Tumor detection in automated breast ultrasound images using quantitative tissue clustering

Woo Kyung Moon, Chung Ming Lo, Rong Tai Chen, Yi Wei Shen, Jung Min Chang, Chiun Sheng Huang, Jeon Hor Chen, Wei Wen Hsu, Ruey Feng Chang

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)


Purpose: A computer-aided detection (CADe) system based on quantitative tissue clustering algorithm was proposed to identify potential tumors in automated breast ultrasound (ABUS) images. Methods: Our three-dimensional (3D) ABUS images database included 148 biopsy-verified lesions (size 0.4-7.9 cm; mean 1.76 cm). An ABUS volume was comprised of 229-282 slices of two-dimensional (2D) images. For tumor detection, the fast 3D mean shift method was used to remove the speckle noise and the segment tissues with similar properties. The hypoechogenic regions, i.e., the tumor candidates, were extracted using fuzzy c-means clustering. Seven features related to echogenicity and morphology were quantified and used to predict the likelihood of identifying a tumor and filtering out the false-positive (FP) regions. Results: The sensitivity of the proposed CADe system achieved 89.19% (132/148) with 2.00 FPs per volume. For the volumes without lesion, the FP rate was 1.27. The sensitivity was 92.50% (74/80) for malignant tumors and 85.29% (58/68) for benign tumors. Conclusions: The proposed CADe system provides an automatic and quantitative procedure for tumor detection in ABUS images. Further studies are needed to reduce the FP rate of the CADe algorithm.

Original languageEnglish
Article number042901
JournalMedical Physics
Issue number4
Publication statusPublished - 2014
Externally publishedYes


  • automated breast ultrasound
  • breast cancer
  • clustering
  • computer-aided detection

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging
  • Medicine(all)


Dive into the research topics of 'Tumor detection in automated breast ultrasound images using quantitative tissue clustering'. Together they form a unique fingerprint.

Cite this