基于梯度向量流活动轮廓模型的图像分割研究

Image Segmentation Based on Gradient Vector Flow Active Contour Model

为了提高图像分割精度,加快图像分割的速度,设计了基于梯度向量流活动轮廓模型的图像分割算法。首先对当前图像分割的研究现状进行分析,找到引起图像分割误差大的原因,然后小波变换对待分割图像进行处理,抑制噪声对图像分割结果的不利影响,然后采用梯度向量流活动轮廓模型对去噪后图像进行分割操作,拟合图像中不同区域的轮廓曲线演化过程,从而实现不同区域的分割,最后与当前其它的图像分割算法进行了仿真对比实验。结果表明,梯度向量流活动轮廓模型可以对图像进行高精度的分割,而且分割时间大幅度减少,提升了抗噪能力,图像分割整体性能要明显优于其它图像分割算法。

In order to improve image segmentation accuracy and speed up image segmentation,an image segmentation algorithm based on gradient vector flow active contour model is designed.Firstly,the current research status of image segmentation is analyzed,and the reasons for the large error of image segmentation are found.Secondly,the wavelet transform is used to process the segmented image to suppress the adverse effects of noise on the image segmentation results.Thirdly,the gradient vector flow active contour model is used to segment the denoised image,and the contour curve evolution process of different regions in the image is fitted.The segmentation of different regions is realized.Finally,the simulation and comparison experiments are carried out with other current image segmentation algorithms.The results show that the gradient vector flow active contour model can segment the image accurately,and the segmentation time is greatly reduced,the anti-noise ability is improved.The overall performances of image segmentation are obviously better than other algorithms.