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© 2012

Creating New Medical Ontologies for Image Annotation

A Case Study

Benefits

  • 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

Book

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

Also part of the SpringerBriefs in Speech Technology book sub series

Table of contents

  1. Front Matter
    Pages i-viii
  2. Liana Stanescu, Dumitru Dan Burdescu, Marius Brezovan, Cristian Gabriel Mihai
    Pages 1-3
  3. Liana Stanescu, Dumitru Dan Burdescu, Marius Brezovan, Cristian Gabriel Mihai
    Pages 5-14
  4. Liana Stanescu, Dumitru Dan Burdescu, Marius Brezovan, Cristian Gabriel Mihai
    Pages 15-43
  5. Liana Stanescu, Dumitru Dan Burdescu, Marius Brezovan, Cristian Gabriel Mihai
    Pages 45-64
  6. Liana Stanescu, Dumitru Dan Burdescu, Marius Brezovan, Cristian Gabriel Mihai
    Pages 65-89
  7. Liana Stanescu, Dumitru Dan Burdescu, Marius Brezovan, Cristian Gabriel Mihai
    Pages 91-102
  8. Liana Stanescu, Dumitru Dan Burdescu, Marius Brezovan, Cristian Gabriel Mihai
    Pages 103-111

About this book

Introduction

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.

Keywords

Automated ontologies Color images segmentation Cross media relevance models Image annotation MESH Ontologies for medical images Segmentation algorithm

Authors and affiliations

  1. 1., Software Engineering Dept.University of CraiovaCraiovaRomania
  2. 2., Software Engineering Dept.University of CraiovaCraiovaRomania
  3. 3., Software Engineering Dept.University of CraiovaCraiovaRomania
  4. 4., Department of Software EngineeringUniversity of CraiovaCraiovaRomania

Bibliographic information

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