Comput Methods Programs Biomed - Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning.

Tópicos

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Resumo

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.

Resumo Limpo

despit sever attempt autom detect microaneurysm ma digit fundus imag still remain open issu due subtl natur mas surround tissu paper microaneurysm detect problem model find interest region blob imag automat localscal select techniqu present sever scaleadapt region descriptor introduc character blob region semisupervis base learn approach requir manual annot learn exampl also propos train classifi can detect true mas develop system built use manual label larg number unlabel retin color fundus imag perform overal system evalu retinopathi onlin challeng roc competit databas competit perform measur cpm show competit propos system stateofth art techniqu well applic propos featur analyz fundus imag

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