Multi-dimensional tumor detection in automated whole breast ultrasound using topographic watershed.
Automated whole breast ultrasound (ABUS) is becoming a popular screening modality for whole breast examination. Compared to conventional handheld ultrasound, ABUS achieves operator-independent and is feasible for mass screening. However, reviewing hundreds of slices in an ABUS image volume is time-consuming. A computer-aided detection (CADe) system based on watershed transform was proposed in this study to accelerate the reviewing. The watershed transform was applied to gather similar tissues around local minima to be homogeneous regions. The likelihoods of being tumors of the regions were estimated using the quantitative morphology, intensity, and texture features in the 2-D/3-D false positive reduction (FPR). The collected database comprised 68 benign and 65 malignant tumors. As a result, the proposed system achieved sensitivities of 100% (133/133), 90% (121/133), and 80% (107/133) with FPs/pass of 9.44, 5.42, and 3.33, respectively. The figure of merit of the combination of three feature sets is 0.46 which is significantly better than that of other feature sets ( [Formula: see text]). In summary, the proposed CADe system based on the multi-dimensional FPR using the integrated feature set is promising in detecting tumors in ABUS images.
Chung-Ming
CM
Lo
Rong-Tai
RT
Chen
Yeun-Chung
YC
Chang
Ya-Wen
YW
Yang
Ming-Jen
MJ
Hung
Chiun-Sheng
CS
Huang
Ruey-Feng
RF
Chang
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008875:Middle Aged; D012372:ROC Curve; D016217:Ultrasonography, Mammary / Q000379:methods*; D055815:Young Adult