Name
Abstract | ||
|---|---|---|
Mammography is a not invasive diagnostic technique widely used for early cancer detection in women breast. A particularly significant clue of such disease is the presence of clusters of microcalcifications. The automatic detection of such clusters is a very difficult task because of the small size of the microcalcifications and of the poor quality of the digital mammograms. In literature, all the proposed method for the automatic detection focus on the single microcalcification. In this paper, an approach that moves the final decision on the regions identified by the segmentation in the phase of clustering is proposed. To this aim, the output of a classifier on the single microcalcifications is used as input data in different clustering algorithms which produce the final decision. The approach has been successfully tested on a standard database of 40 mammographic images, publicly available. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11553595_108 | ICIAP |
Keywords | Field | DocType |
different clustering algorithm,automatic detection,digital mammograms,final decision,automatic detection focus,single microcalcification,single microcalcifications,difficult task,early cancer detection | Computer vision,Mammography,Cluster (physics),Pattern recognition,Microcalcification,Computer science,Segmentation,Ground truth,Artificial intelligence,Classifier (linguistics),Sequential algorithm,Cluster analysis | Conference |
Volume | ISSN | ISBN |
3617 | 0302-9743 | 3-540-28869-4 |
Citations | PageRank | References |
6 | 0.59 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Claudio Marrocco | 1 | 84 | 17.53 |
| Mario Molinara | 2 | 91 | 18.19 |
| Francesco Tortorella | 3 | 370 | 43.39 |