Detail publikace

Segmentation of multi-phase object applying trainable segmentation

Originální název

Segmentation of multi-phase object applying trainable segmentation

Anglický název

Segmentation of multi-phase object applying trainable segmentation

Jazyk

en

Originální abstrakt

In X-ray computed tomography (CT), post-processing of acquired data is necessary for obtaining quantitative information of the object. As initial step, it is necessary to segment different materials of the sample. The easiest and standardly used segmentation method is based on global thresholding according to histogram, but it works well only if histogram with multi-modal character where the intensity is distributed to the separate count peaks. In this paper, we show the possibility of segmentation of tomographic data using trainable segmentation on data, where standard global thresholding fails. Trainable segmentation is a method that combines a collection of machine learning algorithms (decision tree, neural network, etc.) with a set of selected image features to produce binary pixel-based segmentation. This method is demonstrated on a sample of meteorite consisting of multiple phases (silicates, metals, sulphides), where knowledge of volumes of different materials is important for non-destructive study of modal phase composition, meteorite microstructures and identification of lithologies with different origin and evolution.

Anglický abstrakt

In X-ray computed tomography (CT), post-processing of acquired data is necessary for obtaining quantitative information of the object. As initial step, it is necessary to segment different materials of the sample. The easiest and standardly used segmentation method is based on global thresholding according to histogram, but it works well only if histogram with multi-modal character where the intensity is distributed to the separate count peaks. In this paper, we show the possibility of segmentation of tomographic data using trainable segmentation on data, where standard global thresholding fails. Trainable segmentation is a method that combines a collection of machine learning algorithms (decision tree, neural network, etc.) with a set of selected image features to produce binary pixel-based segmentation. This method is demonstrated on a sample of meteorite consisting of multiple phases (silicates, metals, sulphides), where knowledge of volumes of different materials is important for non-destructive study of modal phase composition, meteorite microstructures and identification of lithologies with different origin and evolution.

BibTex


@article{BUT133386,
  author="Dominika {Kalasová} and Jan {Mašek} and Tomáš {Zikmund} and Pavel {Spurný} and Jakub {Haloda} and Radim {Burget} and Jozef {Kaiser}",
  title="Segmentation of multi-phase object applying trainable segmentation",
  annote="In X-ray computed tomography (CT), post-processing of acquired data is necessary for obtaining quantitative information of the object. As initial step, it is necessary to segment different materials of the sample. The easiest and standardly used segmentation method is based on global thresholding according to histogram, but it works well only if histogram with multi-modal character where the intensity is distributed to the separate count peaks.
In this paper, we show the possibility of segmentation of tomographic data using trainable segmentation on data, where standard global thresholding fails. Trainable segmentation is a method that combines a collection of machine learning algorithms (decision tree, neural network, etc.) with a set of selected image features to produce binary pixel-based segmentation. This method is demonstrated on a sample of meteorite consisting of multiple phases (silicates, metals, sulphides), where knowledge of volumes of different materials is important for non-destructive study of modal phase composition, meteorite microstructures and identification of lithologies with different origin and evolution.",
  address="NDT.net",
  booktitle="Conference proceedings: 7th Conference on Industrial Computed Tomography (ICT) 2017",
  chapter="133386",
  howpublished="online",
  institution="NDT.net",
  number="2017",
  year="2017",
  month="february",
  pages="1--6",
  publisher="NDT.net",
  type="journal article - other"
}