THE AUTOMATIC ARTERIOVENOUS RATIO CALCULATION IN RETINAL IMAGE AS AN INDICATOR OF HYPERTENSIVE RETINOPATHY
Hypertensive retinopathy is a disease caused by the presence of high blood pressure in the retinal blood vessels. At a very severe level, this disease may cause blindness in the eye, therefore early detection of hypertensive retinopthy is very important to prevent the severe eye damage. Manual de...
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Format: | Final Project |
Language: | Indonesia |
Online Access: | https://digilib.itb.ac.id/gdl/view/58296 |
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Institution: | Institut Teknologi Bandung |
Language: | Indonesia |
Summary: | Hypertensive retinopathy is a disease caused by the presence of high blood
pressure in the retinal blood vessels. At a very severe level, this disease may cause
blindness in the eye, therefore early detection of hypertensive retinopthy is very
important to prevent the severe eye damage. Manual detection can be performed
by an ophthalmologist by analyzing the patient’s retinal funduscopy image.
Unfortunately, this manual detection system is ineffective due to the limited
number of ophthalmologists in Indonesia and the length of time required for
diagnosis. Advanced development in computer technology have made automatic
detection for this disease possible to be implemented through digital image
processing and machine learning. One of the characteristics that indicate
hypertensive retinopathy in its early stage is narrowing in retinal. This narrowing
can be evaluate quantitatively through the calculation of arteriovenous ratio
(AVR) in fundus image, which is the ratio between artery and vein diameter that is
usually done manually by an ophthalmologist. In this study, an automatic system
to calculate arteriovenous ratio is being proposed which includes the preprocessing process, blood vessel segmentation, diameter calculation, artery and
vena classification, and lastly AVR calculation. The developed segmentation
algorithm is able to overcome false segmentation due to the presence of exudate
and red lessions, thereby increasing the precision from 65,65% to 82,19%. This
study also successfully identify 18 most importat features that become the main
key to distinguish artery and vein with classification accuracy 97,3% (INSPIREAVR) and 98,5% (AVRDB) using SVM model with RBF kernel. The automatic
AVR measurement was developed based on Knudtson method and its correlation
with manual measurements (???? = 0,649) is comparable to the correlation between
manual measurements by two observers (???? = 0,645). Thus, the automatic system
of AVR calculation developed in this study is considered to be an alternative
solution to replace the AVR calculation that was done manually by an
opthalmologist. |
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