Lung nodule classification with multilevel patch-based context analysis

Fan Zhang, Yang Song, Weidong Cai, Min Zhao Lee, Yun Zhou, Heng Huang, Shimin Shan, Michael J. Fulham, Dagan D. Feng

Research output: Contribution to journalArticlepeer-review

53 Scopus citations


In this paper, we propose a novel classification method for the four types of lung nodules, i.e., well-circumscribed, vascularized, juxta-pleural, and pleural-tail, in low dose computed tomography scans. The proposed method is based on contextual analysis by combining the lung nodule and surrounding anatomical structures, and has three main stages: an adaptive patch-based division is used to construct concentric multilevel partition; then, a new feature set is designed to incorporate intensity, texture, and gradient information for image patch feature description, and then a contextual latent semantic analysis-based classifier is designed to calculate the probabilistic estimations for the relevant images. Our proposed method was evaluated on a publicly available dataset and clearly demonstrated promising classification performance.

Original languageEnglish (US)
Article number6690248
Pages (from-to)1155-1166
Number of pages12
JournalIEEE Transactions on Biomedical Engineering
Issue number4
StatePublished - 2014


  • Classification
  • feature design
  • latent semantic analysis
  • patch division

ASJC Scopus subject areas

  • Biomedical Engineering


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