Name
Abstract | ||
|---|---|---|
Texture analysis plays an important role in many image processing tasks. In this work, we present a texture descriptor based on the topology of excursion sets, derived from the concept of Minkowski functionals, and evaluate their usefulness in the detection of breast masses in 2D breast ultrasound images. The application includes three major stages: preprocessing, including candidate generation through computation of gradient concentration under a Fisher-Tippet noise model (in itself another contribution of the paper); texture feature extraction; and region classification using a Random Forests classifier. Performance of the proposed method is evaluated on 135 2D BUS images with 139 masses. Our method reaches 91% sensitivity with an averaged 1.19 false detections, and the proposed texture feature compares favorably against the often-used grey level co-occurrence matrices on the exact the same task. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ISBI.2014.6867963 | ISBI |
Keywords | Field | DocType |
CAD, Breast Cancer, Texture | Breast ultrasound,Computer vision,Pattern recognition,Computer science,Artificial intelligence | Conference |
ISSN | Citations | PageRank |
1945-7928 | 1 | 0.43 |
References | Authors | |
5 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Zhao | 1 | 1 | 0.43 |
| Xiaoxing Li | 2 | 1 | 0.43 |
| Soma Biswas | 3 | 409 | 28.08 |
| Rakesh Mullick | 4 | 71 | 14.86 |
| Paulo R. S. Mendonça | 5 | 610 | 50.38 |
| Vivek Vaidya | 6 | 1 | 0.43 |