Lung Nodule Classification With Multilevel Patch-Based Context Analysis

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...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2014-04, Vol.61 (4), p.1155-1166
Main Authors: Zhang, Fan, Song, Yang, Cai, Weidong, Lee, Min-Zhao, Zhou, Yun, Huang, Heng, Shan, Shimin, Fulham, Michael J, Feng, Dagan D.
Format: Article
Language:English
Subjects:
Anatomical structure
Classification
Context
Educational institutions
feature design
Feature extraction
Histograms
Humans
Image Processing, Computer-Assisted - methods
latent semantic analysis
Lung Neoplasms - classification
Lung Neoplasms - diagnostic imaging
Lung Neoplasms - pathology
Lungs
patch division
Radiography
Semantics
Shape
Support Vector Machine
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Summary: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.
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2013.2295593