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Computational Intelligence in Biomedical Imaging pp 377–397Cite as

Subtraction Techniques for CT and DSA and Automated Detection of Lung Nodules in 3D CT

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Abstract

The interpretation of a large number of CT images is time consuming and hard work for radiologists. Therefore, we have developed two subtraction techniques based on a 3D morphological filtering technique and a temporal subtraction technique to remove normal structures such as pulmonary vessels and bones.

Digital subtraction angiography (DSA) is inadequate for coronary artery due to the existence of severe motion artifacts. In view of this, we have developed a new DSA technique with an artifact reduction technique based on the time-series image processing. The results indicated a considerable improvement in DSA quality; thus, the coronary arteries, carotid artery, and vein were clearly enhanced.

We have also developed an automated computerized method for the detection of lung nodules in 3D computed tomography (CT) images obtained by helical CT. To enhance lung nodules, we employed 3D cross-correlation method by use of a 3D Gaussian template with zero-surrounding as a template. The average number of false positives was 5.2 per case at the sensitivity of 95.8 %.

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Authors and Affiliations

  1. Department of Medical Physics and Engineering, Osaka University, Graduate School of Medicine, 1-7, Yamadaoka, Suita, Osaka, 565-0871, Japan

    Takayuki Ishida

  2. Hiroshima International University, 555-36, Kurose-Gakuendai, Higashihiroshima, Hiroshima, Japan

    Megumi Yamamoto & Yasuhiko Okura

Authors
  1. Takayuki Ishida
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  2. Megumi Yamamoto
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  3. Yasuhiko Okura
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Correspondence to Takayuki Ishida .

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Editors and Affiliations

  1. Department of Radiology, University of Chicago, Chicago, Illinois, USA

    Kenji Suzuki

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Ishida, T., Yamamoto, M., Okura, Y. (2014). Subtraction Techniques for CT and DSA and Automated Detection of Lung Nodules in 3D CT. In: Suzuki, K. (eds) Computational Intelligence in Biomedical Imaging. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7245-2_14

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  • DOI: https://doi.org/10.1007/978-1-4614-7245-2_14

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  • Online ISBN: 978-1-4614-7245-2

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