title: Rapid Retrieval of Lung Nodule CT Images Based on Hashing and Pruning Methods.
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.
authors.affiliation: College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China.
authors.forename: Ling
authors.initials: L
authors.lastname: Pan
authors.affiliation: College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China.
authors.forename: Yan
authors.identifier: 0000-0001-6231-3721
authors.initials: Y
authors.lastname: Qiang
authors.affiliation: Shanxi Provincial People's Hospital, Taiyuan 030012, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Yuan
authors.affiliation: University of Texas at Tyler, 3900 University Blvd., Tyler, TX 75799, USA.
authors.forename: Lidong
authors.initials: L
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D057225:Data Mining / Q000379:methods*; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D000069550:Machine Learning; D010363:Pattern Recognition, Automated / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011873:Radiology Information Systems / Q000458:organization & administration*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*