Name
Abstract | ||
|---|---|---|
The high incidence of breast cancer has increased significantly in the recent years. The most familiar breast tumors types are mass and microcalcifications (Mcs). Mammogram is considered the most reliable method in early detection of breast cancer. Computer-aided diagnosis system can be very helpful for radiologist in detection and diagnosing abnormalities earlier and faster than traditional screening programs. Several techniques can be used to accomplish this task. In this work, the authors present a preprocessing method, based on homomorphic filtering and wavelet, to extract the abnormal Mcs in mammographic images. The authors use four different methods of feature extraction for classification of normal and abnormal patterns in mammogram. Four different feature extraction methods are used here are Wavelet, Gist, Gabor and Tamura. A classification system based on neural network and nearest neighbor classification is used. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.4018/ijcvip.2014010101 | International Journal of Computer Vision and Image Processing |
Keywords | Field | DocType |
mammography,neural networks,homomorphic filtering,microcalcifications,wavelet,nearest neighborhood classifier | Breast cancer,Computer science,Artificial intelligence,Artificial neural network,Homomorphic filtering,Wavelet,k-nearest neighbors algorithm,Computer vision,Mammography,Pattern recognition,Feature extraction,Preprocessor,Machine learning | Journal |
Volume | Issue | Citations |
4 | 1 | 0 |
PageRank | References | Authors |
0.34 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| K. Taifi | 1 | 2 | 1.04 |
| S. Safi | 2 | 21 | 1.19 |
| M. Fakir | 3 | 5 | 4.14 |
| A. Elbalaoui | 4 | 0 | 0.34 |