## 2 Related Work
### Classical Image Processing Based Methods  
Classical image-processing-based methods are the earliest computer-aided methods for EM detection. Classical image-processing-based methods contain two subcategories of detection methods, segmentation-based methods, and classification-based methods.  
Thresholding-based methods are the most used technologies for image segmentation, such as in [31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48]. Thresholding methods are the most commonly used methods in image segmentation. In addition, thresholding methods can select an appropriate threshold for detection according to different EMs, which gives these methods strong generalization abilities. In [32], an area threshold was applied for _Chlamydomonas_ and _Chlamydomonas_ _bicuspidata_ detection. In [37], the multiple thresholds method was employed for motile microorganisms. Multiple thresholds were firstly applied to binarize input images. Then, all white regions are regarded as EMs. In [40], the color threshold was applied for tubercle bacillus detection. In [43], adaptive threshold and global threshold were applied for nematode detection. An adaptive threshold was employed for binarizing the original image. Then, a global threshold was applied for extracting reference labels. By combining these two processed images, the nematode can be detected. Among all these works, the Otsu threshold is the most used one. In [33; 42; 44; 45; 46], the Otsu threshold is applied for different EMsdetection. The main idea of Otsu was to select an optimal threshold automatically from a gray-level histogram by a discriminant criterion [49]. The Otsu threshold can provide good results with simple calculations. Even if the gray value of the object to be segmented is similar to the gray value of the background, the Otsu threshold can achieve good segmentation results. However, due to the limitation of the calculation method, when the difference between the foreground and the background area is too large, the Otsu threshold cannot achieve a good segmentation result [50].  
Classification-based methods apply shape features, geometric features, color features, texture features, and statistical features for EM detection, such as in [51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65]. In [51; 52; 55], shape features were selected as vital information for EM detection, including contour features, area features, squareness, angular, roundness, etc. In [56], a contour feature was used for detecting _Methanospirillum hungatei_ and _Methanosarcina maze_. In [52], a _C. elegans_ nematode worm detection method based on angular features was designed. In [55], roundness was selected as the criterion for judging whether the detected objects are Rotavirus-Aparticles. In [57; 59; 60; 61; 62; 63; 66], geometric features were selected for EM detection. In [60], the area was regarded as an important indicator for judging the presence of bacteriophage. In [61], the most suitable combination of some kinds of geometric features was selected and applied for bacilli detection. In [62; 63], an automatic detection method based on area-to-length ratio was proposed for six different airborne fungi spores. In [57; 61], color features were employed for initial screening regions containing EMs. In [64], a _Anabaena_ and _Oscillatoria_ detection method based on texture features was proposed. From all these classification based methods, we can find that shape features are the most suitable feature for EM detection. In addition, detection methods by combining different features can achieve improved detection performances than a single feature.