Open AccessThe successive projection algorithm as an initialization method for brain tumor segmentation using non-negative matrix factorization
Author(s) -
Nicolas Sauwen,
Marjan Acou,
H. N. Bharath,
Diana M. Sima,
Jelle Veraart,
Frederik Maes,
Uwe Himmelreich,
Eric Achten,
Sabine Van Huffel
Publication year - 2017
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0180268
Subject(s) - initialization , non negative matrix factorization , endmember , computer science , segmentation , matrix decomposition , pattern recognition (psychology) , algorithm , artificial intelligence , projection (relational algebra) , subspace topology , image (mathematics) , eigenvalues and eigenvectors , physics , quantum mechanics , programming language
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.