Name
Abstract | ||
|---|---|---|
Boundary delineation is the fundamental basis of many sonographic image analyses. In sonographic breast lesion images, it's complicated and time consuming for physicians to delineate the lesion boundaries. When it comes to three dimensional sonographic breast lesions image, delineation of lesion boundary becomes much more complicated. Taking advantage of cell competition algorithm along with its good region structure, generated cells can be served as elegant nodes for graph cut. Further, a similar weight function plays an important role in the estimation of lesion boundary to avoid visible weak edge and isolated node in graph cut. The integration of cell competition and graph cut can be intuitively implemented in three dimensional images, in addition to the reduction of computational time. With efficiency and accuracy of lesion detection, a computer aided system was therefore developed to fulfill clinical applications. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ISBI.2010.5490384 | ISBI |
Keywords | Field | DocType |
mammography,computational time,cellular biophysics,three dimensional images,sonographic image analysis,sonographic breast lesion image,image segmentation,lesion boundary,biomedical ultrasonics,breast,2d/3d sonography,cell competition algorithm,cell-based graph cut,graph cut,boundary delineation,cell competition,dimensional sonographic breast lesion,lesion detection,sonographic image,biological organs,sonographic breast image,medical image processing,pixel,cost function,sonogram,three dimensional,ultrasonography,weight function,speckle | Cut,Computer vision,Mammography,Weight function,Lesion,Pattern recognition,Computer-aided,Segmentation,Computer science,Image segmentation,Artificial intelligence,Pixel | Conference |
ISSN | ISBN | Citations |
1945-7928 E-ISBN : 978-1-4244-4126-6 | 978-1-4244-4126-6 | 1 |
PageRank | References | Authors |
0.34 | 3 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hsin-Hung Chiang | 1 | 1 | 0.68 |
| Jie-Zhi Cheng | 2 | 102 | 13.00 |
| Pei-Kai Hung | 3 | 5 | 0.76 |
| Chun-You Liu | 4 | 24 | 1.34 |
| Cheng-Hong Chung | 5 | 1 | 0.34 |
| Chung-Ming Chen | 6 | 176 | 16.17 |