PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Voting Margin Approach for the Detection of Retinal Microaneurysms
Ilkka Autio, Juan Carlos Borrás García, Ilkka Immonen, Petri Jalli and Esko Ukkonen
In: IASTED's 5th Conference on Visualization, Imaging and Image Processing (VIIP-2005), September 7-9, 2005, Benidorm, Spain.

Abstract

We address the problem of detecting microaneurysms in retinal fundus images. Retinal microaneurysms are the earliest known indicator of diabetic retinopathy, an affliction which may result in blindness. We derive a maximum margin classifier capable of utilizing a collection of strong base classifiers, each of which may impose a different similarity induced kernel in the input space. Our experiments demonstrate that the resulting classifier is accurate and at least on par with the previous approaches to the problem.

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EPrint Type:Conference or Workshop Item (Paper)
Additional Information:ML-based retinal microaneurysms detection whose performance is similar to the methods described by Walther et al. at ISMDA2002.
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:1817
Deposited By:Juan Carlos Borrás García
Deposited On:29 November 2005