Functional-molecular imaging techniques promise powerful tools for the visualization and elucidation of important disease-causing physiologic-molecular processes in living tissue. Most applications aim to find temporal-spatial patterns assocaited with different disease stages. When multiple agents are used, imagery signals often represent a composite of more than one distinct source due to functional-molecular biomarker heterogeneity, independent of spatial resolution. We therefore introduce a hybrid decomposition algorithm which allows for a computed simultaneous imaging of multiple biomarkers. The method is based on a combination of time-activity curve clustering, pixel subset selection, and independent component analysis. We demonstrate the principle of the approach on an image data set, and we then apply the method to the tumor vascular characterization using dynamic contrast-enhanced magnetic resonance imaging and brain neuro-transporter imaging using dynamic positron emission tomography.