Comparison of Multi-resolution Analysis Patterns for Texture Classification of Breast Tumors Based on DCE-MRI
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).
KeywordsBreast tumor diagnosis DCE-MRI Texture Wavelet Classification
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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