Cite details and abstract
@INPROCEEDINGS{Cox94,
AUTHOR = {G. S. Cox and G. de Jager},
BOOKTITLE = {Medical Imaging},
MONTH = feb,
ORGANIZATION = {{SPIE}},
PAGES = {188-99},
TITLE = {Automatic Registration of Temporal Image Pairs for Digital Subtraction Angiography},
VOLUME = {2167},
YEAR = {1994},
ABSTRACT = {
Temporal Digital Subtraction Angiography (DSA) is used to visualize blood vessels in x-ray images. A DSA image pair consists of the mask image, which is a digitized x-ray taken before a contrast medium is injected into the bloodstream, and the live image, which is taken once the contrast medium has traversed the circulatory system and reached the blood vessels of interest. The mask image is then subtracted from the live image and ideally only the contrast enhanced blood vessels should remain. DSA has two main limitations. Firstly, gross patient motion and physiological events occur in the time that elapses between x-rays. Secondly, there are local and global differences in the mean gray-level at corresponding points in the live and mask images, excluding the variations introduced by the contrast media. To solve the motion problem, we take the approach of matching regions around control points in the live image in a search area around the approximately corresponding points in the mask image. In this way a motion vector field that describes the spatial offset to the best match position in the mask image (with sub-pixel accuracy) is constructed. The problem of mean gray-level disparity between the live and mask images is to a large extent overcome by the use of a match measure that is invariant to overall additive gray-level differences. Incorrect mismatches caused by the contrast media are avoided by using multiple sub-templates in the matching process. The sub-template method also allows the estimation of mean gray-level disparity between the mask and live images. The smoothed motion vector field and mean gray-level disparity estimates are used to perform an improved subtraction of the mask from the live image with a reduction in the artifacts that are a result of normal subtraction. Efficient best match search techniques are used to reduce the computational cost of the algorithm, at the expense of some difference image quality. Results are provided for simulated and actual DSA image pairs.
}
}