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

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.