Document Type : Original Paper
Authors
1
M.Sc. Student in Medical Physics, Tehran University of Medical Sciences, Tehran, Iran
2
Assistance Professor of Medical Physics and Biomedical Engineering Dept., Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences, Tehran, Iran
3
Associate Professor of Medical Physics and Biomedical Engineering Department, Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences, Tehran, Iran
4
Associate Professor, Ophthalmology Dept., Iran Eye Research Center, Iran University of Medical Sciences, Tehran, Iran
5
M.Sc. Student in Optometry, Iran University of Medical Sciences, Tehran, Iran
6
Assistance Professor, Ophthalmology Dept., Iran Eye Research Center, Iran University of Medical Sciences, Tehran, Iran
7
Fellowship, Ophthalmology Dept., Iran University of Medical Sciences, Tehran, Iran
Abstract
Introduction: Age-related Macular Degeneration (AMD) is one of the most important causes of irreversible
blindness in the developed world and prevents the affected person from performing simple tasks such as
reading, driving and facial recognition. In the AMD, new blood vessels grow underneath the retina in a
process called choroidal neovascularisation (CNV). There is much interest in the quantification of the
angiographic features of CNV, as these parameters are used as markers for monitoring the response to CNV
treatment. To date, the techniques used in angiographic analysis are based on subjective interpretation by
experienced clinicians. The goal of the present study was to propose an automatic algorithm for determining
the extent of CNV.
Materials and Methods: The proposed algorithm was used to analyze indocyanine angiograms of 12
patients with CNV. The angiograms were acquired by a confocal scanning laser ophthalmoscope. The
algorithm included an adaptive Wiener filtering technique, a top-hat morphology method and a new
thresholding technique based on a modification of Otsu’s method. The area of each lesion was obtained and
compared with a subjective evaluation of CNV. Finally, each area was expressed in square millimeters by
making a cylindrical tube filled with indocyanine green.
Results: The CNV area was determined by the proposed algorithm and an observer. No significant
differences were observed between the two data sets (p>0.05).
Discussion and conclusion: This study demonstrates that the proposed algorithm based on a modification of
Otsu’s method can be used to measure the area of CNV.
Keywords
Main Subjects
)