The simple linear iterative clustering (SLIC) method does not perform well in edge detail processing in image segmentation. Thus, a modified color image segmentation algorithm is proposed combining a “parameter-controlled modified simplified” pulse coupled neural network (PC-MSPCNN) and SLIC. The proposed algorithm works in two steps. First, the weighted matrix and connection coefficient of the MSPCNN model are improved, and the auxiliary parameters are added to improve the accuracy of the segmentation. Then, the color image is input into the PC-MSPCNN model, and the edge of the object is divided according to the distribution of the output Y value in the improved model so that the segmentation results appropriately fit the edge of the object, and the proposed similarity criterion is used to merge the scattered fragments to reduce the complexity of subsequent processing. Second, based on the measurement similarity of the SLIC, the internal activity term U values of the three RGB channels in the PC-MSPCNN are introduced to achieve weighted fusion clustering for the remaining parts of the image to improve clustering. Experimental results show that the proposed algorithm can accurately fit the edge of an object, considerably reduce the number of pieces, and effectively improve adherence of the image edge.