Registration of multimodal volume head images via attached markers

We investigate the accuracy of registering arbitrarily oriented, multimodal, volume images of the human head, both to other images and to physical space, by aligning a configuration of three or more fiducial points that are the centers of attached markers. To compute the centers we use an extension of an adaptive thresholding algorithm due to Kittler. Because the markers are indistinguishable it is necessary to establish their correspondence between images. We have evaluated geometric matching algorithms for this purpose. The inherent errors in fiducial localization arising with digital images limits the accuracy with which anatomical targets can be registered. To accommodate this error we apply a least-squares registration algorithm to the fiducials. To evaluate the resulting target registration accuracy we have conducted experiments on images of internally implanted markers in a cadaver and images of externally attached markers in volunteers. We have also produced computer simulations of volume images of a hemispherical model of the head, randomly picking corresponding fiducial points and targets in the images, introducing uniformly distributed error into the fiducial locations, registering the images, and measuring target registration accuracy at the 95% confidence level. Our results indicate that submillimetric accuracy is feasible for high resolution images with four markers.