Display options
Cite
Shiga M, Tatsumi K, Muto S, et al. Sparse modeling of EELS and EDX spectral imaging data by nonnegative matrix factorization. Ultramicroscopy. 2016;170:43-59doi: 10.1016/j.ultramic.2016.08.006.
Shiga, M., Tatsumi, K., Muto, S., Tsuda, K., Yamamoto, Y., Mori, T., & Tanji, T. (2016). Sparse modeling of EELS and EDX spectral imaging data by nonnegative matrix factorization. Ultramicroscopy, 17043-59. https://doi.org/10.1016/j.ultramic.2016.08.006
Shiga, Motoki, et al. "Sparse modeling of EELS and EDX spectral imaging data by nonnegative matrix factorization." Ultramicroscopy vol. 170 (2016): 43-59. doi: https://doi.org/10.1016/j.ultramic.2016.08.006
Shiga M, Tatsumi K, Muto S, Tsuda K, Yamamoto Y, Mori T, Tanji T. Sparse modeling of EELS and EDX spectral imaging data by nonnegative matrix factorization. Ultramicroscopy. 2016 Nov;170:43-59. doi: 10.1016/j.ultramic.2016.08.006. Epub 2016 Aug 06. PMID: 27529804.
Copy Download .nbib Format: NLM
Share it on

Ultramicroscopy. 2016 Nov;170:43-59. doi: 10.1016/j.ultramic.2016.08.006. Epub 2016 Aug 06.

Sparse modeling of EELS and EDX spectral imaging data by nonnegative matrix factorization.

Ultramicroscopy

Motoki Shiga, Kazuyoshi Tatsumi, Shunsuke Muto, Koji Tsuda, Yuta Yamamoto, Toshiyuki Mori, Takayoshi Tanji

Affiliations

  1. Department of Electrical, Electronic and Computer Engineering, Gifu University, 1-1, Yanagido, Gifu 501-1193, Japan. Electronic address: [email protected].
  2. Advanced Measurement Technology Center, Institute of Materials and Systems for Sustainability, Nagoya University, Chikusa-ku, Nagoya 464-8603, Japan.
  3. Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa 277-8561, Japan; Center for Materials Research by Information Integration, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba 305-0047, Japan; Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology, 2-4-7 Aomi Koto-ku, Tokyo 135-0064, Japan.
  4. High-Voltage Electron Microscope Laboratory, Institute of Materials and Systems for Sustainability, Nagoya University, Chikusa-ku, Nagoya 464-8603, Japan.
  5. Environment and Energy Materials Division, National Institute for Materials Science, 1-1 Namiki, Tsukuba 305-0044, Japan.
  6. Division of Materials Research, Institute of Materials and Systems for Sustainability, Nagoya University, Chikusa-ku, Nagoya 464-8603, Japan.

PMID: 27529804 DOI: 10.1016/j.ultramic.2016.08.006

Abstract

Advances in scanning transmission electron microscopy (STEM) techniques have enabled us to automatically obtain electron energy-loss (EELS)/energy-dispersive X-ray (EDX) spectral datasets from a specified region of interest (ROI) at an arbitrary step width, called spectral imaging (SI). Instead of manually identifying the potential constituent chemical components from the ROI and determining the chemical state of each spectral component from the SI data stored in a huge three-dimensional matrix, it is more effective and efficient to use a statistical approach for the automatic resolution and extraction of the underlying chemical components. Among many different statistical approaches, we adopt a non-negative matrix factorization (NMF) technique, mainly because of the natural assumption of non-negative values in the spectra and cardinalities of chemical components, which are always positive in actual data. This paper proposes a new NMF model with two penalty terms: (i) an automatic relevance determination (ARD) prior, which optimizes the number of components, and (ii) a soft orthogonal constraint, which clearly resolves each spectrum component. For the factorization, we further propose a fast optimization algorithm based on hierarchical alternating least-squares. Numerical experiments using both phantom and real STEM-EDX/EELS SI datasets demonstrate that the ARD prior successfully identifies the correct number of physically meaningful components. The soft orthogonal constraint is also shown to be effective, particularly for STEM-EELS SI data, where neither the spatial nor spectral entries in the matrices are sparse.

Copyright © 2016 Elsevier B.V. All rights reserved.

Keywords: Automatic relevance determination; Nonnegative matrix factorization; Sparse modeling; Spatial orthogonality; Spectral imaging

Publication Types