针对腹部器官边缘模糊、形状差异大、小样本集合难建立统计模型等问题，提出了基于多分辨率统计集成模型和曲面缺失数据恢复的混合图像分割算法。该算法根据器官模型的纹理特征，建立外观轮廓模型；并定义标志点自信度。对于自信度较高的点，使用基于主动图像搜索和模型变形的方法进行分割；将自信度较低的点视为未知点，利用统计模型和自信度高的已知点进行数据恢复。实验结果表明，该混合算法可成功地降低器官分割的平均误差。

The segmentation of abdominal CT series is a challenging task due to problems such as blur edges, large variance among individuals and small sample sizes. In this paper, a hybrid 3D surface segmentation algorithm based on a multi-resolution integrated model and missing data recovery technique is proposed. The appearance models to characterize the texture features around surface points are established, and the"confidence level （CFL）"for each point is defined. For the points which have high confidence, segmentation is accomplished by active image searching and model deformation. While for the points which have low confidence, instead of using unreliable edge information, data recovery technique is applied based on a statistical deformable model and available high confidence points. The experimental results demonstrate that the Hybrid-MISTO achieves the lowest segmentation error compared with a variety of state-of-the-art techniques such as Snake, ASM, and MISTO.