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A comparison of computer based classification methods applied to the detection of microaneurysms in



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PERGAMON Computers in Biology and Medicine 28 1998 225 238 A comparison of computer based classi cation methods applied to the detection of microaneurysms in ophthalmic uorescein angiograms Allan J Frame a Peter E Undrill a Michael J Cree a John A Olson b Kenneth C McHardy c Peter F Sharp a John V Forrester b a Department of Bio medical Physics and Bio engineering University of Aberdeen Aberdeen UK b Department of Ophthalmology University of Aberdeen Aberdeen UK c Diabetic Clinic Aberdeen Royal In rmary Aberdeen UK Received 10 October 1997 accepted 11 May 1998 Abstract We compared the performance of three computer based classi cation methods when applied to the problem of detecting microaneurysms on digitised angiographic images of the retina An automated image processing system segmented candidate objects microaneurysms or spurious objects and produced a list of features on each candidate for use by the classi ers We compared an empirically derived rule based system with two automated methods linear discriminant analysis and a learning vector quantiser arti cial neural network to classify the objects as microaneurysms or otherwise ROC analysis shows that the rule based system gave a higher performance than the other methods p 0 92 although a much greater development time is required 1998 Elsevier Science Ltd All rights reserved Keywords Neural networks Linear discriminant analysis Rule based system Ophthalmology Computer aided diagnosis 1 Introduction Diabetic retinopathy DR is the ocular manifestation of the systemic disease diabetes mellitus and is the most common cause of blindness in the UK working population 1 In current research studies assessment of DR is made semi quantitatively by comparing a photograph of the patient s fundus with a standard set of photographs and the stage of DR Corresponding author Tel 44 1224 681818 x52430 Fax 44 1224 685645 E mail a frame biomed abdn ac uk 0010 4825 98 19 00 1998 Elsevier Science Ltd All rights reserved PII S 0 0 1 0



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