关键词: 贝叶斯方法, 图像分割, 贝叶斯分类器

Abstract: On the basis of researching of Bayesian classifier, a new merging method based on Bayesian classifier for the medical image Segmentation result is brought out. The new method improves the accuracy of image Segmentation, and provides more reliable support for the computer identifying of elements contained in medical image automatically. Firstly, the computer distinguishes the visible compositions such as erythrocyte, leukocyte, pipe type cells, epithelium and the crystallization in urinary sediment images. Secondly, segments all kinds of cells correctly. For the existing of background noises in images, preprocessing is needed to eliminate those noises before segmentation. Preprocessing adopts the mathematical morphology methods, and carries out edge pick-up, gradient graph double value, corrosion and expansion. In the end, the new method adopts the biggest posterior probability of Bayesian method into the combination of incomplete object entities during image segmentation, which improves the integrality of the segmentation result, and provides good foundation for image identification. Bayesian classifier is used in classifying of object entities. Experiment result shows that the new method is suitable for urinary sediment images auto analysis system.

Key words: Bayesian Method, Image Segmentation, Bayesian classifier