# Using an Artificial _Physarum polycephalum_ Colony for Threshold Image Segmentation
## 2 Related Work  
Threshold image segmentation is a widely used segmentation technology, where an image is regarded as a combination of two types of areas, i.e., target areas and background areas with different grayscale levels [19]. Taking advantage of the difference between the target areas and the background areas in the grayscale characteristics, threshold image segmentation can generate a corresponding binary image by extracting the target areas [20]. In threshold image segmentation, a reasonable threshold is essential to determine whether each pixel in the image should belong to the target areas or the background areas. Hence, the key in threshold image segmentation is to search for the appropriate threshold accurately and quickly. To find optimal thresholds in image segmentation, recent researchers selected different methods as follows. First, the threshold in most cases can be determined by the separation between gray peaks in a histogram of the original image. The gray level of an original image usually presents normal distribution in target and background areas, so the image gray histogram tends to have two single peaks corresponding to the target and background areas, respectively [21]. The adjacent pixels between the target or background areas are correlated in the gray level, but the pixels at the junction of the target and background areas are different in the gray level [22]. In this case, the bottom of the gray level valley between the two peaks can be selected as a threshold. Second, [PERSON] proposed the inter-class variance threshold segmentation method in 1979, which divides the image into foreground and background parts based on its grayscale distribution [1; 7]. The segmentation values of the foreground and background are calculated using the inter-class variance method to obtain the threshold, so the foreground is the part we segmented. Ref. [23] described an Otsu multi-threshold image segmentation based on an improved particle swarm algorithm. Third, multi-threshold segmentation is based on multiple valleys appearing in the grayscale histogram as segmentation points, using multiple thresholds to segment multiple peaks [2]. Therefore, there are as many target regions as there are peaks. Ref. [5] illustrated a fault diagnosis algorithm of gearboxes based on GWO-SCE adaptive multi-threshold segmentation and subdomain adaptation. The gray value of pixels may change by noise and produce an imbalance in the histogram of an original image, so the bottom of a histogram may be filled to generate new peaks [7]. Hence, the bottom of the gray level valley between the two peaks can be segmented by multi-threshold [9; 10; 13]. Fourth, multi-level segmentation can segment an image into multiple sets of segmentation results with different levels of detail at different scales and can determine the relative size between the targets and background [24]. [PERSON]. [25] employed a multi-level image threshold segmentation using an improved Bloch quantum artificial bee colony algorithm. In many original images, there are a lot of small targets or large targets, so it is not easy to determine the valley bottom in the histogram. Traditional single-level image segmentation results in severe loss of final feature hotspots and resolution, leading to a lack of detail characterization ability in segmentation results [26; 27]. Multi-level threshold segmentation forms a multi-level network structure representation of the same image at different scales [28; 29]. Fifth, dynamic threshold segmentation does not require artificially setting thresholds, but rather segments based on the features present in the image [20]. Dynamic threshold segmentation generally compares the original image with the processed image to generate a difference image and then calculates the bright or dark regions in the difference image. The brightness of the target and background may vary with the illumination, such as maize diseases [7] and cucumber seedlings [14].