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A Semantic Content-Based Retrieval Method for Histopathology Images

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Information Retrieval Technology (AIRS 2008)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4993))

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Abstract

This paper proposes a model for content-based retrieval of histopathology images. The most remarkable characteristic of the proposed model is that it is able to extract high-level features that reflect the semantic content of the images. This is accomplished by a semantic mapper that maps conventional low-level features to high-level features using state-of-the-art machine-learning techniques. The semantic mapper is trained using images labeled by a pathologist. The system was tested on a collection of 1502 histopathology images and the performance assessed using standard measures. The results show an improvement from a 67% of average precision for the first result, using low-level features, to 80% of precision using high-level features.

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Hang Li Ting Liu Wei-Ying Ma Tetsuya Sakai Kam-Fai Wong Guodong Zhou

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© 2008 Springer-Verlag Berlin Heidelberg

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Caicedo, J.C., Gonzalez, F.A., Romero, E. (2008). A Semantic Content-Based Retrieval Method for Histopathology Images. In: Li, H., Liu, T., Ma, WY., Sakai, T., Wong, KF., Zhou, G. (eds) Information Retrieval Technology. AIRS 2008. Lecture Notes in Computer Science, vol 4993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68636-1_6

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  • DOI: https://doi.org/10.1007/978-3-540-68636-1_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-68633-0

  • Online ISBN: 978-3-540-68636-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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