A Multitarget Interested Region Extraction Method for Wrist X-Ray Images Based on Optimized AlexNet and Two-Class Combined Model

Abstract

Bone age assessment based on X-ray Images can accurately determine the actual bone age of adolescents. Accurate extraction of the key regions of interest (ROIs) in X-ray images is required to accurately assess bone age. However, existing ROI extraction methods can only extract a few targets and have poor extraction accuracy. Thus, the strict demands for imaging in the medical field via these methods are difficult. In this article, we propose a multitarget interested region extraction method for wrist X-ray images based on optimized AlexNet and two-class combined model, named OATC, which can simultaneously extract multiple ROIs with high accuracy. Specifically, the square-wave scanning algorithm was implemented to obtain the bounding box size of each bone ROI according to the shape information of the wrist. Then, the optimized AlexNet was used to obtain the key point coordinates of each bone ROI. Bone ROIs could be extracted by combining key point coordinates with the bone bounding box size. Finally, the two-classification model was combined to improve the accuracy of ROI extraction. Experiments conducted on our wrist X-ray image dataset showed that OTAC has a fast convergence speed and small deviation. The average accuracy of extracting 14 ROIs reached 95.57%, which are 7.76% and 4.68% higher than that of VGG16 and AlexNet, respectively.