Optimal feature combination for automated segmentation of prostatic adenocarcinoma from high resolution MRI

In spite of the high global incidence of prostate cancer, limited computer-aided tools to assist in its detection exist and that too only for ultrasound images. In this work we present a novel feature ensemble scheme for combining different 3D texture features for automated segmentation of prostatic adenocarcinoma from 4T MR images. The first step of our methodology comprises of a feature extraction module to extract 3D statistical, gradient and Gabor texture features at multiple scales and orientations and generate the corresponding Feature Scenes. Every voxel in each of the Feature Scenes is assigned a likelihood of malignancy using a Bayesian inference module. These results are then combined using a novel weighted linear combination scheme; weights being determined by minimization of a cost function. The method was found to be optimal compared to other popular ensemble methods such as Boosting, Majority Rule, Product Rule and Averaging in terms of Sensitivity and Positive Predictive Value (PPV). In fact, our feature ensemble scheme also outperformed an expert radiologist in terms of Sensitivity. An interesting result from the comparison of the different feature ensembles was that Boosting performs poorly on MR data that has been corrected for background in homogeneity.