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Creating New Medical Ontologies for Image Annotation A Case Study /
Creating New Medical Ontologies for Image Annotation focuses on the problem of the medical images automatic annotation process, which is solved in an original manner by the authors. All the steps of this process are described in detail with algorithms, experiments and results. The original algorithm...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Springer New York : Imprint: Springer,
2012.
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| Edición: | 1st ed. 2012. |
| Colección: | SpringerBriefs in Electrical and Computer Engineering,
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| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 001 | 978-1-4614-1909-9 | ||
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| 100 | 1 | |a Stanescu, Liana. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Creating New Medical Ontologies for Image Annotation |h [electronic resource] : |b A Case Study / |c by Liana Stanescu, Dumitru Dan Burdescu, Marius Brezovan, Cristian Gabriel Mihai. |
| 250 | |a 1st ed. 2012. | ||
| 264 | 1 | |a New York, NY : |b Springer New York : |b Imprint: Springer, |c 2012. | |
| 300 | |a VIII, 111 p. 27 illus., 10 illus. in color. |b online resource. | ||
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| 490 | 1 | |a SpringerBriefs in Electrical and Computer Engineering, |x 2191-8120 | |
| 505 | 0 | |a Content Based Image Retrieval in Medical Images Databases -- Medical Images Segmentation -- Ontologies -- Medical Images Annotation -- Semantic Based Image Retrieval -- Object Oriented Medical Annotation System. | |
| 520 | |a Creating New Medical Ontologies for Image Annotation focuses on the problem of the medical images automatic annotation process, which is solved in an original manner by the authors. All the steps of this process are described in detail with algorithms, experiments and results. The original algorithms proposed by authors are compared with other efficient similar algorithms. In addition, the authors treat the problem of creating ontologies in an automatic way, starting from Medical Subject Headings (MESH). They have presented some efficient and relevant annotation models and also the basics of the annotation model used by the proposed system: Cross Media Relevance Models. Based on a text query the system will retrieve the images that contain objects described by the keywords. | ||
| 650 | 0 | |a Signal processing. | |
| 650 | 0 | |a Radiology. | |
| 650 | 0 | |a Computer vision. | |
| 650 | 0 | |a Algorithms. | |
| 650 | 1 | 4 | |a Signal, Speech and Image Processing . |
| 650 | 2 | 4 | |a Radiology. |
| 650 | 2 | 4 | |a Computer Vision. |
| 650 | 2 | 4 | |a Algorithms. |
| 700 | 1 | |a Burdescu, Dumitru Dan. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 700 | 1 | |a Brezovan, Marius. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 700 | 1 | |a Mihai, Cristian Gabriel. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
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| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9781461419105 |
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| 830 | 0 | |a SpringerBriefs in Electrical and Computer Engineering, |x 2191-8120 | |
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| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||