A supervised learning framework of statistical shape and probability priors for automatic prostate segmentation in ultrasound images
A new schema of multiple mean statistical shape and appearance model for prostate segmentation is proposed. Supervised soft classification with random forest is performed to identify prostate. Posterior probability is used to build the appearance model that reduces inter dataset contrast variations. Chi square goodness of fit test is performed to prove that the combined shape and appearance space is not Gaussian. Multiple Gaussian mean models of shape and appearance priors are created with the aid of spectral clustering.
prostate segmentation statistical shape and appearance model spectral clustering random forest classification