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Detection of characteristic texture parameters in breast MRI

  • Conference paper
Book cover 4th European Conference of the International Federation for Medical and Biological Engineering

Part of the book series: IFMBE Proceedings ((IFMBE,volume 22))

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Abstract

Breast cancer is the most common cancer in women. Breast MRI (BMRI) has emerged as a promising technique for detecting, diagnosing, and staging the condition. Automated image analysis aims to extract relevant information from MR images of the breast and improve the accuracy and consistency of image interpretation. Texture analysis (TA) is one possible means of detecting tissue features in biomedical images.

The aim of this study was to evaluate the parameters which identify the most important breast cancer characteristics and to assess the ability of MRI-based TA to characterize breast cancer tissue. Seven patients with histopathologically proven breast cancer were included in this preliminary study. The texture analysis was performed with MaZda texture application. The most discriminant texture features identified by Fisher coefficients and POE+ACC (probability of classification error and average correlation coefficients) between breast cancer tissue and reference tissue from the healthy breast and tissue adjoining the cancer area were evaluated. This evaluation was made between patients, different imaging series and two histological types of (ductal vs. lobular) carcinomas. Raw data analysis (RDA), principal component analysis (PCA), linear discriminant analysis (LDA) and nonlinear discriminant analysis (NDA) were run for each subset of images and chosen texture features. The results revealed differences in the textures in every imaging series when non-cancer and cancer tissue were compared and the best discrimination results were obtained within two dynamic contrast-enhanced MRI subtraction series. Furthermore, the texture parameters obtained differed between the two histological groups. The preliminary results show potential in discriminating between normal and abnormal breast tissue elements, encouraging us to continue with larger data sets.

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Authors and Affiliations

  1. Medical Imaging Centre, Tampere University Hospital, Teiskontie 35 PL 2000, 33521, Tampere, Finland

    Kirsi Holli, S. Soimakallio, P. Dastidar & H. J. Eskola

  2. Department of Biomedical Engineering, Tampere University of Technology, Tampere, Finland

    Kirsi Holli, L. Harrison & H. J. Eskola

  3. Faculty of Medicine, University of Tampere, Tampere, Finland

    A. -L. Lääperi, L. Harrison, S. Soimakallio & P. Dastidar

Authors
  1. Kirsi Holli
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  2. A. -L. Lääperi
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  3. L. Harrison
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  4. S. Soimakallio
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  5. P. Dastidar
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  6. H. J. Eskola
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Corresponding author

Correspondence to Kirsi Holli .

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Editors and Affiliations

  1. Biomechanics and Engineering Design Section, KULeuven, Celestijnenlaan 300c - bus 2419, 3001, Heverlee, Belgium

    Jos Vander Sloten

  2. Cardiovascular Mechanics and Biofluid Dynamics Research Unit, Ghent University, De Pintelaan 185, 9000, Gent, Belgium

    Pascal Verdonck

  3. Medical Informatics Department, Free University Brussels, Laarbeeklaan 103, 1090, Brussels, Belgium

    Marc Nyssen

  4. Institute for Biomedical Engineering and Informatics, Technical University of Ilmenau, P.O. Box 100565, 98639, Ilmenau, Germany

    Jens Haueisen

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© 2009 Springer-Verlag Berlin Heidelberg

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Holli, K., Lääperi, A.L., Harrison, L., Soimakallio, S., Dastidar, P., Eskola, H.J. (2009). Detection of characteristic texture parameters in breast MRI. In: Vander Sloten, J., Verdonck, P., Nyssen, M., Haueisen, J. (eds) 4th European Conference of the International Federation for Medical and Biological Engineering. IFMBE Proceedings, vol 22. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89208-3_123

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  • DOI: https://doi.org/10.1007/978-3-540-89208-3_123

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89207-6

  • Online ISBN: 978-3-540-89208-3

  • eBook Packages: EngineeringEngineering (R0)

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