Title
A new method for 3D thinning of hybrid shaped porous media using artificial intelligence. Application to trabecular bone.
Abstract
Curve and surface thinning are widely-used skeletonization techniques for modeling objects in three dimensions. In the case of disordered porous media analysis, however, neither is really efficient since the internal geometry of the object is usually composed of both rod and plate shapes. This paper presents an alternative to compute a hybrid shape-dependent skeleton and its application to porous media. The resulting skeleton combines 2D surfaces and 1D curves to represent respectively the plate-shaped and rod-shaped parts of the object. For this purpose, a new technique based on neural networks is proposed: cascade combinations of complex wavelet transform (CWT) and complex-valued artificial neural network (CVANN). The ability of the skeleton to characterize hybrid shaped porous media is demonstrated on a trabecular bone sample. Results show that the proposed method achieves high accuracy rates about 99.78%-99.97%. Especially, CWT (2nd level)-CVANN structure converges to optimum results as high accuracy rate-minimum time consumption.
Year
DOI
Venue
2012
10.1007/s10916-010-9495-y
J. Medical Systems
Keywords
Field
DocType
neural network,artificial intelligence,complex wavelet transform.complex-valued artificial neural network.skeletonization.3d thinning,cvann structure converges,disordered porous media analysis,hybrid shape-dependant skeleton,hybrid shaped porous media,complex-valued artificial neural network,skeleton combine,high accuracy rate,porous media,new method,trabecular bone
Porosity,Thinning,Skeletonization,Cascade,Artificial intelligence,Complex wavelet transform,Artificial neural network,Porous medium,Medicine,Wavelet
Journal
Volume
Issue
ISSN
36
2
0148-5598
Citations 
PageRank 
References 
0
0.34
26
Authors
6
Name
Order
Citations
PageRank
Rachid Jennane110816.89
Gabriel Aufort2152.41
Claude Laurent Benhamou3374.73
Murat Ceylan4808.37
Yüksel Ozbay531217.03
Osman Nuri Ucan682.30