The processing and analysis of circle-like particle image has a wide application in industry, agriculture, and medical treatment, such as the grain, rock, colony, blood cell, bubble, bar, and etc. After these particles being segmented from the background, we can count the number, extract their features (e.g. area, perimeter, diameter, color etc.), and then we can analyze its quality. For example, colony counting is a basic and important job for quality inspection in agriculture, food, and medical treatment analysis. The number of colonies is an important quality parameter in evaluating the water pollution. In earlier, it is inspected mainly by operator and the process is complicated, time-consuming, and low-efficiency. Moreover, the result tends to subjective, error distinctive, and bad recurring. Image processing and analysis can free operator from the labored work, and can improve the counting and analysis accuracy highly. As a result, it is widely applied and becomes a research hotspot in recent years.Segmentation is a key step during the particle image processing. The segmentation result has direct influence on the counting accuracy and the following processing. However, segmentation is also a classic problem in image information engineering. Though numerous domestic and international scholars have extensive lucubrated into this subject and have presented many applicable algorithms, there is still no such an algorithm that can be applied to any test image and get the best result. Especially in particle images, various in size and modality, and tend to cluster into each other, the particles is more difficult to be segmented. For this, based on the analysis of the presented segmentation algorithms, this dissertation studies new segmentation algorithm to improve the accuracy and speed with the colony image as experiment object.This dissertation studies mainly on two spots. One is on the separation algorithm to separate the clustering particles in particle images; the other one is on the system implementation to develop a particle image segmentation and counting system with flexible combinability of algorithm sequence.Firstly, this dissertation introduces some basic concepts about particle image processing and analysis, such as binary image, adjacency, connectivity, thresholding segmentation, boundary tracing, labeling, and etc. Then, the boundary-tracing algorithm is described in details and its efficiency is analyzed. On this, several new algorithms are presented, fast labeling, separating and Euclidean distance transformation algorithms