Many methods for liver tumor Computed Tomography (CT) segmentation have the difficulty to achieve accurate tumor due to inhomogeneous gray and fuzzy edges. To obtain precise segmentation result, a method using multi-scale morphology was proposed to eliminate local minima. Firstly, the morphological area operation was used to remove image's small burrs and irregular edges so as to avoid boundaries migration. Secondly, local minima in gradient image were distinguished by the combined knowledge of statistic characteristics and morphological properties including depth and scale. After partition, the function relationship was established between multi-scale structure elements and local minima. In order to filter noise via large-size structure elements and preserving major object via small-size structure elements, a morphological method called close operation was then employed to adaptively modify the image.Finally, standard watershed transform was utilized to implement segmentation of liver tumor. The experimental results show that this method can reduce over-segmentation effectively and liver tumor can be segmented accurately while boundaries of objects are located precisely.