Contour detection plays an important role in shape-based object recognition tasks. It is an intractable problem of how to automatically detecting the contour of object from natural scenes. This is mainly because there exist plenty of extraneous elements in background. Parallel physiological and anatomical studies showed that the response to the stimulus in the receptive field suffers iso-orientation surround inhibition. Thus the less of this suppression in areas where there is a change in orientation results in enhanced saliency of isolated edges and region boundary. We proposed a biologically motivated model of contour detection, which reduces edges from a texture background, while retaining object contours. The inhibition step may be expected to improve contour detection performance when images contain objects of interest on a textured or cluttered background.