Name
Title | ||
|---|---|---|
Breast Tumor Classification of Ultrasound Images Using Wavelet-Based Channel Energy and ImageJ |
Abstract | ||
|---|---|---|
The infiltrative nature of lesions is a significant feature that implies a malignant breast lesion in ultrasound images. Characterizing the infiltrative nature of lesions with computationally inexpensive and highly efficacious features is crucial for the realization of a computer-aided diagnosis system. In this study, the infiltrative nature of lesions is regarded as an energy that produces irregu... |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/JSTSP.2008.2011160 | IEEE Journal of Selected Topics in Signal Processing |
Keywords | Field | DocType |
Breast tumors,Ultrasonic imaging,Lesions,Discrete wavelet transforms,Sonogram,Cancer,Computer aided diagnosis,Frequency,High performance computing,Computational efficiency | Computer vision,Mammography,Receiver operating characteristic,Computer science,Edge detection,Image processing,Feature extraction,Artificial intelligence,Contextual image classification,Wavelet,Wavelet transform | Journal |
Volume | Issue | ISSN |
3 | 1 | 1932-4553 |
Citations | PageRank | References |
5 | 0.46 | 9 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hsieh-Wei Lee | 1 | 12 | 2.29 |
| Bin-da Liu | 2 | 563 | 66.56 |
| King-Chu Hung | 3 | 99 | 14.60 |
| Sheau-Fang Lei | 4 | 62 | 9.92 |
| Po-Chin Wang | 5 | 8 | 1.21 |
| Tsung-Lung Yang | 6 | 103 | 3.55 |