  We propose a framework for extracting the bone surface from B-mode images
employing the eigenspace minimum variance (ESMV) beamformer and a ridge
detection method. We show that an ESMV beamformer with a rank-1 signal subspace
can preserve the bone anatomy and enhance the edges, despite an image which is
less visually appealing due to some speckle pattern distortion. The beamformed
images are post-processed using the phase symmetry (PS) technique. We validate
this framework by registering the ultrasound images of a vertebra (in a water
bath) against the corresponding Computed Tomography (CT) dataset. The results
show a bone localization error in the same order of magnitude as the standard
delay-and-sum (DAS) technique, but with approximately 20% smaller standard
deviation (STD) of the image intensity distribution around the bone surface.
This indicates a sharper bone surface detection. Further, the noise level
inside the bone shadow is reduced by 60%. In in-vivo experiments, this
framework is used for imaging the spinal anatomy. We show that PS images
obtained from this beamformer setup have sharper bone boundaries in comparison
with the standard DAS ones, and they are reasonably well separated from the
surrounding soft tissue.
