by Torsten Rohlfing, Robert Br, Olf Menzel, Calvin R. Maurer
http://www.stanford.edu/~rohlfing/publications/./2004-rohlfing-nimg-atlas_selection-preprint.pdf
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Abstract:
This paper evaluates strategies for atlas selection in atlas-based segmentation of three-dimensional biomedical images. Segmentation by intensity-based non-rigid registration to atlas images is applied to confocal microscopy images acquired from the brains of 20 bees. This paper evaluates and compares four different approaches for atlas image selection: registration to an individual atlas image (IND), registration to an average shape atlas image (AVG), registration to the most similar image from a database of individual atlas images (SIM), and registration to all images from a database of individual atlas images with subsequent multi-classifier decision fusion (MUL). The MUL strategy is a novel application of multi-classifier techniques, which are common in pattern recognition, to atlas-based segmentation. For each atlas selection strategy, the segmentation performance of the algorithm was quantified by means of the similarity index between the automatic segmentation result and a manually generated gold standard. The best segmentation accuracy was achieved using the MUL paradigm, which resulted in a mean similarity index value between manual and automatic segmentation
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