Knowledge-based method for fully automatic contour detection in radiographs

Identification of anatomical structure boundaries in radiographs is a necessary step for detecting abnormalities. The aim of this study is to develop a knowledge-based approach to automatically segment the interested structure boundaries in X-ray images. Our method contains four main steps. First, the original gray-level radiograph is segmented into a binary image. Second, the region of interest (ROI) is detected by matching the features extracted from the binary image with a pre-defined anatomical model, and the location of ROI will serve as the landmark for the following search. Third, an anatomical model will be hierarchically applied to the original image. Correlation values and anatomical constraints will be applied to choose the closer match edge candidates. According to the shape of the global model, the best match edge candidates will be selected and connected to generate the boundaries of the interested structure. Finally, active contour model is used to refine the boundaries. The results show the effectiveness and the efficiency of this proposed method.