一种三维CT图像中的线状目标检测方法

当前三维CT图像广泛应用在工业和医学中, 对于工业无损检测和医学上的病灶分析具有重要的研究意义。线状目标广泛存在于医学和工业的CT图像中, 为了实现三维空间中线状目标检测, 传统的直线检测方法有Hough和Radon变换, 但是计算量很大, 而且不适合曲线检测, 对于三维图像来说, 计算更为复杂。因此提出一种基于距离变换, 并通过端点和拐点检测提取线状目标检测算法, 不仅对于直线目标有较好的检测效果, 对特定曲线也有较好的检测结果, 而且通过检测距离变换的距离, 自动地检测线状目标的粗细尺度属性。实验证明, 该方法具有较好的检测结果。

Method of line target detection in 3D CT image

Currently, 3D CT images play an important role in the lossless detection of industry, and the focus analysis of medicine are widely used in the industrial and the medicinal field. In order to detect the line targets widely existed in the CT images, traditional methods were Hough transform and Radon transform. However the complexity of them were huge in the 3D space, what's more, they were not applicable by curve targets. So this paper proposed a method based on distance transform and terminal node, turned nodes detection to detect line targets. It was not only suitable to the line, but also to the curve. Furthermore, it could get the thickness of the line targets automatically from the step of distance transform. The result of experi-ments shows the availability of this method.