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Rapid Retrieval of Lung Nodule CT Images Based on Hashing and Pruning Methods.
- Source :
-
BioMed Research International . 11/22/2016, Vol. 2016, p1-10. 10p. - Publication Year :
- 2016
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *LUNG radiography
*ALGORITHMS
*COMPARATIVE studies
*COMPUTED tomography
*DATABASES
*INFORMATION retrieval
*MEDICAL information storage & retrieval systems
*LUNG tumors
*RESEARCH funding
*TIME
*DECISION making in clinical medicine
*EARLY diagnosis
*DATA analysis software
*DESCRIPTIVE statistics
*COMPUTER-aided diagnosis
*DIAGNOSIS
Subjects
Details
- Language :
- English
- ISSN :
- 23146133
- Volume :
- 2016
- Database :
- Academic Search Index
- Journal :
- BioMed Research International
- Publication Type :
- Academic Journal
- Accession number :
- 119626981
- Full Text :
- https://doi.org/10.1155/2016/3162649