Computer-aided diagnosis for diagnostically challenging breast lesions in DCE-MRI based on image registration and integration of morphologic and dynamic characteristics

Diagnostically challenging lesions comprise both foci (small
lesions) and non-mass-like enhancing lesions and pose a challenge
to current computer-aided diagnosis systems. Motion-based artifacts
lead in dynamic contrast-enhanced breast magnetic resonance to
diagnostic misinterpretation; therefore, motion compensation
represents an important prerequisite to automatic lesion detection
and diagnosis. In addition, the extraction of pertinent kinetic and
morphologic features as lesion descriptors is an equally important
task. In the present paper, we evaluate the performance of a
computer-aided diagnosis system consisting of motion correction,
lesion segmentation, and feature extraction and classification. We
develop a new feature extractor, the radial Krawtchouk moment,
which guarantees rotation invariance. Many novel feature extraction
techniques are proposed and tested in conjunction with lesion
detection. Our simulation results have shown that motion
compensation combined with Minkowski functionals and Bayesian
classifier can improve lesion detection and classification.