Paper Info
Title
Extended three-dimensional rotation invariant local binary patterns.
Abstract
This paper presents a new set of three-dimensional rotation invariant texture descriptors based on the well-known local binary patterns (LBPs). In the approach proposed here, we extend an existing three-dimensional LBP based on the region growing algorithm using existing features developed exquisitely for two-dimensional LBPs (pixel intensities and differences). We have conducted experiments on a synthetic dataset of three-dimensional randomly rotated texture images in order to evaluate the discriminatory power and the rotation invariant properties of our descriptors as well as those of other two-dimensional and three-dimensional texture descriptors. Our results demonstrate the effectiveness of the extended LBPs and improvements against other state-of-the-art hand-crafted three-dimensional texture descriptors on this dataset. Furthermore, we prove that the extended LBPs can be used in medical datasets to discriminate between MR images of oxygenated and non-oxygenated brain tissues of newborn babies. Display Omitted A new set of three-dimensional fully rotation invariant LBP descriptors is proposed.Proven utility of the third dimension in local binary patternsImprovements against other state-of-the-art 3D texture descriptorsApplication to a clinical dataset of susceptibility-weighted MR brain images
Year
DOI
Venue
2017
10.1016/j.imavis.2017.03.004
Image Vision Comput.
Keywords
Field
DocType
Local binary patterns (LBPs),Three-dimensions,Rotation invariance,Texture classification
Computer vision,Pattern recognition,Region growing algorithm,Local binary patterns,Artificial intelligence,Pixel,Invariant (mathematics),Mathematics,Binary number
Journal
Volume
Issue
ISSN
62
C
0262-8856
Citations 
PageRank 
References 
2
0.36
19
Authors
4
Name
Order
Citations
PageRank
Leonardo Citraro120.36
Sasan Mahmoodi29417.37
Angela Darekar321.03
Brigitte Vollmer420.36