A multi-level similarity measure for the retrieval of the common CT imaging signs of lung diseases.
The common CT imaging signs of lung diseases (CISLs) which frequently appear in lung CT images are widely used in the diagnosis of lung diseases. Computer-aided diagnosis (CAD) based on the CISLs can improve radiologists' performance in the diagnosis of lung diseases. Since similarity measure is imp...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 5; pp. 1015 - 1030 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
May2020
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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=142943691&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142943691 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: May2020 vid: 58 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142943691 142943691 NLM32124223 10.1007/s11517-020-02146-4 NLM32124223 142943691 ppf: 1015 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A multi-level similarity measure for the retrieval of the common CT imaging signs of lung diseases. aug: au: Ma, Ling Liu, Xiabi Fei, Baowei affil: College of Software, Nankai University, Tianjin, People's Republic of China sug: ab: The common CT imaging signs of lung diseases (CISLs) which frequently appear in lung CT images are widely used in the diagnosis of lung diseases. Computer-aided diagnosis (CAD) based on the CISLs can improve radiologists' performance in the diagnosis of lung diseases. Since similarity measure is important for CAD, we propose a multi-level method to measure the similarity between the CISLs. The CISLs are characterized in the low-level visual scale, mid-level attribute scale, and high-level semantic scale, for a rich representation. The similarity at multiple levels is calculated and combined in a weighted sum form as the final similarity. The proposed multi-level similarity method is capable of computing the level-specific similarity and optimal cross-level complementary similarity. The effectiveness of the proposed similarity measure method is evaluated on a dataset of 511 lung CT images from clinical patients for CISLs retrieval. It can achieve about 80% precision and take only 3.6 ms for the retrieval process. The extensive comparative evaluations on the same datasets are conducted to validate the advantages on retrieval performance of our multi-level similarity measure over the single-level measure and the two-level similarity methods. The proposed method can have wide applications in radiology and decision support. Graphical abstract. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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