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BioMed Research International
Volume 2017, Article ID 3842659, 15 pages
https://doi.org/10.1155/2017/3842659
Research Article

Empirical Driven Automatic Detection of Lobulation Imaging Signs in Lung CT

1Beijing Key Laboratory of Intelligent Information Technology, School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China
2Department of Software Engineering, Mehran University of Engineering and Technology, SZAB Campus, Khairpur Mir’s, Pakistan
3Department of Imaging Diagnosis, Cancer Institute and Hospital, Chinese Academy of Medical Sciences, Beijing, China

Correspondence should be addressed to Guanghui Han; moc.kooltuo@iuhgnaugnah

Received 23 October 2016; Accepted 22 December 2016; Published 29 March 2017

Academic Editor: Kwang Gi Kim

Copyright © 2017 Guanghui Han et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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 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 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.