# Multiple Preprocessing Hybrid Level Set Model for Optic Disc Segmentation in Fundus Images
## 2 Related Work  
Proposed OD segmentation algorithms can be roughly divided into five categories: threshold- [18,19], pattern- [20, 21, 22, 23], classification and clustering- [24, 25, 26], active contour model- [16, 27, 28, 29, 30, 31, 32, 33], and deep learning- [34, 35, 36]-based methods. In threshold-based methods, the ODwas segmented by OTSU and other thresholding methods in [18, 19]. Although they adopted multichannel information and achieved fast processing speed, it was susceptible to bright noise around the OD, and the segmentation accuracy was low. In pattern-based methods, a limited ellipse fitting method was used to segment the OD [20]. However, the OD is not completely elliptical, so this method was not accurate enough. Segmenting the OD with an active shape model is easily effected by the PPA and bright noise [21, 22, 23]. Regarding classification and clustering-based methods, in [24], superpixels were first segmented according to the similarity between each pixel and then classified with a random forest method. The K-means clustering method [25] and density-based spatial clustering method [26] are also used to segment the OD. However, both classification- and clustering-based methods require pre- and postprocessing, and cannot dispose of the effects of PPA and blood vessels. With the rapid development of deep learning in recent years, it has also been applied to OD segmentation. In deep learning-based methods, U-Net combines encoding and decoding information; due to this characteristic, U-Net, improved U-Net, or structures inspired by U-Net are widely used in OD segmentation. For example, U-Net was directly trained to segment the OD in [37, 38]. A deep-learning structure, M-Net, was developed in [34] that combines features in polar coordinates and Euclidean coordinates. U-Net and ResNet block were combined in [36] and [39], and U-shaped DenseNet was introduced in [40]. NENet, which was inspired by U-Net, was utilized to segment OD in [41]. In addition to the U-shaped network, a segmentation adversarial network (SAN) based on generative adversarial networks (GANs) was proposed in [35], and a fully connected network was improved by combining the distance and density features in [42]. A structure that combines DeepLabv3+ and MobileNet was proposed to segment the OD in [43]. However, it is difficult to obtain a large amount of training data, which is the difficult aspect of deep-learning methods in OD segmentation.  
Because of the high robustness and fewer parameters, the active contour model is also widely applied in OD segmentation. Some researchers proposed various types of active contour models, for example, variation-based [44], gradient-based [45], area-based [46] and shape-based [47]. In active-contour-model-based methods, the gradient-based level set model was used in [27, 29, 30, 31]. However, segmentation fails if the OD boundary is extremely smooth. The level set model was improved in [16, 28] by applying multiple energy functionals that included gradient-, area-, and shape-based energy functionals. A shape-based functional is able to limit the shape of the contour, the area-based functional is adopted to change the position and size of the prior shape model, and the gradient-basedfunctional is the main source of energy to find the OD boundaries. This method solves the above problem of OD segmentation failure, but the changed shape model is easily effected by PPA and bright noise. The area-based level set model was used in [32; 33], which mainly drove the contour according to the average pixel values inside and outside the contour. However, the contour is sensitive to blood vessels, PPA, and bright noise if only using an area-based energy functional, resulting in a reduction in segmentation accuracy.