Proceedings Article | 12 May 2004
Proc. SPIE. 5370, Medical Imaging 2004: Image Processing
KEYWORDS: Detection and tracking algorithms, Sensors, Digital filtering, X-rays, Fluoroscopy, X-ray imaging, Motion models, Nonlinear filtering, X-ray detectors, Motion estimation
One advantage of flat-panel X-ray detectors is the immediate availability
of the acquired images for display. Current limitations in large-area
active-matrix manufacturing technology, however, require that the images
read out from such detectors be processed to correct for inactive pixels.
In static radiographs, these defects can only be interpolated by spatial
filtering. Moving X-ray image modalities, such as fluoroscopy or cine-angiography,
permit to use temporal information as well. This paper describes interframe
defect interpolation algorithms based on motion compensation and filtering.
Assuming the locations of the defects to be known, we fill in the defective
areas from past frames, where the missing information was visible due to motion.
The motion estimator is based on regularized block matching, with speedup obtained
by successive elimination and related measures. To avoid the motion
estimator locking on to static defects, these are cut out of each block
during matching. Once motion is estimated, three methods are available for
defect interpolation: direct filling-in by the motion-compensated predecessor,
filling-in by a 3D-multilevel median filtered value, and spatiotemporal
mean filtering. Results are shown for noisy fluoroscopy sequences acquired in
clinical routine with varying amounts of motion and simulated defects up to
six lines wide. They show that the 3D-multilevel median filter appears as the
method of choice since it causes the least blur of the interpolated data, is robust
with respect to motion estimation errors and works even in non-moving
areas.
