Using Wavelet and Bayesian Decision Theory in Real-Time Prostate Volume Measurements

Author

Nia, Hossein Farid Ghassem ; Huosheng Hu

Author_Institution

Sch. of Comput. Sci. & Electron. Eng., Univ. of Essex, Colchester, UK

fYear

2013

fDate

13-16 Oct. 2013

Firstpage

1199

Lastpage

1204

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 sub-images. 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.

Keywords

Bayes methods; biomedical MRI; cancer; decision theory; medical image processing; patient treatment; wavelet transforms; 1D signal; Bayesian decision theory; MRI images; prostate boundaries detection; prostate cancer diagnosis; prostate cancer treatment; real-time prostate volume measurements; spatially oriented channels; subimage decomposition; wavelet transform; Accuracy; Algorithm design and analysis; Image edge detection; Magnetic resonance imaging; Shape; Wavelet transforms; Bayesian decision theory; Prostate volume measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on

Conference_Location

Manchester

Type

conf

DOI

10.1109/SMC.2013.208

Filename

6721961