Name
Abstract | ||
|---|---|---|
The lack of content-based retrieval models capable to provide fast and relevant results is one of the reasons why CBMIR tools have not yet became present in radiological routines. This work presents a methodology for CBMIR in order to help solve this deficiency. Tests performed have shown a reduction of 95.5% of the execution time for retrieval processes. The relevance of the results is assured by the categorization of the cases in the Case Base. The class of cases is defined through pseudo-semantics and visual attributes. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/CBMS.2007.58 | CBMS |
Keywords | Field | DocType |
testing,data mining,information retrieval,dicom,image retrieval,radiology,biomedical imaging,feature extraction,information analysis | Computer vision,Categorization,Automatic image annotation,Human–computer information retrieval,Information retrieval,Data retrieval,Computer science,Image retrieval,Case base,Execution time,Artificial intelligence,Visual Word | Conference |
ISSN | ISBN | Citations |
2372-9198 | 0-7695-2905-4 | 0 |
PageRank | References | Authors |
0.34 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonio da Luz | 1 | 6 | 1.45 |
| D. D, Abdala | 2 | 28 | 3.45 |
| Aldo von Wangenheim | 3 | 209 | 49.44 |
| Eros Comunello | 4 | 66 | 15.04 |