BRISC -- an open source pulmonary nodule image retrieval framework.
We have created a content-based image retrieval framework for computed tomography images of pulmonary nodules. When presented with a nodule image, the system retrieves images of similar nodules from a collection prepared by the Lung Image Database Consortium (LIDC). The system (1) extracts images of...
| Publicado en: | Journal of Digital Imaging Vol. 20; pp. 63 - 72 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Sep2007 Supplement
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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=105929673&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105929673 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Sep2007 Supplement vid: 20 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 105929673 105929673 2009701159 10.1007/s10278-007-9059-y NLM17701069 105929673 ppf: 63 ppct: 9 formats: fmt: @attributes: type: P tig: atl: BRISC -- an open source pulmonary nodule image retrieval framework. aug: au: Lam MO Disney T Raicu DS Furst J Channin DS affil: James Madison University, 20002 Wooded View Lane, Elkton, VA 22827, USA sug: subj: Diagnosis, Computer Assisted Image Retrieval Lung Radiography Tomography, X-Ray Computed Database Construction Evaluation Research Funding Source Software Software Design XML Human ab: We have created a content-based image retrieval framework for computed tomography images of pulmonary nodules. When presented with a nodule image, the system retrieves images of similar nodules from a collection prepared by the Lung Image Database Consortium (LIDC). The system (1) extracts images of individual nodules from the LIDC collection based on LIDC expert annotations, (2) stores the extracted data in a flat XML database, (3) calculates a set of quantitative descriptors for each nodule that provide a high-level characterization of its texture, and (4) uses various measures to determine the similarity of two nodules and perform queries on a selected query nodule. Using our framework, we compared three feature extraction methods: Haralick co-occurrence, Gabor filters, and Markov random fields. Gabor and Markov descriptors perform better at retrieving similar nodules than do Haralick co-occurrence techniques, with best retrieval precisions in excess of 88%. Because the software we have developed and the reference images are both open source and publicly available they may be incorporated into both commercial and academic imaging workstations and extended by others in their research. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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