Scanned volumetric scalar datasets (as from structural MR or CT) have been the focus of much work in visualization and image analysis. Research in volume visualization has successfully blurred the distinction between methods that merely present pictures of volume data (pure "visualization"), versus methods that generate spatial information about material regions and boundaries ("classification" or "segmentation"). The same underlying information about local differential structure, as captured by the image gradient and Hessian, as well as quantities derived from these, play a role in setting the opacity functions that determine visibility in traditional volume rendering, as well as in image analysis methods for extracting geometric models of mathematically defined image features. I will describe work I've done in this topic, ranging from semi-automatic generation of transfer functions, to recent work on using particle systems to sample image features. The recent particle system work may be especially useful for applications of microCT imaging.