Abstract: In order to overcome the shortcomings of current image segmentation algorithms, such as large segmentation error and long segmentation time and inability to segment online image, we proposed an image segmentation algorithm based on combination of clustering analysis and active contour model. Firstly, the original image was denoised and roughly segmented by clustering analysis algorithm. The roughly segmented result was taken as the initial contour line of the active contour model. Secondly, the active contour model was used to fit the contour of different regions of the image according to the initial contour line to realize the fine segmentation of the image. Finally, it was compared with clustering analysis algorithm, active contour model and the current classical image segmentation algorithm. The experimental results show that the proposed algorithm overcomes the shortcomings of the current image segmentation algorithm, improves the efficiency and accuracy of image segmentation, it is insensitive to noise and has strong robustness. The overall image segmentation effect is significantly better than that of the contrast algorithm.

Key words:  , image segmentation efficiency, noise interference, initial contour line, active contour model, clustering analysis