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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 performance of each texture feature in the task of distinguishing between malignant and benign breast lesions is assessed by receiver operating characteristics (ROC) analysis. Our results show that the classification performance of volumetric texture features is significantly better than that based on 2D analysis. Our investigations of the effects of various of parameters on the diagnostic accuracy provided means for the optimal use of the approach.
Chen et al. (Thu,) studied this question.