Name
Title | ||
|---|---|---|
False positive reduction of microcalcification cluster detection in digital breast tomosynthesis |
Abstract | ||
|---|---|---|
Digital breast tomosynthesis (DBT) is a new modality that has strong potential in improving the sensitivity and specificity of breast mass detection. However, the detection of microcalcifications (MCs) in DBT is challenging because radiologists have to search for the often subtle signals in many slices. We are developing a computer-aided detection (CAD) system to assist radiologists in reading DBT. The system consists of four major steps, namely: image enhancement; pre-screening of MC candidates; false-positive (FP) reduction, and detection of MC cluster candidates of clinical interest. We propose an algorithm for reducing FPs by using 3D characteristics of MC clusters in DBT. The proposed method takes the MC candidates from the pre-screening step described in [14] as input, which are then iteratively clustered to provide training samples to a random-forest classifier and a rule-based classifier. The random-forest classifier is used to learn a discriminative model of MC clusters using 3D texture features, whereas the rule-based classifier revisits the initial training samples and enhances them by combining median filtering and graph-cut-based segmentation followed by thresholding on the final number of MCs belonging to the candidate cluster. The outputs of these two classifiers are combined according to the prediction confidence of the random-forest classifier. We evaluate the proposed FP-reduction algorithm on a data set of two-view DBT from 40 breasts with biopsy-proven MC clusters. The experimental results demonstrate a significant reduction in FP detections, with a final sensitivity of 92.2% for an FP rate of 50%. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1117/12.2043763 | Proceedings of SPIE |
Keywords | Field | DocType |
Microcalcification clusters,digital breast tomosynthesis,false positive reduction,random forest | Computer vision,Median filter,Microcalcification,Segmentation,Computer-aided diagnosis,Artificial intelligence,Thresholding,Classifier (linguistics),Random forest,Discriminative model,Physics | Conference |
Volume | ISSN | Citations |
9034 | 0277-786X | 1 |
PageRank | References | Authors |
0.47 | 5 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ning Xu | 1 | 88 | 10.99 |
| Sheng Yi | 2 | 97 | 5.89 |
| Paulo R. S. Mendonça | 3 | 610 | 50.38 |
| taipeng tian | 4 | 1 | 0.47 |
| Ravi K. Samala | 5 | 16 | 9.96 |
| Heang-Ping Chan | 6 | 408 | 93.38 |