Nerve cells image segmentation is of great significance for neuroscience research, but the complexity of the nerve cells submicroscopic structure and the quality problem of the missing and fuzzy of the boundary produced by transmission electron microscope(TEM) have making it being a problem in medical image processing. Based on the significant local clustering characteristics of nerve cell TEM images, a local feature-constraint information based TEM image segmentation algorithm is proposed by using the superpixels technology. First, superpixel structure is built based on the graph-based model. Then the local spatial information of superpixels based on MRF spatial neighborhood are extracted to solve the complex neighborhood information and space structure. Finally, the segmentation results can be obtained by the MRF model optimization and superpixels merging. The research results show that the proposed algorithm is accurate and robust with better describtation of submicroscopic structure.