Name
Title | ||
|---|---|---|
Multi-dimensional tumor detection in automated whole breast ultrasound using topographic watershed. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TMI.2014.2315206 | IEEE Trans. Med. Imaging |
Keywords | Field | DocType |
mammography,computer-aided detection,multi-dimensional false positive reduction,operator independent abus,3d false positive reduction,topographic watershed,quantitative texture features,malignant tumors,biomedical ultrasonics,benign tumors,2d false positive reduction,whole breast examination,quantitative morphology features,breast cancer,quantitative intensity features,watershed transform,tumours,abus image volume,automated whole breast ultrasound,image texture,computer aided detection system,watershed segmentation,breast mass screening,medical image processing,multidimensional tumor detection,feature extraction,materials,morphology | Computer vision,Automated whole-breast ultrasound,Multi dimensional,Homogeneous,Topographic map,Watershed,Feature set,Artificial intelligence,Whole breast,Mathematics,Ultrasound | Journal |
Volume | Issue | ISSN |
33 | 7 | 1558-254X |
Citations | PageRank | References |
14 | 1.15 | 9 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chung-Ming Lo | 1 | 135 | 6.65 |
| Rong-Tai Chen | 2 | 19 | 1.56 |
| Yeun-Chung Chang | 3 | 36 | 5.49 |
| Ya-Wen Yang | 4 | 32 | 6.37 |
| Ming-Jen Hung | 5 | 14 | 1.15 |
| Chiun-Sheng Huang | 6 | 153 | 9.33 |
| Ruey-Feng Chang | 7 | 395 | 34.88 |