Direct and indirect image rotation estimation methods of orthopedic x-ray images

For many medical questions, X-ray imaging belongs to the gold standard for diagnosis, treatment planning, treatment guidance, and surgery assessment. To improve the reading performance, standardized image rotation is an important step. We propose a new algorithm to estimate the correct image rotation. For many body regions, one line can be defined that is aligned with the upright orientation of the X-ray image. This line can be, for example, the shaft axis of a long bone or the axis of the spine. In this paper, we propose a strategy to automatically align X-ray images with their standard orientation. In a first step, the heatmap of this line is determined using the segmentation network D-LinkNet. The rotation direction, up to a top-down flip, is obtained by computing the orientation of the main axis of this heatmap. For the orientation computation, we compare PCA and Hu moments. While the PCA requires to threshold the heatmap, Hu moments can be used directly on the output values of the network, preserving the (un)certainty of the segmentation. We compare these two methods with a ResNet-18 for the direct estimation of the image rotation on 220 X-ray images from the MURA dataset showing the wrist in the AP view. With the heatmap-based approach followed by Hu moments analysis, the median absolute error for the angle estimation can be reduced to 0.7° compared to 1.7° by a direct estimation method. PCA suffers from noisy heatmaps for images of bad quality degrading the overall performance of this approach.