Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/6515
Title: Learning features on tear film lipid layer classification
Authors: Beatriz Remeseiro López
Bolón Canedo,V
Alonso Betanzos,A
Penedo,MG
Issue Date: 2015
Abstract: Dry eye is a prevalent disease which leads to irritation of the ocular surface, and is associated with symptoms of discomfort and dryness. The Guillon tear film classification system is one of the most common procedures to diagnose this disease. Previous research has demonstrated that this classification can be automatized by means of image processing and machine learning techniques. However, all approaches for automatic classification have been focused on dark eyes, since they are most common in humans. This paper introduces a methodology making use of feature selection methods, to learn which features are the most relevant for each type of eyes and, thus, improving the automatic classification of the tear film lipid layer independently of the color of the eyes. Experimental results showed the adequacy of the proposed methodology, achieving classification rates over 90%, while producing unbiased results and working in real-time.
URI: http://repositorio.inesctec.pt/handle/123456789/6515
metadata.dc.type: conferenceObject
Publication
Appears in Collections:C-BER - Indexed Articles in Conferences

Files in This Item:
File Description SizeFormat 
P-00M-5YT.pdf
  Restricted Access
1.42 MBAdobe PDFThumbnail
View/Open Request a copy


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.