NDLI: Augmenting the classification of retinal lesions using spatial distribution

Content Provider	IEEE Xplore Digital Library
Author	Massey, E.M. Hunter, A.
Copyright Year	2011
Description	Author affiliation: School of Computer Science, University of Lincoln, Brayford Pool, Lincoln, UK, LN6 7TS (Massey, E.M.; Hunter, A.)
Abstract	This paper introduces SAGE — an algorithm that uses the spatial clustering of objects to enhance their classification. It assumes that discrete objects can be identified and classified based on their individual appearance, and further that they tend to appear in spatial clusters (for example, circinate exudates). The algorithm builds spatial distribution maps for objects and confounds for a given image, and adjusts individual object confidence levels to reflect their spatial clustering. SAGE may be combined with a wide range of object identification and classification methods; we demonstrate it using a Multi-Layered Perceptron (MLP) Neural Network and a Support Vector Machine (SVM) classifier types for both dark and bright retinal lesions. Using ROC analysis SAGE improves classifier performance as much as 83%.
Starting Page	3967
Ending Page	3970
File Size	738101
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6090985
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lesions Retina Clustering algorithms Feature extraction Vectors Support vector machines Maximum likelihood estimation
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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