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RESEARCH PRODUCT
Automated Detection of Microaneurysms Using Scale-Adapted Blob Analysis and Semi-Supervised Learning
Kedir M. AdalSharib AliFabrice MeriaudeauDésiré SidibéEdward ChaumThomas P. Karnowskisubject
semi-supervised learningFundus OculiComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONMicroaneurysmsblobsHealth Informatics02 engineering and technologySemi-supervised learningFundus (eye)[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]030218 nuclear medicine & medical imagingScale spaceAutomation03 medical and health scienceschemistry.chemical_compound0302 clinical medicine0202 electrical engineering electronic engineering information engineeringmedicineHumansLearningComputer visionBlob analysisMicroaneurysmbusiness.industry[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]RetinalDiabetic retinopathymedicine.diseaseAneurysmComputer Science Applicationsdiabetic retinopathyfundus imagechemistryscale-space.scale-space020201 artificial intelligence & image processingArtificial intelligencebusinessClassifier (UML)SoftwareRetinopathydescription
International audience; 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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2014-01-07 |