论文标题
一种神经模板匹配的方法来检测膝关节区域
A Neural Template Matching Method to Detect Knee Joint Areas
论文作者
论文摘要
在本文中，新方法被认为可以检测双侧PA固定屈曲膝关节X射线图像中的膝关节区域。该方法是模板匹配类型的，其中距离标准基于负归一化的互相关。当选择模板时，手动注释仅在单侧图像的一侧进行。最好的匹配贴片搜索被配制为无约束的连续域最小化问题。对于最小化问题，考虑了不同的优化方法。本文的主要方法是一种可训练的优化器，其中教授该方法从其看起来像模板的输入图像中获取缩放，并可能从其输入图像中旋转贴片。在实验中，我们比较不同优化方法发现的最小值。我们还查看了一些测试图像，以检查最小值与膝盖区域局部程度之间的对应关系。看来仅对单个图像进行注释才能很精确地检测膝关节区域。
In this paper, new methods are considered to detect knee joint areas in bilateral PA fixed flexion knee X-ray images. The methods are of template matching type where the distance criterion is based on the negative normalized cross-correlation. The manual annotations are made on only one side of a single bilateral image when the templates are selected. The best matching patch search is formulated as an unconstrained continuous domain minimization problem. For the minimization problem different optimization methods are considered. The main method of the paper is a trainable optimizer where the method is taught to take zoomed and possibly rotated patches from its input images which look like the template. In the experiments, we compare the minimum values found by different optimization methods. We also look at some test images to examine the correspondence between the minimum value and how well the knee area is localized. It seems that making annotations only to a single image enables to detect knee joint areas quite precisely.
