基于小波变换的改进DDGVF医学图像分割算法

针对传统Snake模型不能很好地分割带有凹陷边缘图像的问题，提出了一种改进的结合小波的动态方向梯度向量流(简称DDGVF)模型.该算法首先利用小波变换的多尺度特性对待分割图像进行3层分解，然后在每层分解的图像下进行DDGVF算法的分割，不断获得更加精细准确的目标轮廓，最后达到准确分割.针对合成图像、含有噪声的图像和真实的CT以及MRI医学图像进行仿真实验.结果表明:改进算法能很好地解决传统Snake模型不能深入分割凹陷区域、捕获目标范围小等问题，并且具有分割时间较少的优点，是一种高效准确的医学图像分割算法.

DDGVF Medical Image Segmentation Algorithm Based on Wavelet Transform

To overcome the limitations of traditional snake image segmentation model， an improved dynamic directional gradient vector flow (DDGVF) based on wavelet transform was proposed. First， the image to be segmented was decomposed into 3 layers by using the multi scale analysis of wavelet transform， then the DDGVF segmentation was performed under each layer of the decomposition of the image， and finally， a more accuracy target contour could be acquired. Compared with the other image segmentation methods， the proposed algorithm can better segment the depression area of the target image， get a wider capture range and spend less time. The effectiveness of the improved algorithm has been proved through the simulation experiment of the synthetic image and the real medical image.