基于免疫遗传形态学的视网膜光学相干断层图像边缘检测方法

Edge detection method of retinal optical coherence tomography images based on immune genetic morphology

提出了基于免疫遗传算法的形态学自适应结构元素生成算法,并将其用于光学相干断层成像(optical coherence tomography,OCT)图像中视网膜组织边缘检测.首先将图像进行去噪和粗分割的预处理,并将图像划分为若干子图像;其次对每一子图利用免疫遗传算法求取自适应结构元,初始随机生成固定长度的二进制数串作为抗体,并将其转化为结构元素格式,以图像二维熵定义抗体适应度,根据子图像本身结构特征信息,寻找最优抗体结构元素;最后利用寻优得到的各结构元素对子图进行形态学边缘检测,合并各子图的分割结果,实现整体图像目标边界提取.实验结果表明了该方法在图像目标边界提取的有效性.

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.