dc.contributor.author | Ertas, G | |
dc.contributor.author | Doran, SJ | |
dc.contributor.author | Leach, MO | |
dc.date.accessioned | 2016-11-23T10:54:18Z | |
dc.date.issued | 2017-01-01 | |
dc.identifier.citation | Medical & biological engineering & computing, 2017, 55 (1), pp. 57 - 68 | |
dc.identifier.issn | 0140-0118 | |
dc.identifier.uri | https://repository.icr.ac.uk/handle/internal/232 | |
dc.identifier.eissn | 1741-0444 | |
dc.identifier.doi | 10.1007/s11517-016-1484-y | |
dc.description.abstract | 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). | |
dc.format | Print-Electronic | |
dc.format.extent | 57 - 68 | |
dc.language | eng | |
dc.language.iso | eng | |
dc.publisher | SPRINGER HEIDELBERG | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0 | |
dc.subject | Breast | |
dc.subject | Humans | |
dc.subject | Breast Neoplasms | |
dc.subject | Magnetic Resonance Imaging | |
dc.subject | Algorithms | |
dc.subject | Image Processing, Computer-Assisted | |
dc.subject | Female | |
dc.title | A computerized volumetric segmentation method applicable to multi-centre MRI data to support computer-aided breast tissue analysis, density assessment and lesion localization. | |
dc.type | Journal Article | |
dcterms.dateAccepted | 2016-03-04 | |
rioxxterms.versionofrecord | 10.1007/s11517-016-1484-y | |
rioxxterms.licenseref.uri | https://creativecommons.org/licenses/by/4.0 | |
rioxxterms.licenseref.startdate | 2017-01 | |
rioxxterms.type | Journal Article/Review | |
dc.relation.isPartOf | Medical & biological engineering & computing | |
pubs.issue | 1 | |
pubs.notes | No embargo | |
pubs.organisational-group | /ICR | |
pubs.organisational-group | /ICR/Primary Group | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Magnetic Resonance | |
pubs.organisational-group | /ICR | |
pubs.organisational-group | /ICR/Primary Group | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Magnetic Resonance | |
pubs.publication-status | Published | |
pubs.volume | 55 | |
pubs.embargo.terms | No embargo | |
icr.researchteam | Magnetic Resonance | |
dc.contributor.icrauthor | Doran, Simon | |
dc.contributor.icrauthor | Leach, Martin | |