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Lespinats, S., Meyer-Baese, A., Steinbruecker, F., and Schlossbauer, T. “Evaluation and Visual Exploratory Analysis of DCE-MRI Data of Breast Lesions Based on Morphological Features and Novel Dimension Reduction Methods.” International Joint Conference on Neural Networks, IJCNN2009., june 2009, Atlanta, USA.


Abstract :
Visual exploratory data analysis represents a wellaccepted imaging modality for high-dimensional DCE-MRIderived breast cancer data. We employ this paradigm for discriminating between malignant and benign lesions based on different shape descriptors thanks to proven and novel dimension reduction algorithms. We demonstrate that shape descriptors based on moments emphasizing local shape structure changes such as weighted 3D Krawtchouk moments outperform global averaging moments such as geometric moment invariants in terms of discrimination of benign/malignant lesions. The best visualization of tumor shapes in a two-dimensional space is achieved based on nonlinear mapping methods, especially the ones that consider neighborhood ranks.