Name
Abstract | ||
|---|---|---|
Edge is one of the most important characteristics of microcalcifications, edge detection of microcalcification clusters has a great significance in computer-aided diagnosis system for the automatic detection of clustered microcalcifications in digitized mammograms. A lot of algorithms have been suggested for extracting medical image edges, however, few of them are well suited for edge extraction of microcalcifications due to obtaining discontinuous edges, or continuous edges with more over-detection points. In this paper, we propose a new method for clustered microcalcifications edge detection by integrating kirsch edge operator, edge linking with Markov model. First, initial edges are extracted by employing kirsch edge operator. Then, we thin the initial edges and fill many gaps in the edge image using edge linking technique. Finally, closed boundaries of microcalcifications are obtained based on Markov model. The experiments demonstrate that our algorithm can obtain closed boundaries with less over-detection points. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/BMEI.2009.5305811 | BMEI |
Keywords | Field | DocType |
mammography,kirsch edge operator,markov model,computer-aided diagnosis system,edge detection method,digitized mammograms,edge detection,mammogram,medical diagnostic computing,markov processes,microcalfication clusters,clustering algorithms,noise | Cluster (physics),Computer vision,Mammography,Markov process,Pattern recognition,Markov model,Computer science,Edge detection,Artificial intelligence,Cluster analysis | Conference |
ISSN | ISBN | Citations |
1948-2914 | 978-1-4244-4134-1 | 0 |
PageRank | References | Authors |
0.34 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Guang Zhang | 1 | 0 | 1.35 |
| Wen Lu | 2 | 0 | 0.34 |
| Fu Yun Cheng | 3 | 0 | 0.34 |
| Li Song | 4 | 0 | 0.34 |