A three-dimensional normal distribution derived from the cluster is built and regarded as the parametric model for diagnosis of hip dysplasia. Samples, each of which consists of these three angle values, are used for clustering according to their densities in a descending order. Angles including CE, sharp, and Tonnis angle which are commonly measured in clinical diagnosis, are automatically obtained. Instead, a data-driven diagnostic model for hip dysplasia is presented. Traditional knowledge-driven diagnostic criteria is abandoned. Feature points are extracted according to marked contours. Considering the complexity of medical imaging, the contour of acetabulum, femoral head, and the upper side of thigh-bone are manually marked. Results: A semi-automatic method for diagnosis of hip dysplasia is proposed. There has been no method or tool for automatic diagnosis of hip dysplasia till now. Early detection of hip dysplasia on X-ray films may probably conduce to early treatment of patients, which can help to cure patients or relieve their pain as much as possible. Ltd., Harbin, Chinaīackground: Diagnosis of hip joint plays an important role in early screening of hip diseases such as coxarthritis, heterotopic ossification, osteonecrosis of the femoral head, etc. 2Department of Radiology, Affiliated Zhongshan Hosptial of Dalian University, Dalian, China.1Department of Computer Science and Technology, College of Information and Computer Engineering, Northeast Forestry University, Harbin, China.Guangyao Yang 1 † Yaoxian Jiang 2 † Tong Liu 1 Xudong Zhao 1 * Xiaodan Chang 2 * Zhaowen Qiu 1,3 *
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