采用改进Snake模型的胸片肺野自动分割方法

为了解决传统Snake模型应用于X光胸片肺野分割时,对人工初始化轮廓的选择敏感、对凹陷区域分割不准确等问题,提出一种基于自动初始化Snake模型的X光胸片肺野自动分割方法.该方法首先通过Otsu法对原始图像进行二值化,得到包含肺野、背景区域的二值图像,并经过图像取反和连通域处理,运用形态学方法,得到只含有肺野区域的二值图像;然后,通过边界提取,完成对Snake模型轮廓的自动初始化;最后通过Snake模型的演化,得到分割结果.实验结果表明:该方法能摆脱Snake模型对人工初始化轮廓的依赖,提高分割的鲁棒性,同时对凹陷区域的分割更准确,具有更好的分割效果.

Automatic Segmentation Method of Lung Field on Chest Radiograph Using Improved Snake Model

In order to solve the problems such as sensitive selection of artificial initial contour and inaccurate segmentation of the concave area when traditional Snake model is applied to segmentation of lung field in X-ray chest film, an automatic lung field segmentation method based on the automatic initialization Snake model was proposed. In this method, the original image was binarized by Otsu method to obtain binarization images including lung field and background area. After image inversion and connected domain processing, the binary image containing only lung field area was obtained by morphological method. Then, automatic initialization of Snake model contour can be completed through boundary extraction. Finally, the segmentation results are obtained through the evolution of Snake model. The experimental results show that this method can get rid of the dependence of Snake model on artificial initial contour, improve the robustness of segmentation, and at the same time, segment the sunken area more accurately and have better segmentation effect.