Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/87082
Title: Hybrid-feature-guided lung nodule type classification on CT images
Authors: Yuan, Jingjing
Liu, Xinglong
Hou, Fei
Qin, Hong
Hao, Aimin
Keywords: Computer Tomography
Lung Nodule
Issue Date: 2017
Source: Yuan, J., Liu, X., Hou, F., Qin, H., & Hao, A. (2018). Hybrid-feature-guided lung nodule type classification on CT images. Computers & Graphics, 70, 288-299.
Series/Report no.: Computers & Graphics
Abstract: In this paper, we propose a novel classification method for lung nodules from CT images based on hybrid features. Towards nodules of different types, including well-circumscribed, vascularized, juxta-pleural, pleural-tail, as well as ground glass optical (GGO) and non-nodule from CT scans, our method has achieved promising classification results. The proposed method utilizes hybrid descriptors consisting of statistical features from multi-view multi-scale convolutional neural networks (CNNs) and geometrical features from Fisher vector (FV) encodings based on scale-invariant feature transform (SIFT). First, we approximate the nodule radii based on icosahedron sampling and intensity analysis. Then, we apply high frequency content measure analysis to obtain sampling views with more abundant information. After that, based on re-sampled views, we train multi-view multi-scale CNNs to extract statistical features and calculate FV encodings as geometrical features. Finally, we achieve hybrid features by merging statistical and geometrical features based on multiple kernel learning (MKL) and classify nodule types through a multi-class support vector machine. The experiments on LIDC-IDRI and ELCAP have shown that our method has achieved promising results and can be of great assistance for radiologists’ diagnosis of lung cancer in clinical practice.
URI: https://hdl.handle.net/10356/87082
http://hdl.handle.net/10220/44310
ISSN: 0097-8493
DOI: 10.1016/j.cag.2017.07.020
Rights: © 2017 Elsevier. This is the author created version of a work that has been peer reviewed and accepted for publication by Computers & Graphics, Elsevier. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1016/j.cag.2017.07.020].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

Files in This Item:
File Description SizeFormat 
Hybrid-feature-guided lung nodule type classification on CT images.pdf19.97 MBAdobe PDFThumbnail
View/Open

SCOPUSTM   
Citations 50

27
Updated on Jan 11, 2021

PublonsTM
Citations 50

19
Updated on Jan 14, 2021

Page view(s) 50

156
Updated on Jan 17, 2021

Download(s) 20

185
Updated on Jan 17, 2021

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.