Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is useful for breast cancer diagnosis and treatment planning. Nevertheless, due to the multi-temporal nature of DCE-MRI data, the assessment of early stage breast cancer is a challenging task. In this study, we applied an unsupervised clustering approach and cluster validation technique to the analysis of malignant intral-tumoral kinetic curves in DCE-MRI. K-means cluster analysis was performed from real world malignant tumor cases and the data were transformed into an optimal number of reference patterns representative each cluster. The optimal number of clusters was estimated by a cluster validation index, which was calculated with the ratio of inter-class scatter to intra-class scatter. This technique then classifies tumor specific patterns from a given MRI data by measuring the vector distances from the reference pattern set, and compared the result from the k-means clustering with that from three-time-points (3TP) method, which represents a clinical standard protocol for analysis of tumor kinetics. The evaluation of twenty five cases indicates that optimal k-means clustering reflects partitioning intra-tumoral kinetic patterns better than the 3TP technique. This method will greatly enhance the capability of radiologists to identify and characterize internal kinetic heterogeneity and vascular change of a tumor in breast DCE-MRI.

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