Name
Abstract | ||
|---|---|---|
Although screening mammography is widely used for the detection of breast tumors, it is difficult for a radiologist to interpret correctly a mammogram. It is possible to improve this task by using a computer aided diagnosis system (CAD) which highlights the areas most likely to contain cancer cells. In this paper, we present and compare five different feature extraction methods for breast cancer detection in digitized mammograms. All the methods are based on features extracted from a local window and on a k-nearest neighbor classifier with fast search. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11492542_61 | IbPRIA (2) |
Keywords | Field | DocType |
breast tumor,fast search,local window,different feature extraction method,digitized mammograms,k-nearest neighbor classifier,cancer cell,diagnosis system,breast cancer detection,feature extraction,k nearest neighbor | Digital mammography,Mammography,Feature vector,Pattern recognition,Breast cancer,Computer science,Computer-aided diagnosis,Feature extraction,Artificial intelligence,Classifier (linguistics),Cancer | Conference |
Volume | ISSN | ISBN |
3523 | 0302-9743 | 3-540-26154-0 |
Citations | PageRank | References |
2 | 0.41 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rafael Llobet | 1 | 72 | 8.78 |
| Roberto Paredes | 2 | 248 | 15.66 |
| Juan C. Pérez-Cortés | 3 | 137 | 16.20 |