Tissues Classification for Breast MRI Contrast Enhancement Using Spectral Signature Detection Approach

Presently, radiologists used to rely on contrast-injection to acquire the contrast-enhanced breast magnetic resonance imaging (MRI), in order to improve the accuracy of breast cancer screening. Instead of contrast-injection, this paper proposed a spectral signature detection technology, constrained energy minimization (CEM), which could successfully classify breast MRIs into four major tissues (fatty tissue, glandular tissue, tumor and muscle) and show the classified results in high contrast images. After compared with a specific subspace projection operator called orthogonal subspace projection (OSP), the commonly used C-means (CM) algorithm and real contrast-injected breast MRIs, the results show that the high contrast images generated by CEM have superior quality.