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2016 | OriginalPaper | Chapter

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

Authors : Alexia Tzalavra, Kalliopi Dalakleidi, Evangelia I. Zacharaki, Nikolaos Tsiaparas, Fotios Constantinidis, Nikos Paragios, Konstantina S. Nikita

Published in: Machine Learning in Medical Imaging

Publisher: Springer International Publishing

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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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previous chapter Mitosis Detection in Intestinal Crypt Images with Hough Forest and Conditional Random Fields
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Metadata
Title
Comparison of Multi-resolution Analysis Patterns for Texture Classification of Breast Tumors Based on DCE-MRI
Authors
Alexia Tzalavra
Kalliopi Dalakleidi
Evangelia I. Zacharaki
Nikolaos Tsiaparas
Fotios Constantinidis
Nikos Paragios
Konstantina S. Nikita
Copyright Year
2016
Book
Machine Learning in Medical Imaging

Print ISBN: 978-3-319-47156-3

Electronic ISBN: 978-3-319-47157-0

Copyright Year: 2016

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

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