Abstract: Optical coherence tomography (OCT) is an indispensable tool used for the diagnosis and identification of ocular fundus disease and nondestructive, rapid, and high-resolution imaging of the living retinas. The attendant research focuses on the development of computer-aided methods to help ophthalmologists make judgments regarding the morphological changes of retinal tissue and acquire tissue characteristic parameters. Realizing the segmentation of retinal tissue in OCT images is the key aspect of this kind of research. Mathematical morphology, which has been widely used in the fields of image detection, shape analysis, pattern recognition, and computer vision, uses different structural elements to measure, extract, analyze, and identify image targets. However, traditional morphological structure elements cannot be adaptively changed on the basis of the structural characteristics of the images. In this study, an algorithm for generating morphological adaptive structural elements was proposed on the basis of an immune genetic algorithm, which the detection of retinal tissue edges in optical coherence tomography (OCT) images was applied. First, the image is preprocessed by denoising and coarse segmentation and then the image is divided into several sub-images. Second, the adaptive structure elements are computed using an immune genetic algorithm for each sub-image. A string of binary numbers of fixed length is initially randomly generated as an antibody and then converted into a format of structural element. The fitness of an antibody is defined by the two-dimensional entropy of the image and the optimal antibody and structural elements are identified according to the structural characteristics of the subimage itself. Finally, with these optimal structural elements, morphological edge detection is performed to obtain the segmentation results of each sub-image combined with those of each sub-graph to realize the extraction of the target boundary of the whole image. The experimental results show the proposed method to be effective in the boundary extraction of images.