Motion-induced artifacts represent a major problem in detection and diagnosis of breast cancer in dynamic contrast-enhanced magnetic resonance imaging. The goal of this paper is to evaluate the performance of a new nonrigid motion correction algorithm based on the optical flow method. For each of the small lesions, we extracted morphological and dynamical features describing both global and local shape, and kinetics behavior. In this paper, we compare the performance of each extracted feature set under consideration of several 2D or 3D motion compensation parameters for the differential diagnosis of enhancing lesions in breast MRI. Based on several simulation results, we determined the optimal motion compensation parameters. Our results have shown that motion compensation can improve the classification results. The results suggest that the computerized analysis system based on the non-rigid motion compensation technique and spatiotemporal features has the potential to increase the diagnostic accuracy of MRI mammography for small lesions and can be used as a basis for computer-aided diagnosis of breast cancer with MR mammography. 1. Introduction Breast cancer is one of the leading death cases among women in the US. MR technology has advanced tremendously and became a highly sensitive method for the detection of invasive breast cancer [1]. While only dynamic signal intensity characteristics are integrated in today's CAD systems leaving morphological features to the interpretation of the radiologist, an automated diagnosis based on a combination of both should be strived for. Clinical studies have shown that combinations of different dynamic and morphologic characteristics [2] achieve diagnostic sensitivities up to 97 % and specificities up to 76.5 % . However, most of these techniques have not been applied to small enhancing foci having a size of less than 1？cm. These diagnostically challenging cases found unclear ultrasound or mammography indications can be adequately visualized in magnetic resonance imaging (MRI) [3] with MRI providing an accurate estimation of invasive breast cancer tumor size [4]. Automatic motion correction represents an important prerequisite to a correct automated small lesion evaluation [5]. Motion artifacts are caused either by the relaxation of the pectoral muscle or involuntary patient motion and invalidate the assumption of same spatial location within the breast of the corresponding voxels in the acquired volumes for assessing lesion enhancement. Especially for small lesions, the assumption of correct spatial alignment often
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