An improved segmentation method for porous transducer CT images

Meiling Wang; Ruoyu Guo; Ke Ning; Li Ming

doi:10.1117/12.2539604

14 August 2019 An improved segmentation method for porous transducer CT images

Meiling Wang, Ruoyu Guo, Ke Ning, Li Ming

Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 111790P (2019) https://doi.org/10.1117/12.2539604
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China

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.

Citation Download Citation

Meiling Wang, Ruoyu Guo, Ke Ning, and Li Ming "An improved segmentation method for porous transducer CT images", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111790P (14 August 2019); https://doi.org/10.1117/12.2539604

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KEYWORDS

Image segmentation

Transducers

Computed tomography

Gaussian filters

3D modeling

Performance modeling

Data modeling

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