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Proceedings Paper

An improved segmentation method for porous transducer CT images
Author(s): Meiling Wang; Ruoyu Guo; Ke Ning; Li Ming
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Paper Abstract

The paper presents an improved image segmentation method with a straightforward workflow for porous transducer CT images, which can be used to establish porous transducer three-dimensional model and further study its characteristics. Data distribution of CT images is firstly analyzed and Gaussian filtering is conducted to reduce divergence of CT images. An improved fully convolutional neural network model based on U-Net, for which multi-channel images are set as network input, is trained using training set. The proposed method improves pore connectivity of the segmentation results. Improvement of porosity and permeability relative errors as well as MIOU on test set shows that the proposed method is an effective and generic two-phase segmentation method for porous transducer CT images without need of adjusting any parameters.

Paper Details

Date Published: 14 August 2019
PDF: 8 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111790P (14 August 2019); doi: 10.1117/12.2539604
Show Author Affiliations
Meiling Wang, Beijing Institute of Technology (China)
Ruoyu Guo, Beijing Institute of Technology (China)
Ke Ning, Beijing Institute of Technology (China)
Li Ming, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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