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  • © 2012

Creating New Medical Ontologies for Image Annotation

A Case Study

  • 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

Part of the book series: SpringerBriefs in Electrical and Computer Engineering (BRIEFSELECTRIC)

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Table of contents (7 chapters)

  1. Front Matter

    Pages i-viii
  2. Introduction

    • Liana Stanescu, Dumitru Dan Burdescu, Marius Brezovan, Cristian Gabriel Mihai
    Pages 1-3
  3. Content-Based Image Retrieval in Medical Images Databases

    • Liana Stanescu, Dumitru Dan Burdescu, Marius Brezovan, Cristian Gabriel Mihai
    Pages 5-14
  4. Medical Images Segmentation

    • Liana Stanescu, Dumitru Dan Burdescu, Marius Brezovan, Cristian Gabriel Mihai
    Pages 15-43
  5. Ontologies

    • Liana Stanescu, Dumitru Dan Burdescu, Marius Brezovan, Cristian Gabriel Mihai
    Pages 45-64
  6. Medical Images Annotation

    • Liana Stanescu, Dumitru Dan Burdescu, Marius Brezovan, Cristian Gabriel Mihai
    Pages 65-89
  7. Semantic-Based Image Retrieval

    • Liana Stanescu, Dumitru Dan Burdescu, Marius Brezovan, Cristian Gabriel Mihai
    Pages 91-102
  8. Object Oriented Medical Annotation System

    • Liana Stanescu, Dumitru Dan Burdescu, Marius Brezovan, Cristian Gabriel Mihai
    Pages 103-111

About this book

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.

Authors and Affiliations

  • , Software Engineering Dept., University of Craiova, Craiova, Romania

    Liana Stanescu, Dumitru Dan Burdescu, Marius Brezovan

  • , Department of Software Engineering, University of Craiova, Craiova, Romania

    Cristian Gabriel Mihai

Bibliographic Information

Buy it now

Buying options

eBook USD 34.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 49.95
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access