Study On The Segmentation Of Circle-Like Particle Images

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 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.And the cell auto-recognition systems are significant in computer assistant diagnosis, especially in the instances of lacking experts. Since the cell images are various in size and modality, and tend to cluster into each other, the cells are more difficult to be segmented, by now there isn't a segmentation algorithm that fits all kind of images. In this paper, the problem of the image segmentation of overlapping cells is mainly studied. First, traditional pretreatment technologies and segmentation methods are discussed. We can know merit and shortcoming of them. And an improved algorithm based on local watershed transform was presented in this work. It is constructed with two similar stages. Firstly, the mathematics morphology is produced. The traditional pretreatment technologies and mathematics morphology can eliminate background noise and holes effectively together. And single seed regions are marked and removed from the clustering paddy image according to circle threshold( Pth ); Adopt different process watershed algorithm, Since the whole process is based on local watershed algorithm, it eliminate the phenomena of over-segmentation. Cells counting and analysis system developing. With software engineering developing methods, an image processing and analysis frame system is constructed which covers rich image processing and analysis algorithms.