Breast lesion discrimination using statistical analysis and shape measures on magnetic resonance imagery

Magnetic resonance images of intact human breast tissue are evaluated using statistical measures and shape analysis. In this paper, the Mahalanobis distance measurement and a related F-statistical value demonstrate that breast lesions are statistically separable from normal breast tissue. The minimum set of parameters to provide first order statistical separability between fibroadenomas, cysts, and carcinomas are T1-weighted, T2-weighted, and Dixon opposed pulse sequences. Tumor shape is quantified by development of a compactness measure and a spatial frequency analysis of the lesion boundary. Malignant lesions are shown to be separable from benign lesions based on quantitative shape measures.