Prostate Segmentation with Texture Enhanced Active Appearance Model

Abstract

Prostate contour segmented from Trans Rectal Ultra Sound (TRUS) and Magnetic Resonance (MR) images could improve inter-modality registration accuracy and reduce computational complexity of the procedure. However, prostate segmentation in each of these modalities is a challenging task in presence of imaging artifacts, intensity heterogeneities, and large inter patient shape variabilities of the prostate. We propose to use Haar wavelet approximation coefficients to extract texture features of the prostate region in both modalities to guide a deformable parametric model to segment the prostate in a multi-resolution framework. Principal Component Analysis (PCA) of the shape and texture information of the prostate region obtained from the training data aids contour propagation of the deformable parametric model. Prior knowledge of the optimization space is utilized for optimal segmentation of the prostate. Our method achieves a mean Dice Similarity Coefficient (DSC) value of 0.95±0.01, with mean segmentation time of 0.72±0.05 seconds in a leave-one-out validation framework with 25 TRUS images grabbed from a video sequence. DSC value of 0.88 ± 0.06 with a mean segmentation time of 0.81 ± 0.02 seconds was recorded for MR images when validated with 15 central slice images of 15 datasets from the MICCAI prostate segmentation challenge 2009. Our proposed method performs computationally efficient accurate multi-modal prostate segmentation in presence of intensity heterogeneities and imaging artifacts.