Unsupervised Learning of an Atlas from Unlabeled Point-Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Group-Wise Point-Set Registration Using a Novel CDF-Based Havrda-Charvát Divergence
International Journal of Computer Vision
Label fusion using performance estimation with iterative label selection
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Capturing anatomical shape variability using b-spline registration
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Construction and validation of mean shape atlas templates for atlas-based brain image segmentation
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
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We describe a method to generate an average atlas from segmented 3-D images of a population of subjects. Using repeated application of an intensity-based non-rigid registration algorithm based on third-order 3-D B-splines, a sequence of average label images is created. Averaging of the non-numerical label data employs a generalization of the mode of sets of corresponding voxels, parameterized by a threshold value specifying the required level of classificationconfidence. The number of voxels that cannot be assigneda unique average value provides a criterion for the convergenceof the iteration. For improved accuracy, efficiency, and robustnessof the non-rigid registration, deformations computed during one iteration are propagated to the next iteration as initial transformation estimates. The usefulness of our method is demonstrated by applying it to generate an average atlas from segmented 3-D confocal microscopy images of 20 bee brains. We validate that the deformations found by our algorithm are meaningful by deforming the original gray-level images according to the transformations computed for the label fields.