Classification of Breast Masses on Contrast-Enhanced Magnetic Resonance Images Through Log Detrended Fluctuation Cumulant-Based Multifractal Analysis

This paper proposes a multiscale automated model for the classification of suspicious malignancy of breast masses, through log detrended fluctuation cumulant-based multifractal analysis of images acquired by dynamic contrast-enhanced magnetic resonance. Features for classification are extracted by computing the multifractal scaling exponent for each of the 70 clinical cases and by quantifying the log-cumulants reflecting multifractal information related with texture of the enhanced lesions. The output is compared with the radiologist diagnosis that follows the Breast Imaging-Reporting and Data System (BI-RADS). The results suggest that the log-cumulant c(2) can be effective in classifying typically biopsy-recommended cases. The performance of a supervised classification was evaluated by receiver operating characteristic (ROC) with an area under the curve of 0.985. The proposed multifractal analysis can contribute to novel feature classification techniques to aid radiologists every time there is a change in the clinical course, namely, when biopsy should be considered.