In order to detect prostate diseases, urologists usually use ultrasound images or magnetic resonance images (MRI) for clinical diagnosis. In clinical practice, region of prostate is manually outlined by urologist. However, outline the prostate boundary manually is highly time-consuming. In this paper, a prostate segmentation and volume estimation in MRI is proposed. Anactive contour model(ACM) is adopted to obtain the initial contour of the prostate. Four textural features extracted from the prostate were used to train the SVM classifier. Non-prostate regions are then excluded by the trained SVM. A quick convex hull is applied to refine the shape of prostate. The volume of the prostate is eventually estimated by the series of segmented prostate regions. The proposed segmentation method achieves high accuracy of 93.7%. Our experimental results show that the proposed prostate segmentation and volume estimation method is highly potential for helping urologists in clinical diagnosis.