Automatic segmentation technique for acetabulum and femoral head in CT images

• Classify the hip joints into four groups based on anatomical and imaging criteria.
• Divide the image set into bone and non-bone classes for the initial segmentation.
• Present a Bayes decision rule for segmenting the femoral head and acetabulum.
• Refine the bone boundaries based on normal direction of vertices of bone surface.
• Evaluate our method based on clinical application criteria (good, moderate and poor).

Segmentation of the femoral head and proximal acetabulum from three dimensional (3D) CT data is essential for patient specific planning and simulation of hip surgery whereas it still remains challenging due to deformed shapes and extremely narrow inter-bone regions. In this paper, we present an accurate, automatic and fast approach for simultaneous segmentation of the femoral head and proximal acetabulum in the hip joint from 3D CT data. First valley-emphasized image is constructed from original images so that valleys stand out in high relief and initial thresholding segmentation is performed to divide the image set into bone (femoral head and acetabulum) and non-bone classes. It is employed as an initial boundary of the femoral head and acetabulum for further processing in the segmentation procedures. In the subsequent iterative process, the bone regions are further segmented with consideration of the narrow joint space, the neighborhood information and the partial volume effect. Finally, the segmented bone boundaries are corrected based on the normal direction of vertices of the 3D bone surface. Evaluation of the method is performed on the 110 hips including pathologies. Experimental results indicate that our method rapidly leads to very accurate segmentations of the femoral head and acetabulum in the hip joint and can be applied as a tool in the clinical practice.