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Anatomical Object Detection in Fetal Ultrasound: Computer-Expert Agreements

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Biomedical Informatics and Technology (ACBIT 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 404))

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Abstract

An alternative approach to the sliding window method for the detection of anatomical objects in fetal ultrasound image is proposed in this pper. The global feature symmetry map derived from the local phase computation of the images was integrated within a machine learning framework that trains a local classifier using local Haar features from intensity images. This provides a computationally cheap step before invoking a local object detector to be applied in plausible locations. The proposed method exhibits better generalization capability when tested on 2384 images with an accuracy of 82.75% and 72.55% for the detection of the stomach and the umbilical vein, respectively. It also has faster computation time than the typical local object detector with the sliding window approach. It was observed that the method achieved high accuracy detection by focusing only on the high probability region and discarding many false positives candidates as in the sliding-window method. The agreement between the automated method and the experts in detecting the presence and absence of the stomach and the umbilical vein were also compared. The results indicate that the agreement between the automated method and the experts were very good for computer-random-selected images and the agreement was comparable to inter-experts agreement.

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Author information

Authors and Affiliations

  1. Faculty of Arts, Computing and Creative Industry, Sultan Idris Education University, Malaysia

    Bahbibi Rahmatullah

  2. Institute of Biomedical Engineering, Dept. of Engineering Science, University of Oxford, UK

    J. Alison Noble

Authors
  1. Bahbibi Rahmatullah
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  2. J. Alison Noble
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Editor information

Editors and Affiliations

  1. Aizu Research Cluster for Medical Engineering and Informatics Research Center for Advanced Information Science and Technology, The University of Aizu, 965-8580, Aizuwakamatsu, Fukushima, Japan

    Tuan D. Pham

  2. Department of Cancer Biology, Division of Mathematical Oncology, The Institute of Medical Science, The University of Tokyo, 108-8639, Tokyo, Japan

    Kazuhisha Ichikawa

  3. Chaos Technology Research Lab, 5-26-5 Seta, 520-2134, Otsu-shi, Oyama-Higa, Japan

    Mayumi Oyama-Higa

  4. Department of Biostatistics and Medical Informatics, Faculty of Medicine and Pharmacy, The Free University of Brussels, 103, 1090, Laarbeeklaan, Brussels, Belgium

    Danny Coomans

  5. Department of Computer Science, University of Muenster, Einsteinstrasse 62, 48149, Muenster, Germany

    Xiaoyi Jiang

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© 2014 Springer-Verlag Berlin Heidelberg

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Rahmatullah, B., Noble, J.A. (2014). Anatomical Object Detection in Fetal Ultrasound: Computer-Expert Agreements. In: Pham, T.D., Ichikawa, K., Oyama-Higa, M., Coomans, D., Jiang, X. (eds) Biomedical Informatics and Technology. ACBIT 2013. Communications in Computer and Information Science, vol 404. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54121-6_18

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  • DOI: https://doi.org/10.1007/978-3-642-54121-6_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-54120-9

  • Online ISBN: 978-3-642-54121-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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