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Using wavelet and bayesian decision theory in real-time prostate volume measurements

Nia, HFG and Hu, H (2013) Using wavelet and bayesian decision theory in real-time prostate volume measurements. In: UNSPECIFIED, ? - ?.

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Abstract

The volume of prostate is one of the key indicators in the diagnosis and treatment of prostate cancer. This paper presents a novel method to calculate the volume of prostate in MRI images with high accuracy and in real time. In this approach, wavelet transform is used to decompose a MRI image into spatially oriented channels and then decompose each sub-image into 1D signal, by obtaining integral of subimages. Bayesian decision theory is then used to analyze signals and detect the boundaries of prostate. Experimental results show that the proposed method can be implemented in real time and has acceptable accuracy. © 2013 IEEE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Users 161 not found.
Date Deposited: 17 Dec 2014 12:49
Last Modified: 23 Jan 2019 00:18
URI: http://repository.essex.ac.uk/id/eprint/9250

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