A feature-based two-step local stereo matching with high efficiency is presented to eliminate the ambiguity of binocular stereo problem in medical optics. The local binary pattern/constrast (LBP/C) textures describing local features are firstly analyzed for both stereo pairs,and the initial matching costs is constructed from the features. Two-pass aggregation with adaptive support weight (ASW) for the costs are adopted only in the vertical and horizontal directions,which efficiently resolves ambiguity in feature matching. Initial disparity map is obtained via local Winner-Takes-All optimization from them. To solve the ambiguity from other aspects including textureless,repetitive patterns and occlusion etc.,disparity refining procedure consists sequentially of three steps:two-pass ASW-based disparity calibration,warping-based occlusion detection and epipolar-line-based least square fitting to handle occlusion. The experimental results indicate that this approach with texture cues and low complexity has high efficiency and concise structure,being easily implemented and eliminting ambiguity,so that it can obtain comparably accurate and piecewise smooth dense disparity map effectively.