matlab code for glaucoma detection

[ddkarad]

matlab code for glaucoma detection

ABSTRACT

In closed angled Glaucoma, fluid pressure in the eye increases because of inadequate fluid flow between the iris and the cornea. One important technique to assess patients at risk of glaucoma is to analyze ultrasound images of the eye to detect abnormal structural changes. Currently theseimages are analyzed manually .This thesis presents an algorithm to automatically identify and measure clinically important features in ultrasound images of the eye. The main challenge is stable detection of features in the presence of ultrasound speckle noise; an algorithm is developed to address this using multiscale analysis and template matching. Tests were performed by comparison of results with eighty images of glaucoma patients and normals against the feature locations identified by a trained technologist. In 5% of cases, the algorithm could not analyze the images; in the remaining cases, features were correctly identified (within 97.5 m) in 97% of images. This work shows promise as a technique to improve the efficiency of clinical interpretation of ultrasound images of the eye.

INTRODUCTION

Glaucoma is one of the leading causes of blindness. In closed angled Glaucoma, fluid pressure in the eye increases because of inadequate fluid flow between the iris and the cornea. To illustrate the structural changes due to this condition, cross sectional ultrasound images are shown in Figure 1. Figure 1 (a) and (b) show ultrasound images of a healthy and a diseased eye, respectively. Figure 1 (b) shows an eye with a closed-angle as a result of the fluid pressure in the eye that causes damage and eventually death of nerve fibers responsible for vision . One important technique to assess patients at risk of glaucoma is to analyze ultrasound images of the eye to detect the structural changes that reduce the flow of fluids out of the eye. Usually, sequences of ultrasound images of the eye are analyzed manually; a trained technologist determines anatomical feature locations and measures the relevant clinical parameters. We are unaware of any work to develop an automated algorithm to analyze these images. The main features within the eye of clinical interest are: the sclera, a dense, fibrous opaque white outer coat enclosing the eyeball except the part covered by the cornea; the scleral spur, a small triangular region in a meridional section of the sclera tissue with its base along the inner surface of the sclera; the anterior chamber, the region bounded by the posterior surface of the cornea and the central part of the lens; and, the trabecular-iris recess, the apex point between the sclera region and the iris. Manual analysis of eye images is fairly time consuming, and the accuracy of parameter measurements varies between experts. To address these issues, the goal of this thesis is to develop an algorithm to automatically analyze eye ultrasound images and locate all the features of interest within the image. The difficulties in measuring these parameters are associated with noise, poor contrast,poor resolution, and weak edge (boundary) delineation inherently present in ultrasound images. We anticipate that this scheme will reduce the processing time currentlytaken by the technologist to analyze patient images and extract the clinical parameters of interest.

1.1 THESIS CONTRIBUTION

This thesis describes a new method to detect features in ultrasound images, which shows good performance in detection of difficult features. The developed technique makes use of major image processing methods and fundamentals. In order to calculate the clinical parameters of interest, new region classification and segmentation techniques are developed as well as some signal processing to locate the scleral spur. The ultrasound images of the eye are very noisy, with poor resolution and weak edge delineation, which required the development of a three step method to overcome these challenges.

1.2 THESIS OUTLINE

The thesis is organized as follows: Chapter 2 and Chapter 3 present an introduction to Ultrasound imaging and Ultrasound biomicroscopy (UBM) respectively. Chapter 4 describes the Glaucoma disease and presents the major features of interest in the ultrasound image of the eye that are used by the algorithm to compute the trabecular-iris angle. Overviews of the image processing techniques used for feature identification are introduced in Chapter 5. Chapter 6 describes the automated algorithm for feature detection and extraction, including speckle reduction methods, non-linear contrast and edge enhancement, template correlation, regionsegmentation and classification, and computation of clinical parameters. Chapter 7 presents experimental results obtained from testing the algorithm. Finally, Chapter 8 presents a discussion of previous work done in this field and concludes this work.

[ravindra sp]

matlab code for glaucoma detection

ABSTRACT

In closed angled Glaucoma, fluid pressure in the eye increases because of inadequate fluid flow between the iris and the cornea. One important technique to assess patients at risk of glaucoma is to analyze ultrasound images of the eye to detect abnormal structural changes. Currently theseimages are analyzed manually .This thesis presents an algorithm to automatically identify and measure clinically important features in ultrasound images of the eye. The main challenge is stable detection of features in the presence of ultrasound speckle noise; an algorithm is developed to address this using multiscale analysis and template matching. Tests were performed by comparison of results with eighty images of glaucoma patients and normals against the feature locations identified by a trained technologist. In 5% of cases, the algorithm could not analyze the images; in the remaining cases, features were correctly identified (within 97.5 m) in 97% of images. This work shows promise as a technique to improve the efficiency of clinical interpretation of ultrasound images of the eye.
INTRODUCTION
Glaucoma is one of the leading causes of blindness. In closed angled Glaucoma, fluid pressure in the eye increases because of inadequate fluid flow between the iris and the cornea. To illustrate the structural changes due to this condition, cross sectional ultrasound images are shown in Figure 1. Figure 1 (a) and (b) show ultrasound images of a healthy and a diseased eye, respectively. Figure 1 (b) shows an eye with a closed-angle as a result of the fluid pressure in the eye that causes damage and eventually death of nerve fibers responsible for vision . One important technique to assess patients at risk of glaucoma is to analyze ultrasound images of the eye to detect the structural changes that reduce the flow of fluids out of the eye. Usually, sequences of ultrasound images of the eye are analyzed manually; a trained technologist determines anatomical feature locations and measures the relevant clinical parameters. We are unaware of any work to develop an automated algorithm to analyze these images. The main features within the eye of clinical interest are: the sclera, a dense, fibrous opaque white outer coat enclosing the eyeball except the part covered by the cornea; the scleral spur, a small triangular region in a meridional section of the sclera tissue with its base along the inner surface of the sclera; the anterior chamber, the region bounded by the posterior surface of the cornea and the central part of the lens; and, the trabecular-iris recess, the apex point between the sclera region and the iris. Manual analysis of eye images is fairly time consuming, and the accuracy of parameter measurements varies between experts. To address these issues, the goal of this thesis is to develop an algorithm to automatically analyze eye ultrasound images and locate all the features of interest within the image. The difficulties in measuring these parameters are associated with noise, poor contrast,poor resolution, and weak edge (boundary) delineation inherently present in ultrasound images. We anticipate that this scheme will reduce the processing time currentlytaken by the technologist to analyze patient images and extract the clinical parameters of interest.
1.1 THESIS CONTRIBUTION
This thesis describes a new method to detect features in ultrasound images, which shows good performance in detection of difficult features. The developed technique makes use of major image processing methods and fundamentals. In order to calculate the clinical parameters of interest, new region classification and segmentation techniques are developed as well as some signal processing to locate the scleral spur. The ultrasound images of the eye are very noisy, with poor resolution and weak edge delineation, which required the development of a three step method to overcome these challenges.
1.2 THESIS OUTLINE
The thesis is organized as follows: Chapter 2 and Chapter 3 present an introduction to Ultrasound imaging and Ultrasound biomicroscopy (UBM) respectively. Chapter 4 describes the Glaucoma disease and presents the major features of interest in the ultrasound image of the eye that are used by the algorithm to compute the trabecular-iris angle. Overviews of the image processing techniques used for feature identification are introduced in Chapter 5. Chapter 6 describes the automated algorithm for feature detection and extraction, including speckle reduction methods, non-linear contrast and edge enhancement, template correlation, regionsegmentation and classification, and computation of clinical parameters. Chapter 7 presents experimental results obtained from testing the algorithm. Finally, Chapter 8 presents a discussion of previous work done in this field and concludes this work.

[sneha shrama]

matlab code for glaucoma detection

Abstract

This study proposes a semi automated method for glaucoma detection using CDR and ISNT ratio of a fundus image. CDR (Cup to Disc Ratio) is ratio of area of Optic Cup to area of Optic Disc. For a patient with glaucoma Optic Cup size increases while the Optic Disc size remains same and hence CDR will be high for glaucoma patient than normal fundus image. The ROI of green plane is taken and K-Means clustering technique is recursively applied and Optic Disc and Optic Cup is segmented. Through elliptic fiiting, area of Optic Disc and Cup is determined and hence CDR is calculated. ISNT is another parameter used for the diagnosis of glaucoma which is determined through the ratio of area of blood vessels in Inferior Superior to Nasal Temporal side. Blood vessels will shift to Nasal side for glaucoma patients, hence value will be less for
glaucoma patient than normal fundus image. Matched filter and Local entropy thresholding is applied to extract blood vessels. The code is programmed in C++ using OpenCV library functions. OpenCV (Open Source Computer Vision Library) is a library of programming functions developed by Intel. Core, highgui, imgproc, ml are the main libraries used from OpenCV. The optimized functions in OpenCV increase the speed of operation and is very much suitable for real time mass screening purpose. A batch of 50 retinal images (25 normal set and 25 abnormal set) obtained from the Aravind Eye Hospital, is used to assess the performance of the proposed system.

[anokh.scorpio]

matlab code for glaucoma detection

Abstract

This study proposes a semi automated method for glaucoma detection using CDR and ISNT ratio of a fundus image. CDR (Cup to Disc Ratio) is ratio of area of Optic Cup to area of Optic Disc. For a patient with glaucoma Optic Cup size increases while the Optic Disc size remains same and hence CDR will be high for glaucoma patient than normal fundus image. The ROI of green plane is taken and K-Means clustering technique is recursively applied and Optic Disc and Optic Cup is segmented. Through elliptic fiiting, area of Optic Disc and Cup is determined and hence CDR is calculated. ISNT is another parameter used for the diagnosis of glaucoma which is determined through the ratio of area of blood vessels in Inferior Superior to Nasal Temporal side. Blood vessels will shift to Nasal side for glaucoma patients, hence value will be less for
glaucoma patient than normal fundus image. Matched filter and Local entropy thresholding is applied to extract blood vessels. The code is programmed in C++ using OpenCV library functions. OpenCV (Open Source Computer Vision Library) is a library of programming functions developed by Intel. Core, highgui, imgproc, ml are the main libraries used from OpenCV. The optimized functions in OpenCV increase the speed of operation and is very much suitable for real time mass screening purpose. A batch of 50 retinal images (25 normal set and 25 abnormal set) obtained from the Aravind Eye Hospital, is used to assess the performance of the proposed system.

[tanmay]

matlab code for glaucoma detection

How is glaucoma detected?

Regular eye examinations are the best way to detect glaucoma early.

A glaucoma test usually includes the following:

optic nerve check with an ophthalmoscope
eye pressure check (tonometry)
visual field assessment if needed - this tests the sensitivity of the side vision, where glaucoma strikes first

Can glaucoma be treated?

Although there is no cure for glaucoma it can usually be controlled and further loss of sight either prevented or at least slowed down.

Treatments include:

Eyedrops - these are the most common form of treatment and must be used regularly. In some cases pills are prescribed. The drops can be varied to best suit the patient and the type of glaucoma.
Laser (laser trabeculoplasty) - this is performed when eye drops do not stop deterioration in the field of vision. In many cases eye drops will need to be continued after laser. Laser does not require a hospital stay.
Surgery (trabeculectomy) - this is performed usually after eye drops and laser have failed to control the eye pressure. A new channel for the fluid to leave the eye is created.
Treatment can save remaining vision but it does not improve eye sight.

[parneet89]

hi
i am mtech student working on glaucoma detection.. if u have any code regarding glaucoma detection please send to my mail..it will so useful to me..

my mail id :[email protected]

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please send code for glaucoma detection ..it will help me alot..

thank you

[norek]

Hi sir/Madam

i am M.Tech student , i am doing project on detection of glaucoma.so, i want matlab code for detection of glaucoma which is very useful for me..

thank you

[pragie]

Hello,

I want matlab coding for glaucoma detection.. Which produces accuracy value while using SVM classifier..

[[email protected]]

Hello Sir/Madam,
I request you to provide me the code as it will be helpful for my project.
Thank you

[vijichit]

pls send[/size][/font] matlablab code for glaucoma detection