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Evaluation and visual exploratory analysis of DCE-MRI Data of breast lesions based on morphological features and novel dimension reduction methods

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4 Author(s)
Sylvain Lespinats ; CEA, LIST, Multisensor Intelligence and Machine Learning Laboratory. F91191 Gif-sir-Yvette, France ; Anke Meyer-Baese ; Frank Steinbrucker ; Thomas Schlossbauer

Visual exploratory data analysis represents a well-accepted imaging modality for high-dimensional DCE-MRI-derived 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 structure changes such as weighted 3D Krawtchouck 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.

Published in:

2009 International Joint Conference on Neural Networks

Date of Conference:

14-19 June 2009