Name
Abstract | ||
|---|---|---|
Linear structures are a major source of false positives (FPs) in computer-aided detection of clustered microcalcifications (MCs) in mammograms. In this work, we investigate whether it is feasible to improve the performance in MC detection by directly exploiting the FPs associated with linear structures. We analyze the cause of FPs by linear structures and their characteristics with an SVM detector, and design a linear structure detection procedure together with a dual-thresholding scheme to separate the linear structures from other tissue background in a mammogram. The proposed procedure was demonstrated on a set of 200 mammograms containing clustered MCs. The results show that it could effectively reduce the FPs in the SVM detector by as much as 30% with the true detection rate at 85%. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICIP.2013.6738294 | ICIP |
Keywords | Field | DocType |
mammography,clustered microcalcification,computer-aided detection,linear structures,computer-aided diagnosis (cad),linear structure detection procedure,dual-thresholding scheme,object detection,svm detector,mammogram,mc detection,false positive detection,linear structure detection,biological tissues,patient diagnosis,support vector machines,tissue background,medical image processing,false positive reduction | Mammography,Object detection,Computer vision,Pattern recognition,Computer science,Support vector machine,Linear complex structure,Artificial intelligence,Detector,False positive paradox | Conference |
ISSN | Citations | PageRank |
1522-4880 | 4 | 0.44 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan Wang | 1 | 109 | 27.00 |
| Yongyi Yang | 2 | 1409 | 140.74 |
| Robert M Nishikawa | 3 | 599 | 58.25 |