Name
Abstract | ||
|---|---|---|
This paper proposes a texture feature which is applied on human breast Optical Coherence Microscopy (OCM) images to classify different types of breast tissues. Inspired by local binary pattern (LBP) texture features, a new variant of LBP feature, block based LBP (BLBP) is proposed. Instead of representing intensity differences between neighbors and a center pixel, BLBP feature extracts the intensity differences among certain blocks of the neighborhood around a pixel. Two different ways are proposed to organize the blocks: the spokes and the rings. By integrating spoke BLBP with ring BLBP features, very high classification accuracy is achieved using a neural network classifier. In one of our experiments which classifies 4310 OCM images into five tissue types, the classification accuracy increased from 81.7% to 92.4% when new features are used instead of the traditional LBP feature. In another experiment which classifies 46 large field OCM images as either benign or containing tumor, a classification accuracy of 91.3% is reached by using multi-scale BLBP features. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ISBI.2015.7163848 | 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI) |
Keywords | Field | DocType |
Texture feature,Local Binary Pattern (LBP),Optical Coherence Microscopy (OCM),Tissue classification,Tumor detection | Computer vision,Neural network classifier,Pattern recognition,Optical coherence microscopy,Computer science,Local binary patterns,Coherence (physics),Feature extraction,Artificial intelligence,Pixel,Optical imaging | Conference |
ISSN | Citations | PageRank |
1945-7928 | 0 | 0.34 |
References | Authors | |
7 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sunhua Wan | 1 | 7 | 1.15 |
| Xiaolei Huang | 2 | 1084 | 63.94 |
| Hsiang-Chieh Lee | 3 | 7 | 1.48 |
| James G. Fujimoto | 4 | 12 | 6.61 |
| Chao Zhou | 5 | 1 | 1.71 |