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J Synchrotron Radiat. 2017 Sep 1;24(Pt 5):1065-1077. doi: 10.1107/S1600577517010955. Epub 2017 Aug 23.

Insight into 3D micro-CT data: exploring segmentation algorithms through performance metrics.

Author information

1
Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720-8150, USA.
2
Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, CA 94720-8150, USA.
3
Materials Department, University of California Santa Barbara, Santa Barbara, CA 93106-5050, USA.
4
Department of Mathematics, University of California Berkeley, Berkeley, CA 94720, USA.

Abstract

Three-dimensional (3D) micro-tomography (µ-CT) has proven to be an important imaging modality in industry and scientific domains. Understanding the properties of material structure and behavior has produced many scientific advances. An important component of the 3D µ-CT pipeline is image partitioning (or image segmentation), a step that is used to separate various phases or components in an image. Image partitioning schemes require specific rules for different scientific fields, but a common strategy consists of devising metrics to quantify performance and accuracy. The present article proposes a set of protocols to systematically analyze and compare the results of unsupervised classification methods used for segmentation of synchrotron-based data. The proposed dataflow for Materials Segmentation and Metrics (MSM) provides 3D micro-tomography image segmentation algorithms, such as statistical region merging (SRM), k-means algorithm and parallel Markov random field (PMRF), while offering different metrics to evaluate segmentation quality, confidence and conformity with standards. Both experimental and synthetic data are assessed, illustrating quantitative results through the MSM dashboard, which can return sample information such as media porosity and permeability. The main contributions of this work are: (i) to deliver tools to improve material design and quality control; (ii) to provide datasets for benchmarking and reproducibility; (iii) to yield good practices in the absence of standards or ground-truth for ceramic composite analysis.

KEYWORDS:

ceramic matrix composites; image analysis; micro-tomography; unsupervised segmentation

PMID:
28862630
DOI:
10.1107/S1600577517010955

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