The successive projection algorithm as an initialization method for brain tumor segmentation using non-negative matrix factorization

Non-negative matrix factorization (NMF) has become a widely used tool for additive parts-based analysis in a wide range of applications. As NMF is a non-convex problem, the quality of the solution will depend on the initialization of the factor matrices. In this study, the successive projection algo...

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Bibliographic Details
Published in:PloS one Vol. 12; no. 8; p. e0180268
Main Authors: Sauwen, Nicolas, Acou, Marjan, Bharath, Halandur N., Sima, Diana M., Veraart, Jelle, Maes, Frederik, Himmelreich, Uwe, Achten, Eric, Van Huffel, Sabine
Format: Journal Article
Language:English
Published: United States Public Library of Science 28.08.2017
Public Library of Science (PLoS)
Subjects:
Algorithms
Brain
Brain - diagnostic imaging
Brain cancer
Brain Neoplasms - diagnostic imaging
Brain tumors
Clustering
Computer and Information Sciences
Data analysis
Decomposition
Dynamical systems
Electrical engineering
Factorization
Humans
Image processing
Image Processing, Computer-Assisted - methods
Image segmentation
Magnetic resonance
Magnetic Resonance Imaging
Medical imaging
Medicine and Health Sciences
Neuroimaging
Neurosciences
Pattern Recognition, Automated - methods
Physical Sciences
Reproducibility
Research and Analysis Methods
Segmentation
Signal processing
Belgium
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary:Non-negative matrix factorization (NMF) has become a widely used tool for additive parts-based analysis in a wide range of applications. As NMF is a non-convex problem, the quality of the solution will depend on the initialization of the factor matrices. In this study, the successive projection algorithm (SPA) is proposed as an initialization method for NMF. SPA builds on convex geometry and allocates endmembers based on successive orthogonal subspace projections of the input data. SPA is a fast and reproducible method, and it aligns well with the assumptions made in near-separable NMF analyses. SPA was applied to multi-parametric magnetic resonance imaging (MRI) datasets for brain tumor segmentation using different NMF algorithms. Comparison with common initialization methods shows that SPA achieves similar segmentation quality and it is competitive in terms of convergence rate. Whereas SPA was previously applied as a direct endmember extraction tool, we have shown improved segmentation results when using SPA as an initialization method, as it allows further enhancement of the sources during the NMF iterative procedure.
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Competing Interests: We have the following interests: D.M. Sima is employed by Icometrix, a Belgian company that develops software tools for monitoring Multiple Sclerosis using MRI biomarkers. There are no patents, products in development or marketed products to declare. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0180268