Segmenting the femoral head and acetabulum in the hip joint automatically using a multi-step scheme

We describe a multi step approach for automatic segmen tation of the feinoral head and the acetabulum in the hip joint from three dimensional (3D) CT images. Our segmentation method consists of the fol lowing steps: 1) construction of the valley emphasized image by subtract ing valleys from the original images; 2) initial segmentation of the bone regions by using conventional techniques including the initial threshold and binary morphological operations from the valley emphasized image; 3) further segmentation of the bone regions by using the iterative adaptive classification with the initial segmentation result; 4) detection of the rough bone boundaries based on the segmented bone regions; 5) 3D reconstruc tion of the bone surface using the rough bone boundaries obtained in step 4) by a network of triangles; 6) correction of all vertices of the 3D bone sur face based on the normal direction of vertices; 7) adjustment of the bone surface based on the corrected vertices. We evaluated our approach on 35 CT patient data sets. Our experimental results show that our segmentation algorithm is more accurate and robust against noise than other conventional approaches for automatic segmentation of the femoral head and the acetab ulum. Average root mean square (RMS) distance from manual reference segmentations created by experienced users was approximately 0.68mm (in plane resolution of the CT data).