Image segmentation is an important process of image analysis. Most of the published approaches for image segmentation need to set appropriate parameter values to cope with the uncertainty problem. However, the parameter values are usually problem dependent and not easily obtained. An efficient image segmentation algorithm using a generic and non-parametric approach is proposed. In the algorithm, the fuzzy-c-means algorithm based on the measurement of the contrast and the compactness between adjacent regions is used to split the image into many small regions first. Then, the validity of existing common edges between regions is checked to determine the mergence probability based on the rank of significance of their contribution using orthogonal array experiments. This algorithm can lead to better computational efficiency and higher segmentation accuracy. Furthermore, the most discriminative regions can be segmented in order with/without a predefined number of regions in obtaining the best and robust segmentation results. Experimental results using artificial and nature images are used to demonstrate the feasibility and efficiency of the proposed algorithm.