|Title:||Tumor detection in automated breast ultrasound images using quantitative tissue clustering||Authors:||Moon, W.K.
|Issue Date:||2014||Journal Volume:||41||Journal Issue:||4||Source:||Medical Physics||Abstract:||
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. ? 2014 American Association of Physicists in Medicine.
|DOI:||10.1118/1.4869264||metadata.dc.subject.other:||Automation; Medical imaging; Tumors; Ultrasonic applications; Breast Cancer; Breast ultrasound; Breast ultrasound images; clustering; Computer aided detection; Fuzzy C means clustering; Threedimensional (3-d); Two dimensional (2D) image; Clustering algorithms; adult; aged; article; automated breast ultrasound; breast cancer; breast tumor; cancer morphology; classification algorithm; computer assisted diagnosis; echomammography; echomammography device; false positive result; female; fuzzy system; human; laboratory automation; laboratory diagnosis; major clinical study; middle aged; noise reduction; quantitative tissue clustering algorithm; retrospective study; sensitivity and specificity; three dimensional imaging; tumor biopsy; tumor diagnosis; tumor volume; young adult; algorithm; automation; breast; breast tumor; cluster analysis; computer assisted diagnosis; echography; echomammography; pathology; procedures; receiver operating characteristic; Adult; Aged; Algorithms; Automation; Breast; Breast Neoplasms; Cluster Analysis; False Positive Reactions; Female; Humans; Image Interpretation, Computer-Assisted; Middle Aged; Retrospective Studies; ROC Curve; Ultrasonography, Mammary; Young Adult
|Appears in Collections:||醫學系|
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