NDLI: Using local 3D structure for segmentation of bone from computer tomography images

Content Provider	IEEE Xplore Digital Library
Author	Westin, C.-F. Bhalerao, A. Knutsson, H. Kikinis, R.
Copyright Year	1997
Description	Author affiliation: Lab. of Surg. Planning, Brigham & Women's Hospital, Boston, MA, USA (Westin, C.-F.)
Abstract	In this paper we focus on using local 3D structure for segmentation. A tensor descriptor is estimated for each neighbourhood, i.e. for each voxel in the data set. The tensors are created from a combination of the outputs form a set of 3D quadrature filters. The shape of the tensors describe locally the structure of the neighbourhood in terms of how much it is like a plane, a line, and a sphere. We apply this to segmentation of bone from Computer Tomography data (CT). Traditional methods are based purely on gray-level value discrimination and have difficulties in recovering thin bone structures due to so called partial voluming, a problem which is present in all such sampled data. We illuminate the partial voluming problem by showing that thresholding creates complicated artifacts even if the signal is densely enough sampled and can be perfectly reconstructed. The unwanted effects of thresholding can be reduced by a change of the signal basis. We show that by using additional local structure information can significantly reduce the degree of sampling artifacts. Evaluation of the method on a clinical case is presented, the segmentation of a human skull from a CT volume. The method shows that many of the thin bone structures which disappear in a pure thresholding can be recovered.
Starting Page	794
Ending Page	800
File Size	1100557
Page Count	7
File Format	PDF
ISBN	0818678224
ISSN	10636919
DOI	10.1109/CVPR.1997.609418
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-06-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Bones Tensile stress Computed tomography Image reconstruction Surgery Signal sampling Laboratories Shape Image sampling
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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