COMBINING PARTICLE SWARM OPTIMISATION WITH GENETIC ALGORITHM FOR CONTEXTUAL ANALYSIS OF MEDICAL IMAGES

Jonathan Goh, Lilian Tang, Lutfiah Al turk, Yaochu Jin

Abstract

Micro aneurysms are one of the first visible clinical signs of diabetic retinopathy and their detection can help diagnose the progression of the disease. In this paper, we propose to use a hybrid evolutionary algorithm to evolve the structure and parameters of a Hidden Markov Model to obtain an optimised model that best represents the different contexts of micro aneurysms sub images. This technique not only identifies the optimal number of states, but also determines the topology of the Hidden Markov Model, along with the initial model parameters. We also make a comparison between evolutionary algorithms to determine the best method to obtain an optimised model.

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Paper Citation


in Harvard Style

Goh J., Tang L., Al turk L. and Jin Y. (2011). COMBINING PARTICLE SWARM OPTIMISATION WITH GENETIC ALGORITHM FOR CONTEXTUAL ANALYSIS OF MEDICAL IMAGES . In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2011) ISBN 978-989-8425-34-8, pages 235-241. DOI: 10.5220/0003155902350241


in Bibtex Style

@conference{healthinf11,
author={Jonathan Goh and Lilian Tang and Lutfiah Al turk and Yaochu Jin},
title={COMBINING PARTICLE SWARM OPTIMISATION WITH GENETIC ALGORITHM FOR CONTEXTUAL ANALYSIS OF MEDICAL IMAGES},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2011)},
year={2011},
pages={235-241},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003155902350241},
isbn={978-989-8425-34-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2011)
TI - COMBINING PARTICLE SWARM OPTIMISATION WITH GENETIC ALGORITHM FOR CONTEXTUAL ANALYSIS OF MEDICAL IMAGES
SN - 978-989-8425-34-8
AU - Goh J.
AU - Tang L.
AU - Al turk L.
AU - Jin Y.
PY - 2011
SP - 235
EP - 241
DO - 10.5220/0003155902350241