An evolutionary approach for determining hidden Markov model for medical image analysis
Goh, J, Tang, HL, Peto, T and Saleh, G (2012) An evolutionary approach for determining hidden Markov model for medical image analysis 2012 IEEE Congress on Evolutionary Computation, CEC 2012.
WCCI-An Evolutionary Approach for Determining Hidden Markov Model for Medical Image Analysis Final Version.pdf
Available under License : See the attached licence file.
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. © 2012 IEEE.
|Divisions :||Faculty of Engineering and Physical Sciences > Computing Science|
|Identification Number :||https://doi.org/10.1109/CEC.2012.6252996|
|Additional Information :||© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.|
|Depositing User :||Symplectic Elements|
|Date Deposited :||26 Sep 2013 10:54|
|Last Modified :||09 Jun 2014 13:12|
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