A Noisy Image Segmentation Method Based on Improved Active Contour Model

Using active contour model to segment image is a classical image segmentation method. But the effect of this method is not good. The current active contour model is sensitive to noise, and it is difficult to achieve accurate segmentation of weak boundary image. This paper proposes an image segmentation algorithm based on gradient vector flow active contour model. Firstly, the wavelet transform is used to process the segmented image to solve the interference of noise on image segmentation. Then, the gradient vector flow active contour model is used to segment the denoised image to fit the contour curve evolution process of different regions in the image, so as to realize the segmentation of different regions. Compared with other current image segmentation algorithms, the simulation results show that the gradient vector flow active contour model can segment the image with high accuracy, and the segmentation time is greatly reduced, and the anti-noise ability is improved. The overall performance of the algorithm is obviously better than other image segmentation algorithms.