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

J. Goh; H. L. Tang; T. Peto; G. Saleh

doi:10.1109/CEC.2012.6252996

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

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 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 language	English
Title of host publication	2012 IEEE Congress on Evolutionary Computation, CEC 2012
DOIs	https://doi.org/10.1109/CEC.2012.6252996
Publication status	Published - 2012
Event	2012 IEEE Congress on Evolutionary Computation, CEC 2012 - Brisbane, QLD, Australia Duration: 10 Jun 2012 → 15 Jun 2012

Publication series

Name	2012 IEEE Congress on Evolutionary Computation, CEC 2012

Conference

Conference	2012 IEEE Congress on Evolutionary Computation, CEC 2012
Country/Territory	Australia
City	Brisbane, QLD
Period	10/06/12 → 15/06/12

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/CEC.2012.6252996

Cite this

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title = "An evolutionary approach for determining hidden Markov model for medical image analysis",

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.",

keywords = "Contextual Reasoning, Diabetic Retinipathy, Genertic Algorithms, Hidden Markov Models, Memetic Algorithms, Particle Swarm Optimisation",

author = "J. Goh and Tang, {H. L.} and T. Peto and G. Saleh",

year = "2012",

doi = "10.1109/CEC.2012.6252996",

language = "English",

isbn = "9781467315098",

series = "2012 IEEE Congress on Evolutionary Computation, CEC 2012",

booktitle = "2012 IEEE Congress on Evolutionary Computation, CEC 2012",

note = "2012 IEEE Congress on Evolutionary Computation, CEC 2012 ; Conference date: 10-06-2012 Through 15-06-2012",

}

Goh, J, Tang, HL, Peto, T & Saleh, G 2012, An evolutionary approach for determining hidden Markov model for medical image analysis. in 2012 IEEE Congress on Evolutionary Computation, CEC 2012., 6252996, 2012 IEEE Congress on Evolutionary Computation, CEC 2012, 2012 IEEE Congress on Evolutionary Computation, CEC 2012, Brisbane, QLD, Australia, 10/06/12. https://doi.org/10.1109/CEC.2012.6252996

TY - GEN

T1 - An evolutionary approach for determining hidden Markov model for medical image analysis

AU - Goh, J.

AU - Tang, H. L.

AU - Peto, T.

AU - Saleh, G.

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - Contextual Reasoning

KW - Diabetic Retinipathy

KW - Genertic Algorithms

KW - Hidden Markov Models

KW - Memetic Algorithms

KW - Particle Swarm Optimisation

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U2 - 10.1109/CEC.2012.6252996

DO - 10.1109/CEC.2012.6252996

M3 - Conference contribution

AN - SCOPUS:84866876385

SN - 9781467315098

T3 - 2012 IEEE Congress on Evolutionary Computation, CEC 2012

BT - 2012 IEEE Congress on Evolutionary Computation, CEC 2012

T2 - 2012 IEEE Congress on Evolutionary Computation, CEC 2012

Y2 - 10 June 2012 through 15 June 2012

ER -

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

Abstract

Publication series

Conference

Keywords

UN SDGs

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