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An automated segmentation method for lung parenchyma image sequences based on fractal geometry and convex hull algorithm

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journal contribution
posted on 2018-06-04, 14:26 authored by Xiaojiao Xiao, Juanjuan Zhao, Yan Qiang, Hua Wang, Yingze Xiao, Xiaolong Zhang, Yudong Y Zhang
Statistically solitary pulmonary nodules are about 6% to 17% of juxtapleural nodules. The accurate segmentation of lung parenchyma sequences of juxtapleural nodules is the basis of subsequent pulmonary nodule segmentation and detection. In order to solve the problem of incomplete segmentation of the juxtapleural nodules and segmentation inefficiency, this paper proposes an automated framework to combine the threshold iteration method to segment the lung parenchyma images and the fractal geometry method to detect the depression boundary. The framework includes an improved convex hull repair to complete the accurate segmentation of the lung parenchyma. The evaluation results confirm that the proposed method can segment juxtapleural lung parenchymal images accurately and efficiently.

Funding

This work was supported in part by the National Natural Science Foundation of China (61572344), in part by the open funding project of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (Grant No. BUAA-VR-17KF-14; BUAA-VR-17KF-15), in part by Research Project Supported by Shanxi Scholarship Council of China (2016-038).

History

Citation

Applied Sciences, 2018, 8, 832

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Informatics

Version

  • VoR (Version of Record)

Published in

Applied Sciences

Publisher

MDPI

issn

2076-3417

eissn

2076-3417

Acceptance date

2018-05-18

Copyright date

2018

Available date

2018-06-04

Publisher version

http://www.mdpi.com/2076-3417/8/5/832

Notes

This paper uses LIDC data set. The LIDC–IDRI database is the largest open lung nodule database in the world, which contains 1080 cases. https://wiki.cancerimagingarchive.net/display/Public/ LIDC-IDRI.

Language

en

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