Segmentation of MRI Prostate Images using Gaussian Mixture Models (GMMs)

Image processing is advancing rapidly as data acquisition through digital medium is becoming more common. The advancement of data acquisition directly affects the medical field, specifically in terms of processing the images produced by Computed Tomography (CT) scan, Magnetic Resonance Imaging (MRI), ultrasound, and XRay. These wide range of imaging modalities would need a faster, more reliable algorithm for image segmentation to cater for the different characteristics and needs of image processing. Automated segmentation approach is extensively studied as the conventional approach, which is analysing the images manually, has been proven to be very time consuming besides being susceptible to human errors. In this paper, the performance of Gaussian Mixture Model method for medical image segmentation is assessed with the help of Expectation-Maximization (EM) method for data training and Bayesian Information Criterion (BIC) function to prevent data overfitting. This method is evaluated against MRI prostate images in particular. This method is also compared to K-Means algorithm to evaluate its performance with synthetic image and also clinical MRI image