This paper discusses novel texture features that allow providing enhanced classification accuracy for focal hepatic lesions. The proposed texture features takes advantage of the rotation and scale invariant nature of Gabor wavelets, as well as the gray-level co-occurrence matrix (GLCM) for analyzing the spatial distribution of the pixel intensity in the lesion. To verify the effectiveness of the proposed texture features, experiments were performed with 150 ultrasound images containing 150 focal hepatic lesions, consisting of 50 cysts, 50 hemangiomas, and 50 malignancies. Experimental results show that the proposed texture features allow for an improved classification performance, compared to the use of other features.