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Medical Images Annotation

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Creating New Medical Ontologies for Image Annotation

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

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Abstract

Image classification and automatic annotation could be treated as effective solutions to enable keyword-based semantic image retrieval. The importance of automatic image annotation has increased with the growth of the digital images collections being of great interest as it allows indexing, retrieving, and understanding of large collections of image data. In this chapter, we are presenting an overview of the existing methods for the annotation task from several perspectives: unsupervised/supervised learning, parametric/nonparametric unsupervised learning models, or text/image-based. For medical images annotation, we have chosen an extension of the cross-media relevance model based on an object-oriented approach. This method is presented in detail together with the steps that should be applied to annotate a new image. An evaluation of the annotation process and the experimental results are presented in the final part of this chapter.

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Authors and Affiliations

  1. Department of Software Engineering, University of Craiova, Craiova, 200585, Romania

    Liana Stanescu, Dumitru Dan Burdescu, Marius Brezovan & Cristian Gabriel Mihai

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  1. Liana Stanescu
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  2. Dumitru Dan Burdescu
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  3. Marius Brezovan
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  4. Cristian Gabriel Mihai
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Correspondence to Liana Stanescu .

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Stanescu, L., Burdescu, D.D., Brezovan, M., Mihai, C.G. (2012). Medical Images Annotation. In: Creating New Medical Ontologies for Image Annotation. SpringerBriefs in Electrical and Computer Engineering(). Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1909-9_5

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  • DOI: https://doi.org/10.1007/978-1-4614-1909-9_5

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