Most academic and commercial tri-dimensional (3D) parasitic resistance extraction EDA/CAD tools rely on finite ele- ment methods (FEM) and are mainly suited to digital circuitry. In analog and mixed-signal (AMS) circuits, such as power converters and radio-frequency analog front-ends, the layout structures used for the metal interconnections become much more diversified and complex. This paper proposes an EDA/CAD tool, based on an innovative methodology for 3D parasitic resistance extraction, leveraged by image processing techniques and algorithms. Some practical examples are shown to demonstrate the attractiveness of the proposed tool. Moreover, since our tool efficiently works in the domains of 2D image processing, if an extensive database of layouts is provided and enough training is carried out, advanced deep-learning techniques can be straightforwardly employed, speeding up parasitic resistance extraction in highly complex AMS layouts.