MRI-ICIAR-2013.pdf (1.34 MB)
Automatic segmentation of adipose tissue from thigh magnetic resonance images
journal contribution
posted on 2016-02-08, 16:33 authored by Senthil Purushwalkam, Baihua LiBaihua Li, Qinggang MengQinggang Meng, Jamie S. McPheeAutomatic segmentation of adipose tissue in thigh magnetic resonance imaging (MRI) scans is challenging and rarely reported in the literature. To address this problem, we propose a fully automated unsupervised segmentation method involving the use of spatial intensity constraints to guide the segmentation process. The novelty of this method lies in two aspects: firstly, an adaptive distance classifier, incorporating intra-slice spatial continuity, is used for robust region growing and segmentation estimation; secondly, polynomial based intensity inhomogeneity maps are generated to model inter- and intra-slice intensity variation of each pixel class and thus refine the initial classification. Our experimental results have demonstrated the effectiveness of imposing 3D intensity constraints to successfully classify the adipose tissue from muscles in the presence of image noise and considerable amounts of non-uniform MRI intensity. © 2013 Springer-Verlag.
History
School
- Science
Department
- Computer Science
Published in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Volume
7950 LNCSPages
451 - 458Citation
PURUSHWALKAM, S. ... et al, 2013. Automatic segmentation of adipose tissue from thigh magnetic resonance images. IN: Kamel, M. and Campilho, A. (eds). Image Analysis and Recognition: 10th International Conference, ICIAR 2013, Póvoa do Varzim, Portugal, June 26-28, 2013, Proceedings. Lecture Notes in Computer Science, 7950, pp.451-458Version
- AM (Accepted Manuscript)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Publication date
2013Notes
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-39094-4_51ISBN
9783642390937;9783642390944ISSN
0302-9743eISSN
1611-3349Publisher version
Book series
Lecture Notes in Computer Science;7950Language
- en