Abstract: A method for image processing and defect analysis based on edge information and structural feature extraction was proposed to address the problem of low defect detection accuracy caused by uneven grayscale distribution and overlapping structural and defect information in rocket engine nozzle brazing X-ray detection images. Firstly, quality enhancement preprocessing was performed on the original detection image through methods such as brightness matrix, Gaussian filtering, and sharpening; Then, the Canny operator and Hough transform were used to extract edge information and line segment structure information from the image, and the sine like structure information was obtained through adaptive threshold processing; Finally, differential processing was performed on the obtained edge information, line segment information, and sinusoidal like structure information, and defect information was determined. Based on the proposed algorithm, corresponding software was developed to analyze the actual radiographic inspection images of the engine nozzle brazing seam. The processing results showed that the proposed method can effectively overcome the effects of uneven regional grayscale, mixed information of structures and defects, and achieve unified processing of radiographic inspection images and extraction of defect information, with high detection efficiency and accuracy.