Localized Priors for the Precise Segmentation of Individual Vertebras from CT Volume Data

Authors:
Hong Shen;Andrew Litvin;Christopher Alvino
Affiliations:
Siemens Corporate Research, Inc., , Princeton, NJ 08540;Analogic Corporation, , Peabody, MA 01960;Siemens Corporate Research, Inc., , Princeton, NJ 08540
Venue:
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Year:
2008

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Abstract

We present algorithms for the automatic and precise segmentation of individual vertebras in CT Volume data. When a local surface evolution method such as the level set is applied to such a complex structure, global shape priors will not be sufficient to avoid the leakage and local minima problems, particularly if precise object boundary is desired. We propose a prior knowledge base that contains localized priors--a group of high-level features whose detection will augment the surface model and be the key to success. Base on this a set of context blockers are applied to prevent the leakages. Carefully designed initial surface when registered with the data helps avoid the local minimum problem. The results of segmentation well approximate the human delineated object boundaries. We also present the validation result of the segmentation of 150 vertebras.