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Much has been said about medical imaging and it is the intent of this paper to focus on the motion aspects during macroimaging. A classification is proposed that departs from the classical views by identifying motion from imaging and vice versa and involves correction of motion and evoked motion. Several aspects that introduce significant differences in camera vision deserve to be underlined. The complexity of the imaging process is a major point. This is exemplified for ultrasound imaging and medical resonance imaging as will be shown in this article. The nonstandard classification proposed in this article has attempted to show how specific features of medical imaging may affect motion extraction and tracking. If the main issues found in computer vision are also of concern in medical imaging, the importance of replacing the "black box" vision by an in-depth knowledge of object properties, the physics of the sensing device, and the interactions between them, has been emphasized. It has been pointed out that the standard assumptions in generic motion estimation methods are not verified due to the limitations in data acquisition, the complexity of tissues and organs, and the multiple factors that modify the appearances of the objects of interest. Most of the classical paradigms should benefit from prior modeling to attain a better understanding of physiological motion and to derive innovative ways for expanding its place in the diagnostic process.