Volume 14, Issue 5 e1924
ORIGINAL ARTICLE

Prior knowledge snake segmentation of ultrasound images denoised by J-divergence anisotropy diffusion

Jiawen Yan

Jiawen Yan

State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China

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Bo Pan

Corresponding Author

Bo Pan

State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China

Correspondence

Bo Pan, Room 306, C1 building, No. 2 Yikuang Street, Nangang District, Harbin, Heilongjiang Province, 150001, China.

Email: [email protected]

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Yunfeng Qi

Yunfeng Qi

The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China

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Jin Ben

Jin Ben

State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China

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Yili Fu

Yili Fu

State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China

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First published: 06 June 2018
Citations: 3

Abstract

Background

Applying transrectal ultrasound to robot-assisted laparoscopic radical prostatectomy has attracted attention in recent years, and it is considered as a proper method to provide real-time subsurface anatomic features. A precise registration between the ultrasound equipment and robotic surgical system is necessary, which usually requires a fast and accurate recognition of the registration tool in the ultrasound image.

Methods

Tissue forceps are chosen as the registration tool. J-divergence anisotropy diffusion and prior knowledge snake segmentation are proposed for the automatic recognition of forceps in ultrasound images.

Results

Simulation, gel tissue phantom experiments and in vitro experiments are carried out. Several evaluation indices are calculated to compare results under different methods.

Conclusions

The proposed methods are proved to be practicable, reliable and superior to existing ones, with reduced calculation time and higher accuracy.

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