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

研究成果: 雜誌貢獻文章同行評審

50 引文 斯高帕斯(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.
期刊Medical Physics
出版狀態已發佈 - 4月 2014

ASJC Scopus subject areas

  • 生物物理學
  • 放射學、核子醫學和影像學


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