Paper Info
Title
A New Level-Set-Based Protocol for Accurate Bone Segmentation From CT Imaging.
Abstract
A new medical image segmentation pipeline for accurate bone segmentation from computed tomography (CT) imaging is proposed in this paper. It is a two-step methodology, with a pre-segmentation step and a segmentation refinement step, as follows. First, the user performs a rough segmenting of the desired region of interest. Second, a fully automatic refinement step is applied to the pre-segmented data. The automatic segmentation refinement is composed of several sub-steps, namely, image deconvolution, image cropping, and interpolation. The user-defined pre-segmentation is then refined over the deconvolved, cropped, and up-sampled version of the image. The performance of the proposed algorithm is exemplified with the segmentation of CT images of a composite femur bone, reconstructed with different reconstruction protocols. Segmentation outcomes are validated against a gold standard model, obtained using the coordinate measuring machine Nikon Metris LK V20 with a digital line scanner LC60-D and a resolution of 28 pun. High sub-pixel accuracy models are obtained for all tested data sets, with a maximum average deviation of 0.178 mm from the gold standard. The algorithm is able to produce high quality segmentation of the composite femur regardless of the surface meshing strategy used.
Year
DOI
Venue
2015
10.1109/ACCESS.2015.2484259
IEEE ACCESS
Keywords
Field
DocType
Biomedical image processing,deconvolution,image segmentation,level set,spatial resolution
Computer vision,Scale-space segmentation,Computer science,Segmentation,Image texture,Binary image,Segmentation-based object categorization,Image processing,Image segmentation,Artificial intelligence,Image resolution
Journal
Volume
ISSN
Citations 
3
2169-3536
4
PageRank 
References 
Authors
0.42
11
2
Name
Order
Citations
PageRank
Manuel Pinheiro140.42
J. L. Alves271.45