Segmentation of the hip joint in CT volumes using adaptive thresholding classification and normal direction correction

Segmentation of the pelvis and proximal femur in computed tomography (CT) volumes is a prerequisite of patient specific planning and simulation for hip surgery. Existing methods do not perform well due to bone disease and technical limitations of CT imaging. In this paper, an accurate framework for segmenting bone in the hip joint is presented. Our approach begins with valley-emphasized image construction using morphological operations so that valleys stand out in high relief, and then, an initial segmentation with optimal threshold is performed to divide the dataset into bone and non-bone regions. Subsequently, bone regions are reclassified based on 3D iterative adaptive thresholding with consideration of the partial volume effect and the spatial information. Finally, we refine the rough bone boundaries based on the normal direction of vertices of the 3D bone surface. Our segmentation approach is automatic and robust. Its performance is evaluated on 35 datasets consisting of 70 hip joints with a status ranging from healthy to severe osteoarthritis and the results have proved to be very successful.