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Creating New Medical Ontologies for Image Annotation: A Case Study: SpringerBriefs in Electrical and Computer Engineering

Autor Liana Stanescu, Dumitru Dan Burdescu, Marius Brezovan, Cristian Gabriel Mihai
en Limba Engleză Paperback – 15 dec 2011
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.
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Specificații

ISBN-13: 9781461419082
ISBN-10: 1461419085
Pagini: 120
Ilustrații: VIII, 111 p. 27 illus., 10 illus. in color.
Dimensiuni: 155 x 235 x 6 mm
Greutate: 0.11 kg
Ediția:2012
Editura: Springer
Colecția Springer
Seria SpringerBriefs in Electrical and Computer Engineering

Locul publicării:New York, NY, United States

Public țintă

Research

Cuprins

Content Based Image Retrieval in Medical Images Databases.- Medical Images Segmentation.- Ontologies.- Medical Images Annotation.- Semantic Based Image Retrieval.- Object Oriented Medical Annotation System.

Textul de pe ultima copertă

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.

Caracteristici

Introduces a new algorithm for color images segmentation, based on a hexagonal grid, with very good results Covers a high number of experiments effectuated on a database with thousands of color medical images from digestive tract that are rarely used in medical annotation systems Annotation system uses an object-oriented model of the medical images database