Prostate segmentation from extracorporeal ultrasound (ECUS) images is considerably challenging due to the prevailing speckle noise, shadow artifacts, and low contrast intensities. In this paper, we proposed a cascaded shape regression (CSR) method for automatic detection and localization of the prostate. A sequence of random fern predictors are trained in a boosted regression manner. Shape-indexed features are used to achieve invariance against geometric scales, translation, and rotation of prostate shapes. The boundary detected by CSR is used as the initialization for accurate segmentation by using a dynamic directional gradient vector flow (DDGVF) snake model. DDGVF proves to be useful to distinguish desired edges from false edges in ECUS
images. The proposed method is tested on both longitudinal- and axialview ECUS images and achieves Root Mean Square Error (RMSE) under 1.98 mm (=4.95 pixels). It outperforms the active appearance model in terms of RMSE, failure rate, and area error metrics. The testing time of CSR+DDGVF is less than 1 second per image.