Tumor detection in automated breast ultrasound images using quantitative tissue clustering.
OBJECTIVE
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.
Department of Radiology, Seoul National University Hospital, Seoul 110-744, South Korea.
Woo Kyung
WK
Moon
Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan.
Chung-Ming
CM
Lo
Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan.
Rong-Tai
RT
Chen
Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan.
Yi-Wei
YW
Shen
Department of Radiology, Seoul National University Hospital, Seoul 110-744, South Korea.
Jung Min
JM
Chang
Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 10617, Taiwan.
Chiun-Sheng
CS
Huang
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.
Jeon-Hor
JH
Chen
Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan.
Wei-Wen
WW
Hsu
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.
Ruey-Feng
RF
Chang
D000328:Adult; D000368:Aged; D000465:Algorithms; D001331:Automation; D001940:Breast / Q000473:pathology*; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D016000:Cluster Analysis; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008875:Middle Aged; D012372:ROC Curve; D012189:Retrospective Studies; D016217:Ultrasonography, Mammary / Q000379:methods*; D055815:Young Adult