@inproceedings{0d0cbac16e4a42baba8f31f901e61493,
title = "Growcut-based drusen segmentation for age-related macular degeneration detection",
abstract = "Age-related Macular Degeneration (AMD) is the third leading cause of blindness. Its prevalence is increasing in these years for the coming of 'aging time'. Early detection and grading can prohibit it from becoming severe and protect vision. The appearance of drusen is an important indicator for AMD thus automatic drusen detection and segmentation have attracted much research attention in the past years. In this paper, we propose a novel drusen segmentation method by using Growcut. This method first detects the local maximum and minimum points. The maximum points, which are potential drusen, are then classified as drusen or non-drusen. The drusen points will be used as foreground labels while the non-drusen points together with the minima will be used as background labels. These labels are fed into Growcut to obtain the drusen boundaries. The method is tested on a manually labeled dataset with 96 images containing drusen. The experimental results verify the effectiveness of the method.",
keywords = "Age-related Macular Degeneration, Drusen segmentation, Growcut",
author = "Huiying Liu and Yanwu Xu and Wong, {Damon W.K.} and Jiang Liu",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE Visual Communications and Image Processing Conference, VCIP 2014 ; Conference date: 07-12-2014 Through 10-12-2014",
year = "2015",
month = feb,
day = "27",
doi = "10.1109/VCIP.2014.7051529",
language = "English",
series = "2014 IEEE Visual Communications and Image Processing Conference, VCIP 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "161--164",
booktitle = "2014 IEEE Visual Communications and Image Processing Conference, VCIP 2014",
address = "United States",
}