Hanieh Mohammadreza - M.Sc. Student in Medical Physics, Tehran University of Medical Sciences, Tehran, Iran
Marjaneh Hejazi - Assistance Professor of Medical Physics and Biomedical Engineering Dept., Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences, Tehran, Iran
MMohammad Ali Oghabian - Associate Professor of Medical Physics and Biomedical Engineering Department, Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences, Tehran, Iran
Alireza Ahmadian - Associate Professor of Medical Physics and Biomedical Engineering Department, Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences, Tehran, Iran
Masoud Naseripour - Associate Professor, Ophthalmology Dept., Iran Eye Research Center, Iran University of Medical Sciences, Tehran, Iran
Mohammadreza Zarin - M.Sc. Student in Optometry, Iran University of Medical Sciences, Tehran, Iran
Khalil Ghasemi - Assistance Professor, Ophthalmology Dept., Iran Eye Research Center, Iran University of Medical Sciences, Tehran, Iran
Hosein Nazari - Fellowship, Ophthalmology Dept., Iran University of Medical Sciences, Tehran, Iran

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  ۱۲  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>۰.۰۵).   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.

Adaptive wiener filter, Choroidal neovasculariztion, Indocyanine green angiography, Modified Otsu, Segmentation