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Automated Breast Volume of Interest Selection by Analysing Breast-Air Boundaries in MRI

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Bildverarbeitung für die Medizin 2015

Part of the book series: Informatik aktuell ((INFORMAT))

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Abstract

The first step in automated breast density estimation is to extract breast volume of interest, namely, the start and end slice numbers from the whole sequence. We evaluated results produced by two radiologists and developed an automatic strategy for the start and end slice detection. The result comparison showed that it is usually more straightforward to find the breast start than the breast end, Where the tissue gradually disappears. In general, the results produced by the algorithm are sufficiently accurate, and our solution will be integrated into a fully automatic breast segmentation pipeline.

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References

  1. Boyd N, Guo H, et al. Mammographic density and the risk and detection of breast cancer. N Engl J Med. 2007;356(3):227–36.

    Article  Google Scholar 

  2. Wang L, Platel B, Ivanovska T, et al. Fully automatic breast segmentation in 3D breast MRI. Proc ISBI. 2012; p. 1024–7.

    Google Scholar 

  3. Wu S, Weinstein S, Conant E, et al. Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas-aided fuzzy C-means method. Med Phys. 2013;40(12):122–302.

    Article  Google Scholar 

  4. Gubern-M´erida A, Kallenberg M, Mann R, et al. Breast segmentation and density estimation in breast MRI: A fully automatic framework. J Biomed Health Inform. 2014;To appear.

    Google Scholar 

  5. Nie K, Chen JH, et al. Development of a quantitative method for analysis of breast density based on three-dimensional breast MRI. Med Phys. 2008;35(12):52 53–62.

    Article  Google Scholar 

  6. Gonzales R, Woods R. Digital Image Processing. Prentice Hall; 2002.

    Google Scholar 

  7. Weickert J, K¨uhne G. Fast methods for implict active contour models. In: Geometric Level Set Methods in Imaging, Vision, and Graphics. Springer; 2003. p. 43–57.

    Google Scholar 

  8. Ivanovska T, Laqua R, Wang L, et al. Fast implementations of the levelset segmentation method with bias field correction in MR images: full domain and mask-based versions. In: Pattern Recognition and Image Analysis; 2013. p. 674–81.

    Google Scholar 

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Ivanovska, T., Wang, L., Völzke, H., Hegenscheid, K. (2015). Automated Breast Volume of Interest Selection by Analysing Breast-Air Boundaries in MRI. In: Handels, H., Deserno, T., Meinzer, HP., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2015. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46224-9_4

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  • DOI: https://doi.org/10.1007/978-3-662-46224-9_4

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  • Publisher Name: Springer Vieweg, Berlin, Heidelberg

  • Print ISBN: 978-3-662-46223-2

  • Online ISBN: 978-3-662-46224-9

  • eBook Packages: Computer Science and Engineering (German Language)

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