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• W2338331346 startingPage "57" @default.
• W2338331346 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)." @default.
• W2338331346 created "2016-06-24" @default.
• W2338331346 creator A5037269194 @default.
• W2338331346 creator A5061077059 @default.
• W2338331346 creator A5068130659 @default.
• W2338331346 date "2016-04-22" @default.
• W2338331346 modified "2023-10-18" @default.
• W2338331346 title "A computerized volumetric segmentation method applicable to multi-centre MRI data to support computer-aided breast tissue analysis, density assessment and lesion localization" @default.
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• W2338331346 doi "https://doi.org/10.1007/s11517-016-1484-y" @default.
• W2338331346 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5222930" @default.
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• W2338331346 hasPublicationYear "2016" @default.
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