Automatic Insall-Salvati ratio measurement on lateral knee x-ray images using model-guided landmark localization

作者：Chen Hsin Chen; Lin Chii Jeng; Wu Chia Hsing; Wang Chien Kuo; Sun Yung Nien^*

来源：Physics in Medicine and Biology, 2010, 55(22): 6785-6800.

DOI：10.1088/0031-9155/55/22/012

The Insall-Salvati ratio (ISR) is important for detecting two common clinical signs of knee disease: patella alta and patella baja. Furthermore, large inter-operator differences in ISR measurement make an objective measurement system necessary for better clinical evaluation. In this paper, we define three specific bony landmarks for determining the ISR and then propose an x-ray image analysis system to localize these landmarks and measure the ISR. Due to inherent artifacts in x-ray images, such as unevenly distributed intensities, which make landmark localization difficult, we hence propose a registration-assisted active-shape model (RAASM) to localize these landmarks. We first construct a statistical model from a set of training images based on x-ray image intensity and patella shape. Since a knee x-ray image contains specific anatomical structures, we then design an algorithm, based on edge tracing, for patella feature extraction in order to automatically align the model to the patella image. We can estimate the landmark locations as well as the ISR after registration-assisted model fitting. Our proposed method successfully overcomes drawbacks caused by x-ray image artifacts. Experimental results show great agreement between the ISRs measured by the proposed method and by orthopedic clinicians.

• 出版日期2010-11-21