## 2 Related Work
### Classical Counting Methods  
Image segmentation is the most significant part in microorganism-counting task. As shown in Table 1, the classical methods contain thresholding, edge detection and watershed methods [56]. For thresholding approaches, the selection of threshold determines the result of segmentation. The most used approaches are iterative thresholding and Otsu thresholding at present. Otsu thresholding can achieve satisfactory segmentation results for most images [57]. Edge detection approaches can extract all boundaries in an image, and then each close region can be separated [58]. Watershed is one kind of region-based segmentation approach, which can be calculated by iterative labeling [59]. The satisfactory segmentation result can be received though in an image with weak edges. Hough transformation was proposed for line or circle detection tasks in image with strong anti-noise capability and high accuracy, which can be applied for circular microorganism counting tasks [60]. In [35, 36, 37], various thresholding methods were applied for microorganism counting. In [35], an adaptive thresholding was used for microorganism segmentation, and after that, the minima function was applied to locate the center of each colony for counting. The work [36] applied Otsu thresholding for bacteria segmentation, and the hypothesis testing was then applied for debris erasing. In [37], the contrast-limited adaptive histogram equalization was applied to enhance the plate contour first. Then, the Otsu threshold was applied to detect the plate region and binarize the images automatically. Finally, the colonies were separated and counted. The works [38, 39, 40] used several edge detection methods for microorganism counting. In [38], five different combination methods applying for microorganism segmentation were compared, such as Gaussian Laplacian and Canny filters. Then, the concave surface between the connected colonies was detected for counting. The works [39, 40] used Sobel and Laplacian filters for the edge detection of bacteria images. The works [41, 42, 43, 44] use watershed-based methods for microorganism counting. In [41], watershed was applied for separation of clustered colonies of bacteria images. After \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Subcategory** & **Related Work** \\ \hline \multirow{3}{*}{Classical Methods} & Thresholding-Based Methods & [35, 36, 37] \\  & Edge Detection-based Methods & [38, 39, 40] \\  & Watershed-Based Methods & [41, 42, 43, 44] \\ \hline \multirow{3}{*}{Machine-Learning-Methods} & Hough Transformation & [45, 46, 47] \\  & Classical Machine-Learning-Based Methods & [48, 49, 50, 51] \\ \cline{1-1}  & Deep-Learning-Based Methods & [52, 53, 54, 55] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Microorganism image counting methods. that, the circularity ratio was calculated for colony counting. In [42], marker-controlled watershed was applied for bacteria segmentation. Then, the number of colonies was estimated as the ratio of cluster area to an average colony area. In [43], Otsu and adaptive thresholding were applied for image binarization. Then, the combination method of distance transform and watershed was applied for bacteria counting. In [44], watershed was used for image segmentation. After that, the gray level co-occurrence matrix (GLCM) of the image was extracted and classified using SVM. The works [45; 46; 47] used Hough-transformation-based methods for microorganism counting. In [45], the iterative local threshold was used for bacteria segmentation, and then a Hough circle transformation was applied to separate clustered colonies into a single colony. In [46], a median filter was applied for denoising. Then, the circular area was detected using the Hough transform to obtain only the inner area of the dish. Afterward, Gaussian adaptive thresholding was applied for bacteria segmentation. Finally, cross correlation-based granulometry was used to count the bacteria colonies.