The work aims to design an image edge extraction scheme based on Hadamard fusion of different spatial structures, regarding the effect of noise on the edge in the image edge extraction algorithm, thus easily leading to such defects as low edge location accuracy, false edges and missing detection. Firstly, by calculating the variance between pixels and adjacent points, the structure of pixels was analyzed, and the maximum probability distribution matrix (MPDM) of edge points was obtained, representing candidate edge sets by means of MPDM. Secondly, by analyzing the brightness between neighborhood points, the maximum and minimum differences between pixels and their 4 adjacent pixels were calculated, and the maximum and minimum difference matrices were obtained. The Logistic regression analysis was introduced to normalize the two matrices, and a weight matrix (WM) was obtained. Then, MPDM and WM were fused by Hadamard product model, and thus the edge segmentation threshold function was designed. Finally, the edge of the real image was detected by removing the non edge points based on the comparison of the WM and the segmentation threshold. The experiment showed that, compared with the current edge extraction method, the proposed method could effectively suppress the noise, and get clear and complete edges, good edge thinning degree and smoothness, and achieve greater advantages in the FOM and ROC objective evaluation. The proposed algorithm has good edge extraction accuracy, and good application value in image processing and packaging barcode.