Cross modality label fusion in multi-atlas segmentation
Keyvan Kasiri, Paul Fieguth, David A. Clausi · 2014
Multi-atlas label fusion is a widely used approach in medical image analysis that has improved the accuracy of segmentation. Majority voting, as the most common combination strategy, weighs each candidate in the atlas database equally. More sophisticated methods rely on the intensity similarity of each atlas to the target volume. However, these methods cannot handle those cases in which the atlases and the target image are in different modalities. A new method for label fusion is proposed, based on a structural similarity measure, relying on the structural relationships of features extracted from an undecimated wavelet transform instead of explicit image intensities. The new label fusion method has been tested on simulated and real MR images; segmentation results are promising, and open the door to a wider range of multi-modal approaches.