Automated Microaneurysm Detection in Fundus Images by Region Growing

Lin Li1, Juan Shan2

  • 1Seattle University
  • 2Pace University

Details

Category

1-Page Late Breaking Paper (Poster)

Sessions

09:05 - 09:55 | Thu 16 Feb | Ballroom D | ThRAF

Rapid Fire Session 01: Imaging Informatics

Abstract

In this study, we propose an approach to automatically partition a fundus image into regions and evaluate whether a region contains microaneurysm (MA). The approach can achieve sensitivity 81%, specificity 95.4%, and accuracy 91.8%, on a public dataset DIARETDB.

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