Initializing Nonnegative Matrix Factorization using the Successive Projection Algorithm for multi-parametric medical image segmentation
نویسندگان
چکیده
As nonnegative matrix factorization (NMF) represents a nonconvex problem, the quality of its solution will depend on the initialization of the factor matrices. This study proposes the Successive Projection Algorithm (SPA) as a feasible NMF initialization method. SPA is applied to a multi-parametric MRI dataset for automated NMF brain tumor segmentation. SPA provides fast and reproducible estimates of the tissue sources, and segmentation quality is found to be similar compared to repetitive random initialization.
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