Rapid Retrieval of Lung Nodule CT Images Based on Hashing and Pruning Methods.
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...
| Publicado en: | BioMed Research International Vol. 2016; pp. 1 - 11 |
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| Autores principales: | , , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
11/22/2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=119626981&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 119626981 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 11/22/2016 vid: 2016 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 119626981 119626981 119626981 10.1155/2016/3162649 119626981 ppf: 1 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Rapid Retrieval of Lung Nodule CT Images Based on Hashing and Pruning Methods. aug: au: Pan, Ling Qiang, Yan Yuan, Jie Wu, Lidong affil: College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China sug: subj: Lung Radiography Tomography, X-Ray Computed Diagnosis, Computer Assisted Lung Neoplasms Diagnosis Lung Neoplasms Radiography Information Retrieval Algorithms Decision Making, Clinical Time Factors Descriptive Statistics Databases, Health Lung Neoplasms Classification Data Analysis Software Early Diagnosis Comparative Studies Funding Source ab: 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. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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