Name
Abstract | ||
|---|---|---|
Microcalcification (MC) clusters in mammograms can be an indicator of breast cancer. We propose, for the first time, the use of support vector machine (SVM) learning for automated detection of MCs in digitized mammograms. In the proposed framework, MC detection is formulated as a supervised-learning problem and the method of SVM is employed to develop the detection algorithm. The proposed method is developed and evaluated using a database of 76 mammograms containing 1120 MCs. To evaluate detection performance, free-response receiver operating characteristic (FROC) curves are used. Experimental results demonstrate that, when compared to several other existing methods, the proposed SVM framework offers the best performance. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/ICIP.2002.1040110 | Image Processing. 2002. Proceedings. 2002 International Conference |
Keywords | Field | DocType |
cancer,learning (artificial intelligence),learning automata,mammography,medical image processing,object detection,pattern classification,SVM learning,digitized mammograms,free-response receiver operating characteristic curves,microcalcification cluster detection,microcalcification detection,pattern classification,supervised learning,support vector machine learning | Structured support vector machine,Computer vision,Object detection,Mammography,Learning automata,Receiver operating characteristic,Microcalcification,Pattern recognition,Computer science,Support vector machine,Artificial intelligence | Conference |
Volume | ISSN | Citations |
2 | 1522-4880 | 5 |
PageRank | References | Authors |
0.53 | 9 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Issam El-Naqa | 1 | 528 | 36.31 |
| Yongyi Yang | 2 | 1409 | 140.74 |
| Miles N. Wernick | 3 | 595 | 61.13 |
| Nikolas P. Galatsanos | 4 | 632 | 52.16 |
| Robert M Nishikawa | 5 | 599 | 58.25 |
| El-Naqa, I. | 6 | 5 | 0.53 |