Scanning precession electron tomography for three-dimensional nanoscale orientation imaging and crystallographic analysis.

Three-dimensional (3D) reconstructions from electron tomography provide important morphological, compositional, optical and electro-magnetic information across a wide range of materials and devices. Precession electron diffraction, in combination with scanning transmission electron microscopy, can be used to elucidate the local orientation of crystalline materials. Here we show, using the example of a Ni-base superalloy, that combining these techniques and extending them to three dimensions, to produce scanning precession electron tomography, enables the 3D orientation of nanoscale sub-volumes to be determined and provides a one-to-one correspondence between 3D real space and 3D reciprocal space for almost any polycrystalline or multi-phase material.A.S.E. and P.A.M acknowledge financial support from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement 291522-3DIMAGE, the Seventh Framework Programme of the European Commission: ESTEEM2, contract number 312483, EPSRC grant number EP/H017712/1 and the Royal Society. R.K. acknowledges financial support from Rolls-Royce, EPSRC and the BMWi under EP/H022309/1, EP/H500375/1 and grant number 20T0813. We are grateful to Professor Edgar Rauch for valuable discussion on the use of the Astar system, to Dr Cathie Rae and Dr Mark Hardy of Rolls-Royce for supply of the superalloy samples and valuable discussion about their microstructure, Dr Zineb Saghi for help with the tomographic reconstructions and Dr Francisco de la Peña for help with the NMF decompositions.This is the final version. It was first published by NPG at http://www.nature.com/ncomms/2015/150601/ncomms8267/full/ncomms8267.html

New marked point process models for microscopy images

In developing new materials, the characterization of microstructures is one of the key steps. To characterize the microstructure, many microscope modalities have been devised and improved over decades. With the increase in image resolution in the spatial and time domains, the amount of image data keeps increasing in the fields such as materials science and biomedical engineering. As a result, image processing plays a critical role in this era of science and technology. In materials image analysis, image segmentation and feature detection are considered very important. The first part of this research aims to resolve the segmentation problem caused by blurring artifacts in scanning electron microscopy (SEM) images. This blurring issue can lead to a bridged channel problem, which becomes an obstacle in analyzing the microstructures. To tackle the problem, we propose a joint deconvolution and segmentation (JDS) method. As a segmentation method, we use the expectation-maximization/maximization of the posterior marginals (EM/MPM) method, using the Markov random field (MRF) prior model. Experiments show the proposed method improves the segmentation result at object boundaries. The next phase of the image segmentation is detecting image features. In the second part of this research, we detect channel configurations in materials images. We propose a new approach of channel identification, based on the marked point process (MPP) framework, to effectively detect channels in materials images. To describe a higher level of structures in an image, the MPP framework is more effective than the MRF prior model. The reversible-jump Markov chain Monte Carlo (RJMCMC) algorithm embedded with simulated annealing is used as an optimization method, and a new switching kernel in an RJMCMC is used to reduce computational time. The channel configuration is useful in characterizing materials images. In addition, this information can be used to reduce the bridged channel problem more effectively. In materials image processing, one of the most important goals of feature detection is identifying the 3D structure of objects from 3D microscope datasets. The final part of this research is to perform fast and accurate estimation of 3D object configurations from a 3D dataset. We propose a fast 3D fitting method to improve the computational complexity over a full-search 3D MPP method. Experiments show that the fast 3D fitting method significantly decreases execution time compared to the full 3D MPP method