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Automated image analysis aims to extract relevant information from contrast‐enhanced magnetic resonance images (CE‐MRI) of the breast and improve the accuracy and consistency of image interpretation. In this work, we extend the traditional 2D gray‐level co‐occurrence matrix (GLCM) method to investigate a volumetric texture analysis approach and apply it for the characterization of breast MR lesions. Our database of breast MR images was obtained using a T1‐weighted 3D spoiled gradient echo sequence and consists of 121 biopsy‐proven lesions (77 malignant and 44 benign). A fuzzy c‐means clustering (FCM) based method is employed to automatically segment 3D breast lesions on CE‐MR images. For each 3D lesion, a nondirectional GLCM is then computed on the first postcontrast frame by summing 13 directional GLCMs. Texture features are extracted from the nondirectional GLCMs and the …