On the basis of properties of magnetic resonance images for the prostate, an active shape image segmentation method making use of adaptive texture distribution was introduced to segment a prostate magnetic resonance image. Firstly, a prostate region of interest was determined through image classification and image fitting, and several shape parameters were estimated and used in the segmentation. Then, multi-features were fused to build a texture coincidence measure. In order to improve the searching and matching ability of an active shape, the active shape was divided into two portions, the texture distribution shape and the supplementary shape. In search, the estimated parameters were used to optimize the initial estimation of the active shape searching and adjust the iterative process based on the texture distribution shape and the supplementary shape. Experimental results indicate that the Hausdorff Distance is 6.00 pixels between the true prostate contour and that extracted by the proposed method and the segmentation accuracy of the method is 93%. The proposed method can modify the active shape effectively, and can automatically segment the prostate magnetic resonance images with high enough accuracy.