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Comparison of Multi-resolution Analysis Patterns for Texture Classification of Breast Tumors Based on DCE-MRI

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Machine Learning in Medical Imaging (MLMI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10019))

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Abstract

Although Fourier and Wavelet Transform have been widely used for texture classification methods in medical images, the discrimination performance of FDCT has not been investigated so far in respect to breast cancer detection. Ιn this paper, three multi-resolution transforms, namely the Discrete Wavelet Transform (DWT), the Stationary Wavelet Transform (SWT) and the Fast Discrete Curvelet Transform (FDCT) were comparatively assessed with respect to their ability to discriminate between malignant and benign breast tumors in Dynamic Contrast-Enhanced Magnetic Resonance Images (DCE-MRI). The mean and entropy of the detail sub-images for each decomposition scheme were used as texture features, which were subsequently fed as input into several classifiers. FDCT features fed to a Linear Discriminant Analysis (LDA) classifier produced the highest overall classification performance (93.18 % Accuracy).

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Acknowledgements

The authors wish to thank Dr. Sarah Englander and Dr. Mitchell Schnall from University of Pennsylvania, USA, who supported the collection of the data. It should also be noted that K. V. Dalakleidi was supported by a scholarship for Ph.D. studies from the Hellenic State Scholarships Foundation “IKY fellowships of excellence for post-graduate studies in Greece-Siemens Program”. This work has been partially supported from the European Research Council Grant 259112.

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Authors and Affiliations

  1. School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece

    Alexia Tzalavra, Kalliopi Dalakleidi, Nikolaos Tsiaparas & Konstantina S. Nikita

  2. CentraleSupélec, Inria, Université Paris-Saclay, Saint-Aubin, France

    Evangelia I. Zacharaki & Nikos Paragios

  3. NHS Greater Glasgow and Clyde, Glasgow, UK

    Fotios Constantinidis

Authors
  1. Alexia Tzalavra
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  2. Kalliopi Dalakleidi
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  3. Evangelia I. Zacharaki
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  4. Nikolaos Tsiaparas
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  5. Fotios Constantinidis
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  6. Nikos Paragios
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  7. Konstantina S. Nikita
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Correspondence to Alexia Tzalavra .

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Editors and Affiliations

  1. University of North Carolina , Chapel Hill, North Carolina, USA

    Li Wang

  2. University of North Carolina , Chapel Hill, North Carolina, USA

    Ehsan Adeli

  3. Shanghai Jiaotong University , Shanghai, China

    Qian Wang

  4. Nanjing University , Nanjing, China

    Yinghuan Shi

  5. Korea University , Seoul, Korea (Republic of)

    Heung-Il Suk

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Tzalavra, A. et al. (2016). Comparison of Multi-resolution Analysis Patterns for Texture Classification of Breast Tumors Based on DCE-MRI. In: Wang, L., Adeli, E., Wang, Q., Shi, Y., Suk, HI. (eds) Machine Learning in Medical Imaging. MLMI 2016. Lecture Notes in Computer Science(), vol 10019. Springer, Cham. https://doi.org/10.1007/978-3-319-47157-0_36

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  • DOI: https://doi.org/10.1007/978-3-319-47157-0_36

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-47157-0

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