Rapid breast density analysis of partial volumes of automated breast ultrasound images

Woo Kyung Moon, Chung Ming Lo, Jung Min Chang, Min Sun Bae, Won Hwa Kim, Chiun Sheng Huang, Jeon Hor Chen, Ming Hong Kuo, Ruey Feng Chang

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


Rapid volume density analysis (RVDA) for automated breast ultrasound (ABUS) has been proposed as a more efficient method for estimating breast density. In the current experiment, ABUS images were obtained for 67 breasts from 40 patients. For each case, three rectangular volumes of interest (VOIs) were extracted, including the VOIs located at the 6 and 12 o'clock positions relative to the nipple in the anterior to posterior pass and the lateral position relative to the nipple in the lateral pass. The centers of these VOIs were defined to align with the center of nipple, and the depths reached the retromammary fat boundary. The fuzzy c-means classifier was applied to differentiate the fibroglandular and fat tissues to estimate the density. The classification results of the three VOIs were averaged to obtain the breast density. The density correlations between the RVDA and the ABUS methods were 0.98 and 0.96 using Pearson's correlation and linear regression coefficients, respectively. The average computation times for RVDA and ABUS were 4.2 and 17.8 seconds, respectively, using an Intel® Core™2 2.66 GHz computer with 3.25 GB memory. In conclusion, the RVDA method offers a quantitative and efficient breast density estimation for ABUS.

Original languageEnglish
Pages (from-to)333-343
Number of pages11
JournalUltrasonic Imaging
Issue number4
Publication statusPublished - Oct 2013
Externally publishedYes


  • ABUS
  • breast density
  • fuzzy c-means

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging


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