NDLI: Quantification of breast tissue index from MR data using fuzzy clustering

Content Provider	IEEE Xplore Digital Library
Author	Klifa, C. Carballido-Gamio, J. Wilmes, L. Laprie, A. Lobo, C. DeMicco, E. Watkins, M. Shepherd, J. Gibbs, J. Hylton, N.
Copyright Year	2004
Description	Author affiliation: Dept. of Radiol., California Univ., San Francisco, CA, USA (Klifa, C.; Carballido-Gamio, J.; Wilmes, L.; Laprie, A.; Lobo, C.; DeMicco, E.; Watkins, M.; Shepherd, J.; Gibbs, J.; Hylton, N.)
Abstract	The study objective was to develop a segmentation technique to quantify breast tissue and total breast volume from magnetic resonance imaging (MRI) data to obtain a breast tissue index (BTI) related to breast density. Our goal is to quantify MR breast density to improve breast cancer risk assessment for certain high-risk populations for whom mammography is of limited usefulness due to high breast density. A semi-automatic 3D segmentation technique was implemented based on a fuzzy c-means technique (FCM) to segment fibroglandular tissue from fat in the breast images. After validation on a phantom, our FCM technique was first used to test the breast tissue measures reproducibility in two consecutive MR examinations of the same patients. The technique was then applied to measure the BTI on 10 high-risk patients. Results of BTI obtained with the semi-automated FCM method were compared with BTI results for the same patients using two other techniques, manual delineation and global threshold. BTI measures correlated well with mammographic densities (Pearson coefficients r = 0.78 using MR manual delineation, and r = 0.75 using MR FCM). The breast tissue index could therefore become a common measure for future studies of using noncontrast MRI data.
Starting Page	1667
Ending Page	1670
File Size	272981
Page Count	4
File Format	PDF
ISBN	0780384393
DOI	10.1109/IEMBS.2004.1403503
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-09-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biomedical image processing Image segmentation Fuzzy sets cancer risk Breast density fuzzy clustering MR segmentation
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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