B-mode cardiac ultrasound image segmentation is a fundamental step before cardiac functional parameters estimation. Aiming at the problem that the accuracy of segmentation is low because of the low resolution of ultrasound image, and the model based image segmentation algorithms need a large number of training sets, an image segmentation algorithm based on pixel clustering was proposed combined with prior knowledge of B-mode cardiac ultrasound images. Firstly, anisotropic diffusion was used to preprocess the image. Secondly, one-dimensional K-means was used to cluster the pixels. Finally, every pixel value of the image was assigned to the pixel value of its best cluster center according to cluster results and prior knowledge. The theoretical analysis shows that the proposed algorithm can get the maximum Peak Signal-to-Noise Ratio (PSNR) of ultrasound image; the experimental results show that the proposed algorithm performs better than Otsu algorithm, and its PSNR is increased by 11.5% compared with Otsu algorithm. The proposed algorithm can still work even for a single ultrasound image and can be suitble for ultrasound image segmentation of any shapes, so it is conducive to estimate cardiac functional parameters more accurately.