Lesion classification on breast MRI through topological characterization of morphology over time

Morphological characterization of lesions on dynamic breast MRI exams through texture analysis has typically involved the computation of gray-level co-occurrence matrices (GLCM), which serve as the basis for second order statistical texture features. This study aims to characterize lesion morphology through the underlying topology and geometry with Minkowski Functionals (MF) and investigate the impact of using such texture features extracted dynamically over a time series in classifying benign and malignant lesions. 60 lesions (28 malignant & 32 benign) were identified and annotated by experienced radiologists on 54 breast MRI exams of female patients where histopathological reports were available prior to this investigation. 13 GLCM-derived texture features and 3 MF features were then extracted from lesion ROIs on all five post-contrast images. These texture features were combined into high dimensional texture feature vectors and used in a lesion classification task. A fuzzy k-nearest neighbor classifier was optimized using random sub-sampling cross-validation for each texture feature and the classification performance was calculated on an independent test set using the area under the ROC curve (AUC); AUC distributions of different features were compared using a Mann- Whitney U-test. The MF feature 'Area' exhibited significantly improvements in classification performance (p<0.05) when compared to all GLCM-derived features while the MF feature 'Perimeter' significantly outperformed 12 out of 13 GLCM features (p<0.05) in the lesion classification task. These results show that dynamic texture tracking of morphological characterization that relies on topological texture features can contribute to better lesion character classification.