An evolutionary approach for determining hidden Markov model for medical image analysis

J. Goh*, H. L. Tang, T. Peto, G. Saleh

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Hidden Markov Model (HMM) is a technique highly capable of modelling the structure of an observation sequence. In this paper, HMM is used to provide the contextual information for detecting clinical signs present in diabetic retinopathy screen images. However, there is a need to determine a feature set that best represents the complexity of the data as well as determine an optimal HMM. This paper addresses these problems by automatically selecting the best feature set while evolving the structure and obtaining the parameters of a Hidden Markov Model. This novel algorithm not only selects the best feature set, but also identifies the topology of the HMM, the optimal number of states, as well as the initial transition probabilities.

Original languageEnglish
Title of host publication2012 IEEE Congress on Evolutionary Computation, CEC 2012
DOIs
Publication statusPublished - 04 Oct 2012
Externally publishedYes
Event2012 IEEE Congress on Evolutionary Computation, CEC 2012 - Brisbane, QLD, Australia
Duration: 10 Jun 201215 Jun 2012

Conference

Conference2012 IEEE Congress on Evolutionary Computation, CEC 2012
Country/TerritoryAustralia
CityBrisbane, QLD
Period10/06/201215/06/2012

Keywords

  • Contextual Reasoning
  • Diabetic Retinipathy
  • Genertic Algorithms
  • Hidden Markov Models
  • Memetic Algorithms
  • Particle Swarm Optimisation

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science

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