Three-dimensional reconstruction of liver tumor image based on fractional differential equation algorithm

An Rui; Yu Peng; Yang Zhiyuan; Liu Jihua

doi:10.3934/era.2020030

Article Contents

2020, Volume 28, Issue 1: 381-391. Doi: 10.3934/era.2020030

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Three-dimensional reconstruction of liver tumor image based on fractional differential equation algorithm

Department of Medical Radiology, First Teaching Hospital of Tianjin, University of Traditional Chinese Medicine, Tianjin City 300381, China

^* Corresponding author: Jihua Liu
^* Corresponding author: Jihua Liu

Received: August 2019

Revised: November 2019

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Abstract

In view of the large noise of current liver tumor image segmentation CT, a fractional differential equation algorithm with edge preservation is used to perform image preprocessing. The multi-threshold fractional differential equation algorithm is used to segment the CT images of human liver parts, and the ideal results are obtained. In the following three-dimensional reconstruction process, the paper uses the visual tool library VTK, which is commonly used in medical 3D reconstruction, to render and reconstruct the segmented blood vessels, which lays a solid foundation for the next step of surgical navigation.

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Mathematics Subject Classification: Primary: 60G22, 60J70; Secondary: 60G22.

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Figure 1. Liver CT image

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Figure 2. Different energy curves corresponding to different parameters

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Figure 3. Comparison of two methods of denoising

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Figure 4. Schematic diagrams of the internal liver tube

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Figure 5. Original CT image and smooth, segmented liver image

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Figure 6. Multi-threshold OTSU segmentation of the sixth layer of liver CT images

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Figure 7. MC algorithm example

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Figure 8. Three-dimensional reconstruction of hepatic veins

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Figure 9. Three-dimensional smoothing effect diagrams

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Table 1. Comparison of vascular area obtained by segmentation method

Segmentation method	Vascular area as a percentage of liver area
Segmentation method	6 floors	15 floors	30 floors
Manual outline	20.13	2.95	3.12
OTSU	15.64	2.46	2.82

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