skip to main content

SciTech ConnectSciTech Connect

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

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.
 [1] ;  [2] ;  [2] ;  [3] ;  [1] ;  [1]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Univ. of Burgundy, Dijon (France)
  3. Univ. of Tennessee, Knoxville, TN (United States)
Publication Date:
OSTI Identifier:
DOE Contract Number:
Resource Type:
Journal Article
Resource Relation:
Journal Name: Computer Methods and Programs in BioMedicine; Journal Volume: 114; Journal Issue: 1
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
ORNL work for others
Country of Publication:
United States