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

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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Communication dans un congrès
7th Int. Workshop on Machine Learning in Medical Imaging (MICCAI workshop), Oct 2016, Athens, Greece. Machine Learning in Medical Imaging, 10019 pp.296-304, 〈10.1007/978-3-319-47157-0_36〉
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Alexia Tzalavra, Kalliopi Dalakleidi, Evangelia I. Zacharaki, Nikolaos Tsiaparas, Fotios Constantinidis, et al.. Comparison of Multi-resolution Analysis Patterns for Texture Classification of Breast Tumors Based On DCE-MRI. 7th Int. Workshop on Machine Learning in Medical Imaging (MICCAI workshop), Oct 2016, Athens, Greece. Machine Learning in Medical Imaging, 10019 pp.296-304, 〈10.1007/978-3-319-47157-0_36〉. 〈hal-01359118〉

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