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BioMed Research International
Volume 2016, Article ID 3162649, 10 pages
http://dx.doi.org/10.1155/2016/3162649
Research Article

Rapid Retrieval of Lung Nodule CT Images Based on Hashing and Pruning Methods

1College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China
2Shanxi Provincial People’s Hospital, Taiyuan 030012, China
3University of Texas at Tyler, 3900 University Blvd., Tyler, TX 75799, USA

Received 19 July 2016; Accepted 18 October 2016

Academic Editor: Yong Xia

Copyright © 2016 Ling Pan 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

The similarity-based retrieval of lung nodule computed tomography (CT) images is an important task in the computer-aided diagnosis of lung lesions. It can provide similar clinical cases for physicians and help them make reliable clinical diagnostic decisions. However, when handling large-scale lung images with a general-purpose computer, traditional image retrieval methods may not be efficient. In this paper, a new retrieval framework based on a hashing method for lung nodule CT images is proposed. This method can translate high-dimensional image features into a compact hash code, so the retrieval time and required memory space can be reduced greatly. Moreover, a pruning algorithm is presented to further improve the retrieval speed, and a pruning-based decision rule is presented to improve the retrieval precision. Finally, the proposed retrieval method is validated on 2,450 lung nodule CT images selected from the public Lung Image Database Consortium (LIDC) database. The experimental results show that the proposed pruning algorithm effectively reduces the retrieval time of lung nodule CT images and improves the retrieval precision. In addition, the retrieval framework is evaluated by differentiating benign and malignant nodules, and the classification accuracy can reach 86.62%, outperforming other commonly used classification methods.