自适应权重CV模型在超声相控阵图像分割中的应用

图像分割是超声相控阵无损检测图像三维重建的关键环节，分割精度和效率是图像三维重建准确性与实时性的重要保障。由于超声相控阵无损检测图像中含有大量噪声且灰度不均匀，在使用传统的CV模型分割检测图像时采用固定迭代步长，这导致分割效率较低且精度不高。本文利用分水岭算法过分割的特性，在对图像做分水岭变换后，统计各区域中的像素数目与灰度信息，从而得到一个权重矩阵并引入CV模型中，得到一种自适应权重CV模型。在水平集函数迭代过程中，权重矩阵可以根据图像信息自适应调整迭代步长。实验表明，与CV模型和LBF模型相比，本文提出的权重CV模型在分割超声相控阵无损检测图像时，具有更高的效率和分割精度。

Application of Adaptive Weight CV Model in Ultrasonic Phased Array Image Segmentation

Image segmentation is the key link in the 3D reconstruction of ultrasonic phased array NDT, and the accuracy and efficiency of the segmentation are the important guarantee for the accuracy and real time of the 3D reconstruction of the image. The ultrasonic phased array NDT image contains a lot of noise and intensity in homogeneity. Traditional CV model uses fixed iterative step to segment the ultrasonic phased array NDT image, which leads to low segmentation efficiency and low precision. This paper makes use of the characteristics of the over segmentation of the traditional watershed algorithm to make a watershed transformation to the image. The number of pixels and intensity information in each region are counted, and a weight matrix is obtained. The matrix is introduced into the CV model, and the adaptive weight CV model is obtained. In the iterative process of level set function, the weight matrix can adaptively adjust the iteration step size according to the image information. The experiment shows that, in the segmentation of the ultrasonic phased array image, compared with the CV model and the LBF model, the weighted CV model proposed in this paper has higher efficiency and segmentation accuracy.