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9 March 2010 A novel and fast method for cluster analysis of DCE-MR image series of breast tumors
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A novel approach is introduced for clustering tumor regions with similar signal-time series measured by dynamic contrast-enhanced (DCE) MRI to segment the tumor area in breast cancer. Each voxel of the DCE-MRI dataset is characterized by a signal-time curve. The clustering process uses two describer values for each pixel. The first value is L2-norm of each time series. The second value r is calculated as sum of differences between each pair of S(n-i) and S(i) for i = {0...n/2} where S is the intensity and n the number of values in a time series. We call r reverse value of a time series. Each time series is considered as a vector in an n-dimensional space and the L2-norm and reverse value of a vector are used as similarity measures. The curves with similar L2-norms and similar reverse values are clustered together. The method is tested on breast cancer DCE-MRI datasets with N = 256 x 256 spatial resolution and n = 128 temporal resolution. The quality of each cluster is described through the variance of Euclidean distances of the vectors to the mean vector of the corresponding cluster. The combination of both similarity measures improves the segmentation compared to using each measure alone.
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Mojgan Mohajer, Gunnar Brix, and Karl-Hans Englmeier "A novel and fast method for cluster analysis of DCE-MR image series of breast tumors", Proc. SPIE 7626, Medical Imaging 2010: Biomedical Applications in Molecular, Structural, and Functional Imaging, 76260R (9 March 2010);

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