基于边缘流的改进梯度矢量流算法及其在淋巴结超声图像分割中的应用

目的 提出一种基于边缘流的梯度矢量流（gradient vector flow，GVF）形变模型的图像分割方法，并用于淋巴结超声图像的分割。方法 综合图像灰度和纹理特征构造边缘流，使每点的边缘流矢量指向最近的边缘，再由边缘流扩散得到GVF场作为形变模型的外部势力，引导模型形变实现图像分割。结果 在给定4个标记点的条件下，实现了对淋巴结超声图像的半自动分割。结论 将边缘流引入GVF将明显改善对低对比度超声图像的分割效果。

An Improved Gradient Vector Flow(GVF) Snake Model for Lymphatic Ultrasonic Image Segmentation Based on Edge Flow

Objective To put forward an improved gradient vector flow (GVF) snake model based on the edge flow for segmentation of medical ultrasonic images of the lymph node. Method An edge flow vector was constructed at each pixel location basing on the integration of image intensity and texture characteristics to make the edge flow vector point to the direction of potential boundary pixels. The GVF field obtained by the diffusion of the edge flow vector was regarded as the external potential force of the snake model. Then the curve propagation was guided by the improved GVF snake model. Result A semi-automatic segmentation of ultrasonic images of the lymph node was accomplished under the condition that the four marks were provided by an expert.Conclusion By the integration of the edge flow into the GVF snake model, the segmentation quality is obviously improved for low contrast ultrasonic images.