The intensities of pixels in images of multi-look Synthetic Aperture Radar (SAR) are usually modeled by gamma distribution. To obtain a segmentation result, the parameters that indicate the distributions should be estimated. However, traditional expectation maximization (EM) algorithm cannot approach the shape parameter through taking partial derivation of the likelihood probability. Thus, to solve this problem, the shape parameter should be made equal to the number of looks, and only the scale parameter should be estimated. Nevertheless, the conclusion is obtained under the assumption that a pixel is constructed by infinite scattering units, which is impossible especially in high-resolution SAR images. Apparently, that assumption is unreasonable and will lead to inaccurate estimation of the corresponding parameters. Moreover, gamma distribution with fixed-shape parameter cannot completely describe the characteristics of intensity distribution in multi-look SAR images. Thus, this paper proposes a scheme for solving the parameter based on Expectation/Conditional Maximization (ECM) algorithm and develops a new segmentation algorithm for multi-look SAR images. First, the neighborhood system on the label field is considered, and Markov random field model is employed to define prior distribution. Then, intensities in each homogeneous region of a multi-look SAR image are modeled by gamma distribution, in which both shape and scale parameters are considered as variables. Finally, the gamma distribution along with the prior distribution constructs the posterior distribution, on which purpose it should be maximized. In this paper, Metropolis-Hastings algorithm is used as a random sampling method in estimating the marginal posterior probability model and changing the labels of each pixel. However, the estimation of the shape parameter remains a problem because it cannot be solved through partial derivation. ECM algorithm introduces the Newton iterative method to approach the real value of parameters, which cannot be directly solved from an equation. Therefore, shape parameter is evaluated by ECM algorithm, and all variables are obtained ultimately. The proposed algorithm is applied to simulated and real multi-look SAR images. Experimental results show that the proposed method can accurately estimate shape and scale parameters of gamma distribution. The proposed algorithm's user, product, and total accuracies, and kappa coefficient are all higher than those of the EM algorithm's, and the estimated values of the algorithm are much closer to the real values. The proposed algorithm can estimate the parameters of gamma distribution and the corresponding reality of the label field under the circumstance of maximized posteriori probability. Experiments on simulated and real multi-look SAR image verify the effectiveness and feasibility of the proposed algorithm, and the shape and scale parameters of gamma distribution can quickly converge to their stable state in a considerably short time. The ECM algorithm introduced in this paper can estimate the value of the shape parameter in gamma distribution, which is treated as the number of looks in the traditional EM algorithm. Thus, the connections between different looks are included in the distribution, and the corresponding segmentation results are improved.