Abstract：Liver hydatid is a common parasitic disease in Xinjiang and a big concern for people's health. At present, CT imaging analysis is always a method for diagnosising liver hydatid. The CT image of liver hydatid owns their characteristics, such as inconsistent gray distribution and fuzzy regional boundary. Meanwhile, the representations of CT images are also dissimilar among different types of liver hydatid cyst. Based on CT imaging features of this disease, an iterative approach for liver segmentation and hydatid lesion extraction is proposed in this paper. Each iteration consists of two main steps. Firstly, according to the user-defined pixel seeds in the liver and lesion which are defined by user, Gaussian probability model fitting is adopted to fit gray distribution in different regions and smoothed Bayesian classification is applied to obtain the initial segmentation results of liver and lesion. Secondly, the parametric active contour model using the priori shape force field is adopted to refine the initial segmentation and to get accurate boundaries of liver and lesion. The algorithm from subjective and objective aspects are evaluated on different patients' CT slices. By comparing the algorithm of segmentation to the ground-truth manual segmentation. The proposed algorithm is shown to be effective in liver segmentation and hydatid lesion extraction.