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Lung Segmentation of MR Images: A Review

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Book cover Visualization in Medicine and Life Sciences III

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

Abstract

Magnetic resonance imaging (MRI) is a non-radiation based examination method, which gains an increasing popularity in research and clinical settings. Manual analysis of large data volumes is a very time-consuming and tedious process. Therefore, automatic analysis methods are required. This paper reviews different methods that have been recently proposed for automatic and semi-automatic lung segmentation from magnetic resonance imaging data. These techniques include thresholding, region growing, morphological operations, active contours, level sets, and neural networks. We also discuss the methodologies that have been utilized for performance and accuracy evaluation of each method.

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Authors and Affiliations

  1. Institute of Community Medicine, Ernst-Moritz-Arndt University, Greifswald, Germany

    Tatyana Ivanovska & Henry Völzke

  2. Institute of Diagnostic Radiology and Neuroradiology, Ernst-Moritz-Arndt University, Greifswald, Germany

    Katrin Hegenscheid & René Laqua

  3. Department of Internal Medicine B, Division of Pulmonary Medicine, Ernst-Moritz-Arndt University, Greifswald, Germany

    Sven Gläser & Ralf Ewert

Authors
  1. Tatyana Ivanovska
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  2. Katrin Hegenscheid
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  3. René Laqua
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  4. Sven Gläser
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  5. Ralf Ewert
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  6. Henry Völzke
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Correspondence to Tatyana Ivanovska .

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Editors and Affiliations

  1. Department of Computer Science and Elect, Jacobs University, Bremen, Germany

    Lars Linsen

  2. Department of Computer Science, University of California, Davis, California, USA

    Bernd Hamann

  3. Visualization and Data Analysis, Zuse Institute Berlin (ZIB), Berlin, Germany

    Hans-Christian Hege

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Ivanovska, T., Hegenscheid, K., Laqua, R., Gläser, S., Ewert, R., Völzke, H. (2016). Lung Segmentation of MR Images: A Review. In: Linsen, L., Hamann, B., Hege, HC. (eds) Visualization in Medicine and Life Sciences III. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-24523-2_1

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