Document Detail

Automated "Disease / No Disease" Grading of Age-Related Macular Degeneration by an Image Mining Approach.
MedLine Citation:
PMID:  23150624     Owner:  NLM     Status:  Publisher    
PURPOSE: To describe and evaluate an automated grading system for age-related macular degeneration (AMD) by color fundus photography. METHODS: An automated "disease / no disease" grading system for AMD was developed based on image mining techniques. First, image pre-processing was performed to normalize color and non-uniform illumination of the fundus images, to define a region of interest, and to identify and remove pixels belonging to retinal vessels. To represent images for the prediction task, a graph based image representation using quadtrees was then adopted. Next, a graph mining technique was applied to the generated graphs to extract relevant features (in the form of frequent sub-graphs) from images of both AMD and healthy volunteers. Features of the training data were then fed into a classifier generator (Naïve Bayes and Support Vector Machines were used with respect to the evaluation presented later in this paper) for training purposes before employing the trained classifiers to classify new "unseen images". RESULTS: The algorithm was evaluated on two publically available fundus image datasets (ARIA and STARE) comprising 258 images (160 AMD and 98 normal). Ten-fold cross validation was used. The experiments produced a best specificity of 100% and a best sensitivity of 99.4% with an overall accuracy of 99.6%. Our approach outperformed previous approaches reported in the literature. CONCLUSIONS: The proposed technique has demonstrated a proof of concept for an automated AMD grading technique. It has the potential to be further developed as an automated grading tool for future whole scale AMD screening programs.
Yalin Zheng; Mohd Hanafi Ahmad Hijazi; Frans Coenen
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-11-13
Journal Detail:
Title:  Investigative ophthalmology & visual science     Volume:  -     ISSN:  1552-5783     ISO Abbreviation:  Invest. Ophthalmol. Vis. Sci.     Publication Date:  2012 Nov 
Date Detail:
Created Date:  2012-11-14     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  7703701     Medline TA:  Invest Ophthalmol Vis Sci     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Department of Eye and Vision Science, University of Liverpool, Institute of Ageing and Chronic Disease, Daulby Street, Liverpool, L69 3GA, United Kingdom.
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