A computerized volumetric segmentation method applicable to multi-centre MRI data to support computer-aided breast tissue analysis, density assessment and lesion localization.
Density assessment and lesion localization in breast MRI require accurate segmentation of breast tissues. A fast, computerized algorithm for volumetric breast segmentation, suitable for multi-centre data, has been developed, employing 3D bias-corrected fuzzy c-means clustering and morphological oper...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 55; no. 1; pp. 57 - 69 |
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| Autores principales: | , , , , |
| Formato: | algorithm diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jan2017
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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=120629457&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 120629457 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jan2017 vid: 55 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 120629457 120629457 NLM27106750 120629457 10.1007/s11517-016-1484-y NLM27106750 120629457 ppf: 57 ppct: 12 formats: fmt: @attributes: type: P tig: atl: A computerized volumetric segmentation method applicable to multi-centre MRI data to support computer-aided breast tissue analysis, density assessment and lesion localization. aug: au: Ertas, Gokhan Doran, Simon Leach, Martin Doran, Simon J Leach, Martin O affil: Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging , The Institute of Cancer Research , 123 Old Brompton Road London SW7 3RP UK sug: subj: Magnetic Resonance Imaging Methods Image Processing, Computer Assisted Breast Neoplasms Breast Pathology Breast Neoplasms Diagnosis Female Algorithms Human Female ab: Density assessment and lesion localization in breast MRI require accurate segmentation of breast tissues. A fast, computerized algorithm for volumetric breast segmentation, suitable for multi-centre data, has been developed, employing 3D bias-corrected fuzzy c-means clustering and morphological operations. The full breast extent is determined on T1-weighted images without prior information concerning breast anatomy. Left and right breasts are identified separately using automatic detection of the midsternum. Statistical analysis of breast volumes from eighty-two women scanned in a UK multi-centre study of MRI screening shows that the segmentation algorithm performs well when compared with manually corrected segmentation, with high relative overlap (RO), high true-positive volume fraction (TPVF) and low false-positive volume fraction (FPVF), and has an overall performance of RO 0.94 ± 0.05, TPVF 0.97 ± 0.03 and FPVF 0.04 ± 0.06, respectively (training: 0.93 ± 0.05, 0.97 ± 0.03 and 0.04 ± 0.06; test: 0.94 ± 0.05, 0.98 ± 0.02 and 0.05 ± 0.07). 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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