11 Electron Tomography
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36. L. Yedra et al., EELS tomography in multiferroic nanocomposites: from spectrum images to
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41. W. Van den Broek et al., Correction of non-linear thickness effects in HAADF STEM electron
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42. N. Dobigeon, S. Moussaoui, M. Coulon, J.-Y. Tourneret, A.O. Hero, Joint bayesian end member
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43. N. Dobigeon, N. Brun, Ultramicroscopy spectral mixture analysis of EELS spectrum-images.
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44. J. Nascimento, J. Bioucas-Dias, Does independent component analysis play a role in unmixing
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45. P. Torruella et al., Clustering analysis strategies for electron energy loss spectroscopy (EELS).
Ultramicroscopy 185, 42–48 (2018)
46. L.A. Giannuzzi, F.A. Stevie, A review of focused ion beam milling techniques for TEM specimen preparation 10_1016-S0968-4328(99), 00005–0 Micron ScienceDirect_com. Micron 30,
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49. J. Sander, M. Ester, H.P.P. Kriegel, X. Xu, Density-based clustering in spatial databases: the
algorithm GDBSCAN and its applications. Data Min. Knowl. Discovery 194, 169–194 (1998)
50. I. Davidson, S.S. Ravi, Agglomerative hierarchical clustering with constraints: theory and
empirical resutls, in 9th European Conference on Principles of Data Mining and Knowledge
Discovery Databases, PKDD 2005, pp. 59–70 (2005)
51. L. Yedra et al., Oxide wizard: an EELS application to characterize the white lines of transition
metal edges. Microsc. Microanal. 20, 698–705 (2014)
52. T. Printemps et al., Self-adapting denoising, alignment and reconstruction in electron
tomography in materials science. Ultramicroscopy 160, 23–34 (2016)
53. M. Makitalo, A. Foi, Optimal inversion of the Anscombe transformation in low-count Poisson
image denoising. IEEE Trans. Image Process. 20, 99–109 (2011)
54. M. Makitalo, A. Foi, Optimal inversion of the generalized Anscombe transformation for
Poisson-Gaussian noise. IEEE Trans. Image Process. 22, 91–103 (2013)
55. M. López-Haro et al., A macroscopically relevant 3D-metrology approach for nanocatalysis
research. Part. Part. Syst. Charact. 35, 1700343 (2018)
56. B. Goris, W. Van den Broek, K.J. Batenburg, H.H. Mezerji, S. Bals, Electron tomography based
on a total variation minimization reconstruction technique. Ultramicroscopy 113, 120–130
(2012)
57. F. Murtagh, P. Legendre, Ward’s hierarchical agglomerative clustering method: which
algorithms implement ward’s criterion? J. Classif. 31, 274–295 (2014)
283
34. P. Torruella et al., 3D visualization of the iron oxidation state in FeO/Fe 3 O 4 core-shell
nanocubes from electron energy loss tomography. Nano Lett. 16, 5068–5073 (2016)
35. L. Rudin, Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268
(1992)
36. L. Yedra et al., EELS tomography in multiferroic nanocomposites: from spectrum images to
the spectrum volume. Nanoscale 6, 6646–6650 (2014)
37. L. Yedra et al., EEL spectroscopic tomography: towards a new dimension in nanomaterials
analysis. Ultramicroscopy 122, 12–18 (2012)
38. H. Abdi, L.J. Williams, Principal component analysis. WIREs Comp. Stat. 2, 433–459 (2010)
39. A. Hyvärinen, J. Karhunen, E. Oja, Independent component analysis (2004)
40. J.D. Bayliss, J.A. Gualtieri, R.F. Cromp, Analyzing hyperspectral data with independent
component analysis, in (ed. J.M. Selander) vol. 3240, pp. 133–143 (International Society for
Optics and Photonics, 1998)
41. W. Van den Broek et al., Correction of non-linear thickness effects in HAADF STEM electron
tomography. Ultramicroscopy 116, 8–12 (2012)
42. N. Dobigeon, S. Moussaoui, M. Coulon, J.-Y. Tourneret, A.O. Hero, Joint bayesian end member
extraction and linear unmixing for hyperspectral imagery. IEEE Trans. Signal Process. 57,
4355–4368 (2009)
43. N. Dobigeon, N. Brun, Ultramicroscopy spectral mixture analysis of EELS spectrum-images.
Ultramicroscopy 120, (2012)
44. J. Nascimento, J. Bioucas-Dias, Does independent component analysis play a role in unmixing
hyperspectral data? IEEE Trans. Geosci. Remote Sens. 43, 175–187 (2005)
45. P. Torruella et al., Clustering analysis strategies for electron energy loss spectroscopy (EELS).
Ultramicroscopy 185, 42–48 (2018)
46. L.A. Giannuzzi, F.A. Stevie, A review of focused ion beam milling techniques for TEM specimen preparation 10_1016-S0968-4328(99), 00005–0 Micron ScienceDirect_com. Micron 30,
197–204 (1999)
47. F. de la Peña, et al., Hyperspy/Hyperspy: Hyperspy 1.3. hyperspy/hyperspy: HyperSpy 1.3
(2017). https://doi.org/10.5281/zenodo.583693
48. A.K. Jain, Data clustering: 50 years beyond K-means. Pattern Recognit. Lett. 31, 651–666
(2010)
49. J. Sander, M. Ester, H.P.P. Kriegel, X. Xu, Density-based clustering in spatial databases: the
algorithm GDBSCAN and its applications. Data Min. Knowl. Discovery 194, 169–194 (1998)
50. I. Davidson, S.S. Ravi, Agglomerative hierarchical clustering with constraints: theory and
empirical resutls, in 9th European Conference on Principles of Data Mining and Knowledge
Discovery Databases, PKDD 2005, pp. 59–70 (2005)
51. L. Yedra et al., Oxide wizard: an EELS application to characterize the white lines of transition
metal edges. Microsc. Microanal. 20, 698–705 (2014)
52. T. Printemps et al., Self-adapting denoising, alignment and reconstruction in electron
tomography in materials science. Ultramicroscopy 160, 23–34 (2016)
53. M. Makitalo, A. Foi, Optimal inversion of the Anscombe transformation in low-count Poisson
image denoising. IEEE Trans. Image Process. 20, 99–109 (2011)
54. M. Makitalo, A. Foi, Optimal inversion of the generalized Anscombe transformation for
Poisson-Gaussian noise. IEEE Trans. Image Process. 22, 91–103 (2013)
55. M. López-Haro et al., A macroscopically relevant 3D-metrology approach for nanocatalysis
research. Part. Part. Syst. Charact. 35, 1700343 (2018)
56. B. Goris, W. Van den Broek, K.J. Batenburg, H.H. Mezerji, S. Bals, Electron tomography based
on a total variation minimization reconstruction technique. Ultramicroscopy 113, 120–130
(2012)
57. F. Murtagh, P. Legendre, Ward’s hierarchical agglomerative clustering method: which
algorithms implement ward’s criterion? J. Classif. 31, 274–295 (2014)