Multi-dimensional tumor detection in automated whole breast ultrasound using topographic watershed

Chung Ming Lo, Rong Tai Chen, Yeun Chung Chang, Ya-Wen Yang, Ming Jen Hung, Chiun Sheng Huang, Ruey Feng Chang

Research output: Contribution to journalArticlepeer-review

76 Citations (Scopus)


Automated whole breast ultrasound (ABUS) is becoming a popular screening modality for whole breast examination. Compared to conventional handheld ultrasound, ABUS achieves operator-independent and is feasible for mass screening. However, reviewing hundreds of slices in an ABUS image volume is time-consuming. A computer-aided detection (CADe) system based on watershed transform was proposed in this study to accelerate the reviewing. The watershed transform was applied to gather similar tissues around local minima to be homogeneous regions. The likelihoods of being tumors of the regions were estimated using the quantitative morphology, intensity, and texture features in the 2-D/3-D false positive reduction (FPR). The collected database comprised 68 benign and 65 malignant tumors. As a result, the proposed system achieved sensitivities of 100% (133/133), 90% (121/133), and 80% (107/133) with FPs/pass of 9.44, 5.42, and 3.33, respectively. The figure of merit of the combination of three feature sets is 0.46 which is significantly better than that of other feature sets (p-value <0.05). In summary, the proposed CADe system based on the multi-dimensional FPR using the integrated feature set is promising in detecting tumors in ABUS images.

Original languageEnglish
Article number6782644
Pages (from-to)1503-1511
Number of pages9
JournalIEEE Transactions on Medical Imaging
Issue number7
Publication statusPublished - Jul 2014
Externally publishedYes


  • Automated whole breast ultrasound
  • breast cancer
  • computer-aided detection
  • multi-dimensional false positive reduction
  • watershed segmentation

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Electrical and Electronic Engineering
  • Computer Science Applications


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