![](/images/graphics-bg.png)
Empirical Driven Automatic Detection of Lobulation Imaging Signs in Lung CT
Joint Authors
Qadeer, Nouman
Liu, Xiabi
Han, Guanghui
Zhao, Yanfeng
Zhao, Xinming
Zhou, Chunwu
Sun, Jia
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-03-29
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
Computer-aided detection (CAD) of lobulation can help radiologists to diagnose/detect lung diseases easily and accurately.
Compared to CAD of nodule and other lung lesions, CAD of lobulation remained an unexplored problem due to very complex and varying nature of lobulation.
Thus, many state-of-the-art methods could not detect successfully.
Hence, we revisited classical methods with the capability of extracting undulated characteristics and designed a sliding window based framework for lobulation detection in this paper.
Under the designed framework, we investigated three categories of lobulation classification algorithms: template matching, feature based classifier, and bending energy.
The resultant detection algorithms were evaluated through experiments on LISS database.
The experimental results show that the algorithm based on combination of global context feature and BOF encoding has best overall performance, resulting in F1 score of 0.1009.
Furthermore, bending energy method is shown to be appropriate for reducing false positives.
We performed bending energy method following the LIOP-LBP mixture feature, the average positive detection per image was reduced from 30 to 22, and F1 score increased to 0.0643 from 0.0599.
To the best of our knowledge this is the first kind of work for direct lobulation detection and first application of bending energy to any kind of lobulation work.
American Psychological Association (APA)
Han, Guanghui& Liu, Xiabi& Qadeer, Nouman& Sun, Jia& Zhao, Yanfeng& Zhao, Xinming…[et al.]. 2017. Empirical Driven Automatic Detection of Lobulation Imaging Signs in Lung CT. BioMed Research International،Vol. 2017, no. 2017, pp.1-15.
https://search.emarefa.net/detail/BIM-1136446
Modern Language Association (MLA)
Han, Guanghui…[et al.]. Empirical Driven Automatic Detection of Lobulation Imaging Signs in Lung CT. BioMed Research International No. 2017 (2017), pp.1-15.
https://search.emarefa.net/detail/BIM-1136446
American Medical Association (AMA)
Han, Guanghui& Liu, Xiabi& Qadeer, Nouman& Sun, Jia& Zhao, Yanfeng& Zhao, Xinming…[et al.]. Empirical Driven Automatic Detection of Lobulation Imaging Signs in Lung CT. BioMed Research International. 2017. Vol. 2017, no. 2017, pp.1-15.
https://search.emarefa.net/detail/BIM-1136446
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1136446