Segmentation by Edge Detection – This method is done by identifying the different types of boundaries (spots, lines, curves). The process of locating and identifying sharp lines with fine discontinuities is called Edge Detection and it is helpful when needed to retain the change between grey tones in the image.
Segmentation by Clustering is the task of dividing the population (data points) into a few groups, based on the similarity of data points. In one group are gathered data points which are way more similar or related in any kind than the population in other groups. These kinds of groups are recognizable and known as clusters. The most commonly used clustering algorithm is the k-means algorithm which helps the improvement of efficiency and performance.
Segmentation by Artificial Neural Network– Image segmentation processing is performed on the raw image in order to detect and locate the bright spot. The level of accuracy of Artificial Neural Networks is tested by creating a labeled set of images for the determination of true and false positive values during the segmentation processing.
Segmentation by Thresholding is the simplest method of image segmentation. This kind of algorithm directly divides the image grayscale information processing based on gray values of different targets. This method can be divided into 2 methods:
Global threshold method– This method splits the image into 2 regions of background and targets by a single threshold.
Local threshold method– This method implies the need for selecting the multiple segmentation threshold and divides the image into multiple targeted backgrounds and regions by multiple thresholds.
These are the most commonly used methods for image segmentation, so feel free to explore further and choose which one fits your need the most.