Bayesian Transductive Markov Random Fields for Interactive Segmentation in Retinal Disorders

  • Noah Lee
  • Andrew F. Laine
  • R. Theodore Smith
Part of the IFMBE Proceedings book series (IFMBE, volume 25/11)

Abstract

In the realm of computer aided diagnosis (CAD) interactive segmentation schemes have been well received by physicians, where the combination of human and machine intelligence can provide improved segmentation efficacy with minimal expert intervention [1-3]. Transductive learning (TL) or semi-supervised learning (SSL) is a suitable framework for learning-based interactive segmentation given the scarce label problem. In this paper we present extended work on Bayesian transduction and regularized conditional mixtures for interactive segmentation [3]. We present a Markov random field model integrating a semi-parametric conditional mixture model within a Bayesian transductive learning and inference setting. The model allows efficient learning and inference in a semi-supervised setting given only minimal approximate label information. Preliminary experimental results on multimodal images of retinal disorders such as drusen, geographic atrophy (GA), and choroidal neovascularisation (CNV) with exudates and subretinal fibrosis show promising segmentation performance.

Keywords

Interactive Segmentation Naive Bayes Mixture Models Generative Learning Markov Random Fields Bayesian Transduction 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Noah Lee
    • 1
  • Andrew F. Laine
    • 1
  • R. Theodore Smith
    • 2
  1. 1.Heffner Biomedical Imaging Lab (HBIL) Department of Biomedical EngineeringColumbia UniversityNew YorkUSA
  2. 2.Department of OphthalmologyColumbia UniversityNew YorkUSA

Personalised recommendations