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Med Phys. 2014 Apr;41(4):042901. doi: 10.1118/1.4869264.

Tumor detection in automated breast ultrasound images using quantitative tissue clustering.

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Department of Radiology, Seoul National University Hospital, Seoul 110-744, South Korea.
Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan.
Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 10617, Taiwan.
Center for Functional Onco-Imaging and Department of Radiological Science University of California Irvine, California 92697 and Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung 82445, Taiwan.
Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan and Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan.



A computer-aided detection (CADe) system based on quantitative tissue clustering algorithm was proposed to identify potential tumors in automated breast ultrasound (ABUS) images.


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.


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.


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.

[Indexed for MEDLINE]

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