Stitching of volumes obtained from three dimensional (3D) ultrasound (US) scanners improves visualization of anatomy
in many clinical applications. Fast but accurate volume registration remains the key challenge in this area.We propose a
volume stitching method based on efficient registration of 3D US volumes obtained from a tracked US probe. Since the
volumes, after adjusting for probe motion, are coarsely registered, we obtain salient correspondence points in the central
slices of these volumes. This is done by first removing artifacts in the US slices using intensity invariant local phase
image processing and then applying the Harris Corner detection algorithm. Fast sub-volume registration on a small
neighborhood around the points then gives fast, accurate 3D registration parameters. The method has been tested on 3D
US scans of phantom and real human radius and pelvis bones and a phantom human fetus. The method has also been
compared to volumetric registration, as well as feature based registration using 3D-SIFT. Quantitative results show
average post-registration error of 0.33mm which is comparable to volumetric registration accuracy (0.31mm) and much
better than 3D-SIFT based registration which failed to register the volumes. The proposed method was also much faster
than volumetric registration (~4.5 seconds versus 83 seconds).
Registration of two dimensional to three dimensional orthopaedic medical image data has important applications
particularly in the area of image guided surgery and sports medicine. Fluoroscopy to computer tomography (CT)
registration is an important case, wherein digitally reconstructed radiographs derived from the CT data are registered to
the fluoroscopy data. Traditional registration metrics such as intensity-based mutual information (MI) typically work
well but often suffer from gross misregistration errors when the image to be registered contains a partial view of the
anatomy visible in the target image. Phase-based MI provides a robust alternative similarity measure which, in addition
to possessing the general robustness and noise immunity that MI provides, also employs local phase information in the
registration process which makes it less susceptible to the aforementioned errors. In this paper, we propose using the
complex wavelet transform for computing image phase information and incorporating that into a phase-based MI
measure for image registration. Tests on a CT volume and 6 fluoroscopy images of the knee are presented. The femur
and the tibia in the CT volume were individually registered to the fluoroscopy images using intensity-based MI,
gradient-based MI and phase-based MI. Errors in the coordinates of fiducials present in the bone structures were used to
assess the accuracy of the different registration schemes. Quantitative results demonstrate that the performance of
intensity-based MI was the worst. Gradient-based MI performed slightly better, while phase-based MI results were the
best consistently producing the lowest errors.
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