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International Conference on Image Analysis and Recognition

ICIAR 2020: Image Analysis and Recognition pp 193–201Cite as

B-Mode Ultrasound Breast Anatomy Segmentation

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12132))

Abstract

Breast Ultrasound has long been used to support diagnostic and exploratory procedures concerning breast cancer, with an interesting success rate, specially when complemented with other radiology information. This usability can further enhance visualization tasks during pre-treatment clinical analysis by coupling the B-Mode images to 3D space, as found in Magnetic Resonance Imaging (MRI) per instance. In fact, Lesions in B-mode are visible and present high detail when comparing with other 3D sequences. This coupling, however, would be largely benefited from the ability to match the various structures present in the B-Mode, apart from the broadly studied lesion. In this work we focus on structures such as skin, subcutaneous fat, mammary gland and thoracic region. We provide a preliminary insight to several structure segmentation approaches in the hopes of obtaining a functional and dependable pipeline for delineating these potential reference regions that will assist in multi-modal radiological data alignment. For this, we experiment with pre-processing stages that include Anisotropic Diffusion guided by Log-Gabor filters (ADLG) and main segmentation steps using K-Means, Meanshift and Watershed.

Among the pipeline configurations tested, the best results were found using the ADLG filter that ran for 50 iterations and H-Maxima suppression of 20% and the K-Means method with \(K=6\). The results present several cases that closely approach the ground truth despite overall having larger average errors. This encourages the experimentation of other approaches that could withstand the innate data variability that makes this task very challenging.

This work was funded by the ERDF - European Regional Development Fund through the Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement and through the Portuguese National Innovation Agency (ANI) as a part of project BCCT.Plan–NORTE-01-0247-FEDER-01768 and by Fundação para a Ciência e a Tecnologia (FCT) within PhD grants number SFRH/BD/135834/2018.

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Notes

  1. 1.

    BCCT.plan - 3D tool for planning breast cancer conservative treatment - NORTE-01-0247-FEDER-017688.

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Author information

Authors and Affiliations

  1. Faculty of Engineering of the University of Porto, Porto, Portugal

    João F. Teixeira & António M. Carreiro

  2. Faculty of Sciences of the University of Porto, Porto, Portugal

    Hélder P. Oliveira

  3. INESC TEC, Porto, Portugal

    João F. Teixeira & Hélder P. Oliveira

  4. Escola Superior de Tecnologia da Saúde de Coimbra, Coimbra, Portugal

    Rute M. Santos

Authors
  1. João F. Teixeira
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  2. António M. Carreiro
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  3. Rute M. Santos
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  4. Hélder P. Oliveira
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Corresponding author

Correspondence to João F. Teixeira .

Editor information

Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Aurélio Campilho

  2. University of Waterloo, Waterloo, ON, Canada

    Fakhri Karray

  3. University of Waterloo, Waterloo, ON, Canada

    Zhou Wang

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Teixeira, J.F., Carreiro, A.M., Santos, R.M., Oliveira, H.P. (2020). B-Mode Ultrasound Breast Anatomy Segmentation. In: Campilho, A., Karray, F., Wang, Z. (eds) Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science(), vol 12132. Springer, Cham. https://doi.org/10.1007/978-3-030-50516-5_17

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  • DOI: https://doi.org/10.1007/978-3-030-50516-5_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50515-8

  • Online ISBN: 978-3-030-50516-5

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