Paper Info
Title
Retinal Area Detector from Scanning Laser Ophthalmoscope (SLO) Images for Diagnosing Retinal Diseases.
Abstract
Scanning Laser Ophthalmoscopes (SLOs) can be used for early detection of retinal diseases. With the advent of latest screening technology, the advantage of using SLO is its wide Field of View (FOV), which can image a large part of the retina for better diagnosis of the retinal diseases. On the other hand, during the imaging process, artefacts such as eyelashes and eyelids are also imaged along with the retinal area. This brings a big challenge on how to exclude these artefacts. In this paper, we propose a novel approach to automatically extract out true retinal area from an SLO image based on image processing and machine learning approaches. To reduce the complexity of image processing tasks and provide a convenient primitive image pattern, we have grouped pixels into different regions based on the regional size and compactness, called superpixels. The framework then calculates image based features reflecting textural and structural information and classifies between retinal area and artefacts. The experimental evaluation results have shown good performance with an overall accuracy of 92%.
Year
DOI
Venue
2015
10.1109/JBHI.2014.2352271
IEEE journal of biomedical and health informatics
Keywords
Field
DocType
entropy,indexes,feature extraction,machine learning,learning artificial intelligence,image classification,image processing,feature selection
Field of view,Computer vision,Ophthalmoscopes,Feature selection,Pattern recognition,Computer science,Retina,Image processing,Feature extraction,Artificial intelligence,Pixel,Retinal
Journal
Volume
Issue
ISSN
PP
99
2168-2208
Citations 
PageRank 
References 
1
0.36
15
Authors
5
Name
Order
Citations
PageRank
Muhammad Salman Haleem1202.42
Liangxiu Han215020.13
Jano van Hemert361.27
Baihua Li417621.71
Alan Fleming5141.98