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Tumor detection in automated breast ultrasound images using quantitative tissue clustering
Author(s) -
Moon Woo Kyung,
Lo ChungMing,
Chen RongTai,
Shen YiWei,
Chang Jung Min,
Huang ChiunSheng,
Chen JeonHor,
Hsu WeiWen,
Chang RueyFeng
Publication year - 2014
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4869264
Subject(s) - echogenicity , artificial intelligence , cluster analysis , computer science , ultrasound , speckle pattern , breast tumor , pattern recognition (psychology) , breast cancer , radiology , medicine , cancer
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.