14.ijaest-vol-no-6-issue-no-1-automatic-detection-of-glaucoma-disease-in-eye-077-080

Automatic Detection of Glaucoma Disease In Eye

K.Chiranjeevi Dept.of. ECE

GMR Institute of Technology Rajam,Srikakulam Dist,AP,India [email protected]

Prabhakar Telagarapu Dept.of. ECE

GMR Institute of Technology Rajam,Srikakulam Dist,AP,India [email protected]

Abstract— Glaucoma arises due to the inadequate fluid

flow from the drainage canals of the eye, leading to the

crystallization of the fluid in the cornea and iris regions.

Especially 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 the

structural changes. Currently, these images are analyzed

manually. In this paper, an algorithm is proposed to

automatically compute this accretion from the ultrasound images

of the eye. Apart from improving the contrast of the low

resolution ultrasound image, the algorithm aims to determine the

exact location of the apex point of the anterior chamber region

for efficient angle calculation. It is highly imperative to detect

Glaucoma in its early stages for diagnosis and hence the

algorithm also addresses the importance of precise results with

effective immunity towards speckle noise. This work shows a

technique to improve the efficiency of clinical interpretation of Glaucoma in ultrasound images of the eye.

Keywords- Glaucoma, drainage canals, cornea, and iris region, ultrasound images.

I. INTRODUCTION Glaucoma is a group of diseases that can steal sight

without warning or symptoms. Some of the alarming facts about Glaucoma are (1) Glaucoma is a leading cause of blindness, (2) There is no cure for Glaucoma yet, (3) Everyone is at risk and (4) There may be no symptoms. Nearly half of those with Glaucoma do not know they have the disease. This has been shown repeatedly in studies conducted in developed countries. Glaucoma is a potentially blinding disease that affects 66 million persons worldwide. It is the second leading cause of blindness worldwide. The disease is characterized by typical changes in the optic nerve (the nerve that connects the eye to the brain) with associated visual field defects (the area seen by the eye). Since the outer portion of the visual field is the first to be affected and most types of Glaucoma are asymptomatic the disease is often diagnosed once significant vision/field has been lost. Therefore, early diagnosis is essential so that treatment to halt/slow progression can be instituted. Glaucoma study from Chennai city and rural Tamilnadu reveals that every 1 on 3 patients above 40 years having vision related problems were diagnosed with Glaucoma. When the Glaucoma is understood and managed, humans can continue to live their life fully. Glaucoma is a

group of diseases of the optic nerve involving loss of retinal ganglion cells in a characteristic pattern of optic neuropathy. Eye has pressure just like blood, and when this intraocular pressure (IOP) increases to dangerous levels, it damages the optic nerve. This can result in decreased peripheral vision and, eventually, blindness. Glaucoma is similar to ocular hypertension but with accompanying optic nerve damage and vision loss. Although raised intraocular pressure is a significant risk factor for developing Glaucoma, there is no set threshold for intraocular pressure that causes Glaucoma. One person may develop nerve damage at a relatively low pressure, while another person may have high eye pressures for years and yet never develop damage. Untreated Glaucoma leads to permanent damage of the optic nerve and resultant visual field loss, which can progress to blindness. In this paper, the Closed Angle Glaucoma is addressed, in which the fluid at the front of the eye cannot reach the angle and leave the eye. The angle gets blocked by part of the iris. People with this type of Glaucoma have a sudden increase in eye pressure. Symptoms include severe pain and nausea, as well as redness of the eye and blurred vision. This status requires immediate medical attention.

II. CURRENT TECHNIQUES TO DETECT GLAUCOMA

Regular Glaucoma check-ups include two routine eye tests: Tonometry and Ophthalmoscopy.

2.1 Tonometry

The Tonometry test measures the inner pressure of the eye. Usually drops are used to numb the eye. Then the doctor or technician will use a special device, called Tonometer, which measures the eye’s pressure. The normal range of this pressure is in between 10mmHg and 22mmHG.

2.2 Ophthalmoscopy

Ophthalmoscopy is used to examine the inside of the eye, especially the optic nerve. In a darkened room, the doctor will magnify the eye by using an ophthalmoscope (an instrument with a small light on the end). This helps the doctor look at the shape and color of the optic nerve.If the pressure in the eye is not in the normal range (10mmHg to 22mmHg), or if the optic nerve looks unusual, then one or two special Glaucoma tests will be done. These two tests are called Perimetry and Gonioscopy.

K.Chiranjeevi et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 6, Issue No. 1, 077 - 080

ISSN: 2230-7818 @ 2011 http://www.ijaest.iserp.org. All rights Reserved. Page 77

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2.2.1 Perimetry

The Perimetry test is also called a visual field test. During this test, the patient will be asked to look straight ahead and then indicate when a moving light passes his/her peripheral (or side) vision. This helps draw a ―map‖ of patient’s vision.

2.2.2 Gonioscopy

Gonioscopy is a painless eye test that checks if the angle where the iris meets the cornea is open or closed, showing if either open angle or closed angle Glaucoma is present.

Figure 2.1 Clinical parameters in Gonioscopic images

2.3 Manual Calculation of AOD from Ultrasound Images of

the Eye

In this method, the doctor examines the Ultrasound image of the patient’s eye and he/she estimates the angle between the iris and the cornea. If the estimated angle is less than 190, then the eye is treated as Glaucoma affected, otherwise as normal eye. But the manual estimation may be wrong.

2.4 Disadvantages of Current Techniques

Manual analysis of eye images is fairly time consuming, and the accuracy of parameter measurements varies between experts. For this reason, an algorithm is developed to automatically analyze eye ultrasound images. The proposed algorithm is expected to reduce the processing time taken by the existing techniques of manual/computer-

based algorithms without compromising on the speed, accuracy, sensitivity, cost and compatibility of the product. Ultrasound images of eye are usually associated with poor resolution, poor contrast, noise and divaricated anterior chamber edges. Algorithm is proposed to effectively mitigate the above challenges.

III. ALGORITHM DESIGN This algorithm describes a new method to detect

features in ultrasound images, which shows good performance in detection of difficult features. The developed techniques make 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 other signal processing techniques are used 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. The complete algorithm is shown in Figure 3.1.

IV. RESULTS The angles between iris and cornea for Ultrasound images of different patients are calculated and decision about the presence of Glaucoma is made follows.

4.1 Results for Ultrasound Image 1

Figure 4.1: Input Ultrasound Image 1



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TABLE :1 Comparison of Results with Current Techniques for Image 1

As the Intraocular Pressure is same as the threshold value, the status of Glaucoma cannot be decided using Tonometry. As the Perimetry method is the visual filed of the patient, in this method also the status of Glaucoma cannot be decided. Even though AOD is greater than 190 in the case of Gonioscopy and direct view, which decided that Glaucoma is present, the developed algorithm detected Glaucoma, as AOD is less than 190.




As the Perimetry method is the visual filed of the

patient, in this method also the status of Glaucoma cannot be decided. Even though Tonometry and Gonioscopy decided that Glaucoma is present, the developed algorithm detected no Glaucoma, which prevents the unnecessary surgery.




In this case, all methods including developed algorithm decided that Glaucoma is present. Perimetry method also decided that Glaucoma is present, as the visual field of the patient’s eye is very poor.





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In this case, all methods including developed

algorithm decided that Glaucoma is present.

In ultrasound imaging, speckle noise severely degrades the visual quality of the image. In order to achieve high accuracy when extracting features, speckle must be filtered without destroying any important characteristics in the image. In the developed algorithm, speckle noise was reduced using a multi-scale algorithm. It is worthwhile to investigate a different speckle reduction technique that do not depend on the selection of the window size and that can be used on the ultrasound images of the eye before edge enhancement. One easy way to reduce speckle is to average multiple uncorrelated images of the same object obtained from different spatial positions. However, this procedure is computationally costly and will increase the processing time of the algorithm. It seems to design an algorithm for fine enhancement, which does not require the selection of a fixed window size and to reduce speckle noise based on each pixel surrounding area. However, for images with very poor resolution, more iteration can be applied until all pixels lying in the same local neighborhood have similar intensity values close to the initial spike value. If this technique shows improvement in speckle reduction and does not destroy edges in the original image, the enhancement process in the algorithm will require less iteration, resulting in a considerable reduction of the processing time.

V. CONCLUSION This thesis has developed an algorithm to

automatically identify clinical features in ultrasound images of the eye. The algorithm computes the AOD 500 used to measure the presence and severity of glaucoma. Overall, the algorithm predictions are very advantageous compared to the technologist’s observation. In the processed images, features were correctly identified in 97% of the cases. 3% of images presented inaccurate approximation of the clinical parameters. The difficulties encountered in measuring clinical parameters, which are associated with the speckle noise, poor contrast, poor resolution, and weak edge delineation present in the processed ultrasound images, are accurately eliminated. However, the designed algorithm failed for a few of images, where more noise is present. The algorithm was designed with a goal of robustness through the use of enhancement process on the original image, and by validation of the proper segmentation of the anterior chamber at each step. Overall, the benefit of this work is the ability of algorithm to reduce the processing time and improve processing consistency for each

patient’s ultrasound image, leading hopefully to an increase in efficiency and a reduction of cost.

ACKNOWLEDGMENT The Authors wish to thank Guru Kamesh Reddy, JTO,AP,India. For his sugessions which have the improved the presentation of the material in this paper.

REFERENCES

[1] R. Youmaran, P. Dicorato, R. Munger, T.Hall, A. Adler - Automatic Detection of Features in Ultrasound Images of the Eye, IMTC 2005 – Instrumentation and Measurement Technology Conference, Ottawa, Canada, 17-19 May 2005.

[2] Xiaoyang Song, Keou Song, Yazhu Chen - A Computer-based Diagnosis System for Early Glaucoma Screening, Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, September 1-4, 2005.

[3] Rafael C. Gonzalez, Richard E. Woods, ―Digital Image Processing‖, Second Edition, Pearson Education Asia Publications.

[4] Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins, ―Digital Image Processing using MATLAB®

‖, Pearson Education Asia Publications.

[5] Glaucoma Research Foundation - funding innovative research to find a cure for Glaucoma.251 Post Street, Suite 600, San Francisco, CA.

K.Chiranjeevi received B.E Electronics and Communication Engineering from J.N.T.U. Anantapur, and M.Tech Instrumentation and Control from JNTU Kakinada, He is working as Asst. Professor in GMR Institute of Technology. His research interests are in Signal Processing and Image Processing.

T.Prabhakar received M.Tech degree from Jawarlal Nehru Technological University Kakinada, Andhra Pradesh, India. B.Tech degree in Electronics and Communication Engineering from SIR C.R.Reddy College of Engineering, Eluru, Andhra Pradesh, India. He is joined as Lecturer in the Department. Of Electronics and Communication Engineering at GMR Institute of Technology, Rajam, Srikakulam

District, Andhra Pradesh, India in 2002. Prior to join in this Institute he worked as a Service Engineer in Machine Diagnostics and Deployed to work at National Remote Sensing agency, Department. Of. Space, Hyderabad for 1 year 1 month and Trainee Programmer in Indo Tech Computers, for 8 months in Hyderabad. He is presently working as Senior. Assistant Professor in the Department. Of Electronics and Communication Engineering at GMR Institute of Technology. Having Total experience is 12 years out of which 10 years in Teaching (GMRIT) and 2 Years in Industry. His research interests are Communication, Signal Processing and Image Processing. He has published

10 Technical papers in various International journals and conferences. He is a life member of ISTE Since 2002.



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14.ijaest-vol-no-6-issue-no-1-automatic-detection-of-glaucoma-disease-in-eye-077-080

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