Automated segmentation of breast fat-water MR images using empirical analysis

Jose A. Rosado-Toro, Tomoe Barr, Jean Philippe Galons, Marilyn T. Marron, Alison Stopeck, Cynthia Thomson, Maria I. Altbach, Jeffrey J. Rodriguez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Breast density (BD) has been advocated as a risk factor for the development of breast cancer. BD is typically measured from mammograms. However for longitudinal studies of patients at risk, BD can be better assessed using MRI due to the lack of ionizing radiation and the 3D capabilities of the technique. A fat-water (FW) imaging technique called RAD-GRASE was developed to acquire images of the entire breast in a few minutes and can generate fat-fraction maps, which can be used to assess BD. The time consuming manual segmentation on ∼19 slices per exam can be challenging. In this paper, we present a method to automatically segment the breast tissue in FW images and yield FW profiles of the region of interest (ROIs).

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages1018-1022
Number of pages5
DOIs
StatePublished - Oct 18 2013
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: May 26 2013May 31 2013

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
CountryCanada
CityVancouver, BC
Period5/26/135/31/13

Keywords

  • automated segmentation
  • breast MRI
  • dynamic programming
  • fat-water MRI
  • k-means++

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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