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

Xiaojiao Xiao, Juanjuan Zhao, Yan Qiang, Hua Wang, Yingze Xiao, Xiaolong Zhang, Yudong Zhang

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number832
JournalApplied Sciences (Switzerland)
Volume8
Issue number5
DOIs
StatePublished - May 21 2018

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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