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Comput Methods Programs Biomed. 2014 Apr;114(1):1-10. doi: 10.1016/j.cmpb.2013.12.009. Epub 2014 Jan 7.

Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning.

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Université de Bourgogne, Laboratoire Le2i UMR CNRS 6306, Le Creusot 71200, France. Electronic address:
Université de Bourgogne, Laboratoire Le2i UMR CNRS 6306, Le Creusot 71200, France.
Hamilton Eye Institute, U. Tennessee Health Sciences Center, Memphis, TN, USA.
Oak Ridge National Laboratory, Oak Ridge, TN, USA.


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 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 which can 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.


Blobs; Diabetic retinopathy; Fundus image; Microaneurysms; Scale-space; Semi-supervised learning

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