free life of affected patients. We
devised a novel, automated, appearance based glaucoma classification system that does not depend on
segmentation based measurements. Our purely data-driven approach is applicable in large-scale screening
examinations. The proposed segmentation methods have been evaluated in a database of 650 images with optic
disc and optic cup boundaries manually marked by trained professionals. Our expected Experimental results
may be average overlapping error of 9.5% and 24.1% in optic disc and optic cup segmentation, respectively.
Glaucoma is a chronic eye disease in which the optic nerve head is progressively damaged which leads to loss of
vision. Early diagnosis and treatment is the key to preserving sight in people with glaucoma. Current tests using
intraocular pressure (IOP) are not sensitive enough for population based glaucoma screening. Assessment of the
damaged optic nerve head is both more promising, and superior to IOP measurement or visual field testing. This paper
presents superpixel classification based optic disc and optic cup segmentation for glaucoma screening. In optic disc
segmentation, histograms and centre surround statistics are used to classify each superpixel as disc or non-disc. For optic
cup segmentation, in addition to the histograms and centre surround statistics, the location information is also included
into the feature space to boost the performance. The segmented optic disc and optic cup are used to compute the CDR
for glaucoma screening. The Cup to Disc Ratio (CDR) of the color retinal fundus camera image is the primary identifier
to confirm Glaucoma given patient.
Keywords — IOP measurement, optic cup segmentation, optic disc segmentation, CDR.
FUZZY CLUSTERING BASED GLAUCOMA DETECTION USING THE CDR sipij
Glaucoma is a serious eye disease, overtime it will result in gradual blindness. Early detection of thedisease will help prevent against developing a more serious condition. A vertical cup-to-disc ratio which isthe ratio of the vertical diameter of the optic cup to that of the optic disc, of the fundus eye image is an important clinical indicator for glaucoma diagnosis. This paper presents an automated method for the extraction of optic disc and optic cup using Fuzzy C Means clustering technique combined with
thresholding. Using the extracted optic disc and optic cup the vertical cup-to-disc ratio was calculated.
The validity of this new method has been tested on 365 colour fundus images from two different publicly
available databases DRION, DIARATDB0 and images from an ophthalmologist. The result of the method
seems to be promising and useful for clinical work.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Tool-Matlab
Drive database is considered for extraction of features and testing images to detect the ground truth and even images from internet.
The image features like Blood vessel area,optic disk area,entropy,energy are calculated.
Glaucoma Screening Test By Segmentation of Optical Disc& Cup Segmentation Usi...IJERA Editor
Glaucoma is one of the most common causes of blindness and it is becoming even more important considering
the ageing society. Because healing of died retinal nerve fibers is not possible early detection and prevention is
essential. Robust, automated mass-screening will help to extend the symptom-free life of affected patients. We
devised a novel, automated, appearance based glaucoma classification system that does not depend on
segmentation based measurements. Our purely data-driven approach is applicable in large-scale screening
examinations. The proposed segmentation methods have been evaluated in a database of 650 images with optic
disc and optic cup boundaries manually marked by trained professionals. Our expected Experimental results
may be average overlapping error of 9.5% and 24.1% in optic disc and optic cup segmentation, respectively.
Glaucoma is a chronic eye disease in which the optic nerve head is progressively damaged which leads to loss of
vision. Early diagnosis and treatment is the key to preserving sight in people with glaucoma. Current tests using
intraocular pressure (IOP) are not sensitive enough for population based glaucoma screening. Assessment of the
damaged optic nerve head is both more promising, and superior to IOP measurement or visual field testing. This paper
presents superpixel classification based optic disc and optic cup segmentation for glaucoma screening. In optic disc
segmentation, histograms and centre surround statistics are used to classify each superpixel as disc or non-disc. For optic
cup segmentation, in addition to the histograms and centre surround statistics, the location information is also included
into the feature space to boost the performance. The segmented optic disc and optic cup are used to compute the CDR
for glaucoma screening. The Cup to Disc Ratio (CDR) of the color retinal fundus camera image is the primary identifier
to confirm Glaucoma given patient.
Keywords — IOP measurement, optic cup segmentation, optic disc segmentation, CDR.
FUZZY CLUSTERING BASED GLAUCOMA DETECTION USING THE CDR sipij
Glaucoma is a serious eye disease, overtime it will result in gradual blindness. Early detection of thedisease will help prevent against developing a more serious condition. A vertical cup-to-disc ratio which isthe ratio of the vertical diameter of the optic cup to that of the optic disc, of the fundus eye image is an important clinical indicator for glaucoma diagnosis. This paper presents an automated method for the extraction of optic disc and optic cup using Fuzzy C Means clustering technique combined with
thresholding. Using the extracted optic disc and optic cup the vertical cup-to-disc ratio was calculated.
The validity of this new method has been tested on 365 colour fundus images from two different publicly
available databases DRION, DIARATDB0 and images from an ophthalmologist. The result of the method
seems to be promising and useful for clinical work.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Tool-Matlab
Drive database is considered for extraction of features and testing images to detect the ground truth and even images from internet.
The image features like Blood vessel area,optic disk area,entropy,energy are calculated.
GLAUCOMA is a chronic eye disease that can damage optic nerve. According to WHO It
is the second leading cause of blindness, and is predicted to affect around 80 million people by 2020.
Development of the disease leads to loss of vision, which occurs increasingly over a long period of
time. As the symptoms only occur when the disease is quite advanced so that glaucoma is called the
silent thief of sight. Glaucoma cannot be cured, but its development can be slowed down by
treatment. Therefore, detecting glaucoma in time is critical. However, many glaucoma patients are
unaware of the disease until it has reached its advanced stage. In this paper, some manual and
automatic methods are discussed to detect glaucoma. Manual analysis of the eye is time consuming
and the accuracy of the parameter measurements also varies with different clinicians. To overcome
these problems with manual analysis, the objective of this survey is to introduce a method to
automatically analyze the ultrasound images of the eye. Automatic analysis of this disease is much
more effective than manual analysis.
Recent advances in glaucoma includes all the newer trends in the fields of measurement of increased IOP, assessment of anterior chamber angle and depth assessment and lastly the assessment of changes in the optic nerve head and RNFL thickness.
Recent advances in diagnosis of glaucoma includes all the newer trends in the fields of measuring increased IOP, anterior chamber angle and depth assessment and optic nerve head assessment including RNFL thickness.
Glaucoma Disease Diagnosis Using Feed Forward Neural Network ijcisjournal
Glaucoma is an eye disease which damages the optic nerve and or loss of the field of vision which leads to
complete blindness caused by the pressure buildup by the fluid of the eye i.e. the intraocular pressure
(IOP). This optic disorder with a gradual loss of the field of vision leads to progressive and irreversible
blindness, so it should be diagnosed and treated properly at an early stage. In this paper,
thedaubechies(db3) or symlets (sym3)and reverse biorthogonal (rbio3.7) wavelet filters are employed for
obtaining average and energy texture feature which are used to classify glaucoma disease with high
accuracy. The Feed-Forward neural network classifies the glaucoma disease with an accuracy of 96.67%.
In this work, the computational complexity is minimized by reducing the number of filters while retaining
the same accuracy.
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Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
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Semelhante a Glaucoma Screening Test by Segmentation of Optical Disc & Cup Using MATLAB
GLAUCOMA is a chronic eye disease that can damage optic nerve. According to WHO It
is the second leading cause of blindness, and is predicted to affect around 80 million people by 2020.
Development of the disease leads to loss of vision, which occurs increasingly over a long period of
time. As the symptoms only occur when the disease is quite advanced so that glaucoma is called the
silent thief of sight. Glaucoma cannot be cured, but its development can be slowed down by
treatment. Therefore, detecting glaucoma in time is critical. However, many glaucoma patients are
unaware of the disease until it has reached its advanced stage. In this paper, some manual and
automatic methods are discussed to detect glaucoma. Manual analysis of the eye is time consuming
and the accuracy of the parameter measurements also varies with different clinicians. To overcome
these problems with manual analysis, the objective of this survey is to introduce a method to
automatically analyze the ultrasound images of the eye. Automatic analysis of this disease is much
more effective than manual analysis.
Recent advances in glaucoma includes all the newer trends in the fields of measurement of increased IOP, assessment of anterior chamber angle and depth assessment and lastly the assessment of changes in the optic nerve head and RNFL thickness.
Recent advances in diagnosis of glaucoma includes all the newer trends in the fields of measuring increased IOP, anterior chamber angle and depth assessment and optic nerve head assessment including RNFL thickness.
Glaucoma Disease Diagnosis Using Feed Forward Neural Network ijcisjournal
Glaucoma is an eye disease which damages the optic nerve and or loss of the field of vision which leads to
complete blindness caused by the pressure buildup by the fluid of the eye i.e. the intraocular pressure
(IOP). This optic disorder with a gradual loss of the field of vision leads to progressive and irreversible
blindness, so it should be diagnosed and treated properly at an early stage. In this paper,
thedaubechies(db3) or symlets (sym3)and reverse biorthogonal (rbio3.7) wavelet filters are employed for
obtaining average and energy texture feature which are used to classify glaucoma disease with high
accuracy. The Feed-Forward neural network classifies the glaucoma disease with an accuracy of 96.67%.
In this work, the computational complexity is minimized by reducing the number of filters while retaining
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Semelhante a Glaucoma Screening Test by Segmentation of Optical Disc & Cup Using MATLAB (20)
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
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Glaucoma Screening Test by Segmentation of Optical Disc & Cup Using MATLAB
1. Glaucoma Screening Test by
Segmentation of Optical Disc &
Cup Using MATLAB
Showcased by: Hussain S
2. AGENDA
1.What is Glaucoma & it’s
screening test?
2.What is meant by Optical
disc and Cup?
3.Why segmentation is
necessary?
4.Architectural Design of
System
5.Conclusion
3. What is meant by
Glaucoma?
• Glaucoma is a general term used to
describe a group of eye disorders
that damage your optic nerve.
• It’s the most common form of optic
nerve damage leading to vision loss.
• The name of this pressure is
intraocular pressure (IOP), or eye
pressure.
5. What is meant by Optical
Cup & Disc?
• Optic cup is the bright central part of the
optic disc and which is an essential
parameter for detecting glaucoma.
• Compared to the optic disc, the optic cup
is smaller in size.
• Glaucoma usually presents as optic disc
cupping without pallor.
• This can help differentiate glaucoma
from other ophthalmic pathologies that
can change the color of the optic nerve.
• A normal optic disc has a pink-orange
appearance with a pale center where the
optic cup is.
6. Glaucoma Screening Test
• Glaucoma tests can determine
whether the optic nerve is
damaged, which may cause
vision problems.
• An ophthalmologist may
recommend a combination of
quick, painless procedures.
• Tests include angle test, corneal
thickness test, dilated eye exam,
eye pressure check, optic nerve
imaging and visual field testing.
6
8. 8
Image
Acquisition
Image
Preprocessing
Optic Disc using disc
Photography (DP) and
Optical Coherence
Tomography(OCT).
K-means/Gabor
Filter/ONH &
RNHL
Segmentation.
Morphological
Features:
1)Erode,
2)Dilate.
Optic Cup
Segmentation :
Using
Binarization.
Cup-to-Disc
ratio(CDR
Calculation)
Glaucoma
Diagnosis.
Architectural
Design of
System.
9. Architectural Design of System:
o Image Acquisition: It can be broadly defined as action of retrieving from source.
o Image Preprocessing: It mainly focuses on noise remioval process .Such as image filteration,
color contrast enhancement.
o Optic Disc Segmentation: Using Disc Tomography (DT) and Optical Coherence Tomography(OCT)
o K-means Clustering: It is unsupervised clustering algorithm input data points into multiple class,
inherent distance.
o Gabour Filter: Used to accurate the image boundary.
o ONH and RNFL segmentation:Dividing and analyzing specific layers or regions of the optic nerve
head (ONH) and retinal nerve fiber layer (RNFL).
o Morphological Features: For description of shape of Object/ Regions. There are two operations
performed, Erosion and Dilation.
o Optic Cup Segmentation: Using Binarization.
o Cup-to-Disc ratio(CDR Calculation): If CDR value grater than threshold, then it is glaucomatous,
otherwise Healthy eye.
o Glaucoma Diagnosis: Through Medical Image acquisition, we can detect the Glaucoma of the
human eye.
9
10. 10
Conclusion
From Glaucoma,screening test of
Optic Disc and Cup using Image
processing technique (MATLAB) for
diagnosis is done.
Through Cup-to-disc calculation/ratio
(CDR): Distance of optic cup is
measured, from which types of
glaucoma can be detected (as given
in image) and accordingly further
diagnosis is done.