Name
Abstract | ||
|---|---|---|
This paper proposes a texture analysis technique applied on human breast Optical Coherence Microscopy (OCM) images to classify different types of breast tissues. Local binary pattern (LBP) image features are extracted. In order to improve classification precision, a new variant of LBP feature, average LBP (ALBP) is proposed. The new LBP is integrated with the original LBP feature to improve classification precision. Our experiments show that by integrating a selected set of LBP and ALBP features, very high classification accuracy is achieved using a AdaBoost meta classifier combined with neural network weak classifiers. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ISBI.2014.6867817 | ISBI |
Keywords | Field | DocType |
neural network weak classifiers,local binary pattern image feature extraction,local binary pattern,biomedical optical imaging,image analysis,texture analysis,optical microscopy,feature extraction,image classification,ocm image texture analysis,optical coherence microscopy (ocm),biological tissues,image texture,breast tissue classification,adaboost meta classifier,human breast optical coherence microscopy images,neural nets,medical image processing,tissue classification | Computer vision,Pattern recognition,Image texture,Computer science,Artificial intelligence | Conference |
ISSN | Citations | PageRank |
1945-7928 | 2 | 0.39 |
References | Authors | |
5 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sunhua Wan | 1 | 7 | 1.15 |
| Hsiang-Chieh Lee | 2 | 7 | 1.48 |
| James G. Fujimoto | 3 | 12 | 6.61 |
| Xiaolei Huang | 4 | 1084 | 63.94 |
| Chao Zhou | 5 | 7 | 1.82 |