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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Stanescu, Liana (Συγγραφέας), Burdescu, Dumitru Dan (Συγγραφέας), Brezovan, Marius (Συγγραφέας), Mihai, Cristian Gabriel (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: New York, NY : Springer New York : Imprint: Springer, 2012.
Σειρά:SpringerBriefs in Electrical and Computer Engineering,
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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100 1 |a Stanescu, Liana.  |e author. 
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. 
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 2191-8112 
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 Engineering. 
650 0 |a Radiology. 
650 0 |a Algorithms. 
650 0 |a Image processing. 
650 1 4 |a Engineering. 
650 2 4 |a Signal, Image and Speech Processing. 
650 2 4 |a Imaging / Radiology. 
650 2 4 |a Image Processing and Computer Vision. 
650 2 4 |a Algorithm Analysis and Problem Complexity. 
700 1 |a Burdescu, Dumitru Dan.  |e author. 
700 1 |a Brezovan, Marius.  |e author. 
700 1 |a Mihai, Cristian Gabriel.  |e author. 
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776 0 8 |i Printed edition:  |z 9781461419082 
830 0 |a SpringerBriefs in Electrical and Computer Engineering,  |x 2191-8112 
856 4 0 |u http://dx.doi.org/10.1007/978-1-4614-1909-9  |z Full Text via HEAL-Link 
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950 |a Engineering (Springer-11647)