Overlapping Node Discovery for Improving Classification of Lung Nodules

被引:0
|
作者
Zhang, Fan [1 ]
Cai, Weidong [1 ]
Song, Yang [1 ]
Lee, Min-Zhao [2 ]
Shan, Shimin [3 ]
Feng, David Dagan [2 ,4 ]
机构
[1] Univ Sydney, Sch Informat Technol, Biomed & Multimedia Informat Technol BMIT Res Grp, Sydney, NSW 2006, Australia
[2] Univ Sydney, Sch Informat Technol, BMIT Res Grp, Sydney, NSW 2006, Australia
[3] Dalian Univ Technol, Sch Software, Dalian, Peoples R China
[4] Shanghai Jiao Tong Univ, Med X Res Inst, Shanghai 200030, Peoples R China
来源
2013 35TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) | 2013年
关键词
Lung nodules; Classification; SVM; CPMw; Overlap;
D O I
暂无
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Distinguishing malignant lung nodules from benign nodules is an important aspect of lung cancer diagnosis. In this paper, we propose an automatic method to classify lung nodules into four different types, i.e. well-circumscribed, juxta-vascular, juxta-pleural and pleural-tail. Additionally, since the morphology of lung nodules forms a continuum between the different types, our proposed method is superior to previous methods that classify single nodules into a single type. First, a weighted similarity network is constructed based on the SVM with probability estimates, turning the 128-length SIFT descriptor to a 4-length probability vector against the four types. Then, the classification of nodules while identifying those with overlapping types is made using the weighed Clique Percolation Method (CPMw). We evaluate the proposed method on low-dose CT images from ELCAP. Our results show that there is more overlap between well-circumscribed and juxta-vascular, and between juxta-pleural and pleural tail. Also, quantitative comparisons among various methods demonstrate highly effective nodule classification results by identifying the overlapping nodule types.
引用
收藏
页码:5461 / 5464
页数:4
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