# Fast Segmentation Algorithm for Cytoid Macular Edema Based on Omnidirectional Wave Operator
###### Abstract  
Optical coherence tomography (OCT) is widely used in the field of ophthalmic imaging. The existing technology cannot automatically extract the contour of the OCT images of cystoid macular edema (CME) and can only evaluate the degree of lesions by detecting the thickness of the retina. To solve this problem, this paper proposes an automatic segmentation algorithm that can segment the CME in OCT images of the fundus quickly and accurately. This method firstly constructs the working environment by denoising and contrast stretching, secondly extracts the region of interest (ROI) containing CME according to the average gray distribution of the image, and then uses the omnidirectional wave operator to perform multidirectional automatic segmentation. Finally, the fused segmentation results are screened by gray threshold and position feature, and the contour extraction of CME is realized. The segmentation results of the proposed method on data set images are compared with those obtained by manual marking of experts. The accuracy, recall, Dice index, and F1-score are 88.8%, 75.0%, 81.1%, and 81.3%, respectively, with the average process time being 1.2 s. This algorithm is suitable for general CME image segmentation and has high robustness and segmentation accuracy. image segmentation; optical coherence tomography; cystoid macular edema +
Footnote †: journal: Medical Imaging  
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Footnote †: journal: Medical Imaging  
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Footnote 1: email: _Applied_  
Footnote †: journal: Medical Imaging  
of the retinal cyst area is an effective tool for evaluating retinal diseases, which can detect, diagnose, and treat retinal diseases early. Image segmentation methods are divided into traditional segmentation methods and neural network methods. In traditional segmentation methods, the threshold-based method is not suitable for processing complex images, like Otsu [9]. The region-based growing method is susceptible to noise interference thus causes false segmentation easily. Algorithms based on level sets, active contour models, graph theory, and graph cuts have excellent segmentation capabilities, but they are time-consuming. The segmentation algorithm based on machine learning is limited by the number of data sets and the accuracy of manual labeling, and low robustness. Among traditional methods, [PERSON] et al. clustered the liquid area by the fuzzy C-means method and then used the fuzzy level set to realize target segmentation [10]. Since the traditional level set method cannot converge or has convergence errors locally on the surface, [PERSON] et al. put forward a level set evolution model based on distance regularization (DRLSE) [11], which greatly improves the efficiency of the level set algorithm. [PERSON] et al. used the GVF Snake model [12] in the active contour model combined with the nonlinear anisotropic diffusion filter to semi-automatically segment CME, which has high accuracy but takes a long time. [PERSON] et al. performed segmentation from a region of interest (ROI) based on the shortest path method of graph theory and used K clustering for cyst segmentation [13]. [PERSON] et al. combined classification based on kernel regression with graph theory to segment retinal boundaries. The algorithm has high space complexity and time complexity [14]. For the machine learning method, [PERSON] et al. combined unsupervised feature representation and graph theory segmentation [15]. The use of machine learning methods is limited by the shortage of training data sets and requires manual division of the cyst area as the truth map to put in the network. Thus, the pre-training cost for machine learning methods is too high. The wave algorithm [16] is a novel image segmentation algorithm, which obtains the potential energy of pixels in the image by analogy with the potential energy equation of fluid mechanics. Then, it obtains the target boundary according to the potential energy characteristics.