Release Time:2019-03-09  Hits:

Indexed by: Journal Article

Date of Publication: 2014-04-01

Journal: IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING

Included Journals: EI、SCIE

Volume: 61

Issue: 4

Page Number: 1155-1166

ISSN: 0018-9294

Key Words: Classification; feature design; latent semantic analysis; patch division

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.