title: Lung nodule classification with multilevel patch-based context analysis.
abstract: 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.
authors.forename: Fan
authors.initials: F
authors.lastname: Zhang
authors.forename: Yang
authors.initials: Y
authors.lastname: Song
authors.forename: Weidong
authors.initials: W
authors.lastname: Cai
authors.forename: Min-Zhao
authors.initials: MZ
authors.lastname: Lee
authors.forename: Yun
authors.initials: Y
authors.lastname: Zhou
authors.forename: Heng
authors.initials: H
authors.lastname: Huang
authors.forename: Shimin
authors.initials: S
authors.lastname: Shan
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Fulham
authors.forename: Dagan D
authors.initials: DD
authors.lastname: Feng
mesh_terms: D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008175:Lung Neoplasms / Q000145:classification* / Q000000981:diagnostic imaging / Q000473:pathology; D011859:Radiography; D012660:Semantics; D060388:Support Vector Machine