Content-Based Retrieval of Focal Liver Lesions Using Bag-of-Visual-Words Representations of Single- and Multiphase Contrast-Enhanced CT Images.
This paper is aimed at developing and evaluating a content-based retrieval method for contrast-enhanced liver computed tomographic (CT) images using bag-of-visual-words (BoW) representations of single and multiple phases. The BoW histograms are extracted using the raw intensity as local patch descri...
| Publicado en: | Journal of Digital Imaging Vol. 25; no. 6; pp. 708 - 720 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2012
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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=104432750&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104432750 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2012 vid: 25 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104432750 83184794 10.1007/s10278-012-9495-1 NLM22692772 104432750 ppf: 708 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Content-Based Retrieval of Focal Liver Lesions Using Bag-of-Visual-Words Representations of Single- and Multiphase Contrast-Enhanced CT Images. aug: au: Yang, Wei Lu, Zhentai Yu, Mei Huang, Meiyan Feng, Qianjin Chen, Wufan affil: School of Biomedical Engineering, Southern Medical University, Guangzhou 510515 China sug: subj: Image Retrieval Methods Tomography, X-Ray Computed Liver Radiography Vocabulary, Controlled Image Retrieval Evaluation Systems Design Liver Diseases Diagnosis Algorithms Evaluation Research Factor Analysis Chi Square Test Human Funding Source ab: This paper is aimed at developing and evaluating a content-based retrieval method for contrast-enhanced liver computed tomographic (CT) images using bag-of-visual-words (BoW) representations of single and multiple phases. The BoW histograms are extracted using the raw intensity as local patch descriptor for each enhance phase by densely sampling the image patches within the liver lesion regions. The distance metric learning algorithms are employed to obtain the semantic similarity on the Hellinger kernel feature map of the BoW histograms. The different visual vocabularies for BoW and learned distance metrics are evaluated in a contrast-enhanced CT image dataset comprised of 189 patients with three types of focal liver lesions, including 87 hepatomas, 62 cysts, and 60 hemangiomas. For each single enhance phase, the mean of average precision (mAP) of BoW representations for retrieval can reach above 90 % which is significantly higher than that of intensity histogram and Gabor filters. Furthermore, the combined BoW representations of the three enhance phases can improve mAP to 94.5 %. These preliminary results demonstrate that the BoW representation is effective and feasible for retrieval of liver lesions in contrast-enhanced CT images. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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