Texture image segmentation using without re-initialization geodesic active contour model

A novel method of texture image segmentation is proposed, which has three advantages compared to other active contours. First, by combining the gray levels of pixels and texture information of an image, this method can be used for segmentation of a texture image or a non-texture image. Second, the method has low computation complexity because local binary pattern (LBP) is employed to extract texture features. And last, the proposed algorithm can avoid the additional computation problem without re-initialization of signal distance function repeatedly. The segmentation tests show that the proposed segmentation method is efficient, accurate, fast and robust.