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

研究成果: 雜誌貢獻文章同行評審

51 引文 斯高帕斯(Scopus)

摘要

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.
原文英語
文章編號6782644
頁(從 - 到)1503-1511
頁數9
期刊IEEE Transactions on Medical Imaging
33
發行號7
DOIs
出版狀態已發佈 - 2014
對外發佈Yes

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Radiological and Ultrasound Technology
  • Software
  • Medicine(all)

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