Segmentation on edge preserving smoothing image based on graph theory

The presence of noise in an image will cause many undesired small holes in the segmented image. This effect causes an important efficiency decrease for classifying and describing objects. To remove the embedded noise while the edges of the image is still preserved, an edge preserving smoothing process must be applied. The smoothing process replaces the pixel intensity of the considered pixel by the average intensity of the most homogeneous mask among the proposed masks. The proposed masks can be preserved the thin region even its width is less than 3 pixels then, the smoothed image will be segmented by graph theory in order to obtain the higher accurate region's boundaries. In the mean time of segmentation process, the homogeneous threshold value has been applied to ensure that the maximum different gray value of each segmented region is controlled.