RbQE: An Efficient Method for Content-Based Medical Image Retrieval Based on Query Expansion.
Systems for retrieving and managing content-based medical images are becoming more important, especially as medical imaging technology advances and the medical image database grows. In addition, these systems can also use medical images to better grasp and gain a deeper understanding of the causes a...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 3; pp. 1248 - 1262 |
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| Autores principales: | , , |
| Formato: | algorithm diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2023
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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=164473096&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164473096 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2023 vid: 36 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 164473096 161503330 164473096 164473096 10.1007/s10278-022-00769-7 164473096 ppf: 1248 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: RbQE: An Efficient Method for Content-Based Medical Image Retrieval Based on Query Expansion. aug: au: Rashad, Metwally Afifi, Ibrahem Abdelfatah, Mohammed affil: Department of Computer Science, Faculty of Computers & Artificial Intelligence, Benha University, Benha, Egypt sug: subj: Image Retrieval Systems Methods Information Retrieval Methods Algorithms Human Tomography, X-Ray Computed Magnetic Resonance Imaging Sensitivity and Specificity Deep Learning Methods Descriptive Statistics Funding Source ab: Systems for retrieving and managing content-based medical images are becoming more important, especially as medical imaging technology advances and the medical image database grows. In addition, these systems can also use medical images to better grasp and gain a deeper understanding of the causes and treatments of different diseases, not just for diagnostic purposes. For achieving all these purposes, there is a critical need for an efficient and accurate content-based medical image retrieval (CBMIR) method. This paper proposes an efficient method (RbQE) for the retrieval of computed tomography (CT) and magnetic resonance (MR) images. RbQE is based on expanding the features of querying and exploiting the pre-trained learning models AlexNet and VGG-19 to extract compact, deep, and high-level features from medical images. There are two searching procedures in RbQE: a rapid search and a final search. In the rapid search, the original query is expanded by retrieving the top-ranked images from each class and is used to reformulate the query by calculating the mean values for deep features of the top-ranked images, resulting in a new query for each class. In the final search, the new query that is most similar to the original query will be used for retrieval from the database. The performance of the proposed method has been compared to state-of-the-art methods on four publicly available standard databases, namely, TCIA-CT, EXACT09-CT, NEMA-CT, and OASIS-MRI. Experimental results show that the proposed method exceeds the compared methods by 0.84%, 4.86%, 1.24%, and 14.34% in average retrieval precision (ARP) for the TCIA-CT, EXACT09-CT, NEMA-CT, and OASIS-MRI databases, respectively. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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