Prior knowledge driven multiscale segmentation of brain MRI

Authors:
Ayelet Akselrod-Ballin;Meirav Galun;John Moshe Gomori;Achi Brandt;Ronen Basri
Affiliations:
Dept. of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot;Dept. of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot;Dept. of Radiology, Hadassah University Hospital, Jerusalem, Israel;Dept. of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot;Dept. of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot
Venue:
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Year:
2007

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Abstract

We present a novel automatic multiscale algorithm applied to segmentation of anatomical structures in brain MRI. The algorithm which is derived from algebraic multigrid, uses a graph representation of the image and performs a coarsening process that produces a full hierarchy of segments. Our main contribution is the incorporation of prior knowledge information into the multiscale framework through a Bayesian formulation. The probabilistic information is based on an atlas prior and on a likelihood function estimated from a manually labeled training set. The significance of our new approach is that the constructed pyramid, reflects the prior knowledge formulated. This leads to an accurate and efficient methodology for detection of various anatomical structures simultaneously. Quantitative validation results on gold standard MRI show the benefit of our approach.