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Indexed by:期刊论文

Date of Publication:2014-04-01

Journal:IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING

Included Journals:SCIE、EI

Volume:61

Issue:4

Page Number:1155-1166

ISSN No.: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.