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Automated Detection of Microaneurysms Using Scale-Adapted Blob Analysis and Semi-Supervised Learning

Abstract : Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented. Several scale-adapted region descriptors are then introduced to characterize these blob regions. A semi-supervised based learning approach, which requires few manually annotated learning examples, is also proposed to train a classifier to detect true MAs. The developed system is built using only few manually labeled and a large number of unlabeled retinal color fundus images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. A competition performance measure (CPM) of 0.364 shows the competitiveness of the proposed system against state-of-the art techniques as well as the applicability of the proposed features to analyze fundus images.
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Contributeur : Désiré Sidibé Connectez-vous pour contacter le contributeur
Soumis le : mercredi 15 janvier 2014 - 10:14:05
Dernière modification le : mardi 19 octobre 2021 - 11:45:32
Archivage à long terme le : : mardi 15 avril 2014 - 22:10:40


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Kedir Adal, Désiré Sidibé, Sharib Ali, Edward Chaum, Thomas Karnowski, et al.. Automated Detection of Microaneurysms Using Scale-Adapted Blob Analysis and Semi-Supervised Learning. Computer Methods and Programs in Biomedicine, Elsevier, 2014, pp.1-22. ⟨10.1016/j.cmpb.2013.12.009⟩. ⟨hal-00931220⟩



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