Modern microscopy provides rich information about complex biological systems, yet image data are often used only for illustration, causing important quantitative details to be overlooked. Automated image analysis enables objective and large-scale data extraction, but its interpretation is limited without mathematical modeling. Image-based Systems Biology combines high-content imaging, quantitative feature extraction, and computer simulations to infer hidden variables, generate testable hypotheses, and reveal spatial and functional patterns not visible directly in images. Applied to examples such as plasma-cell niches in bone marrow, this integrated approach demonstrates how linking imaging with modeling provides deeper biological insight than either method alone.