Nanoparticles have become a powerful tool for cell imaging and biomolecule, cell and protein interaction studies, but are difficult to rapidly and accurately measure in most assays. Dark-field microscope (DFM) image analysis approaches used to quantify nanoparticles require high-magnification near-field (HN) images that are labor intensive due to a requirement for manual image selection and focal adjustments needed when identifying and capturing new regions of interest. Low-magnification far-field (LF) DFM imagery is technically simpler to perform but cannot be used as an alternate to HN-DFM quantification, since it is highly sensitive to surface artifacts and debris that can easily mask nanoparticle signal. We now describe a new noise reduction approach that markedly reduces LF-DFM image artifacts to allow sensitive and accurate nanoparticle signal quantification from LF-DFM images. We have used this approach to develop a "Dark Scatter Master" (DSM) algorithm for the popular NIH image analysis program ImageJ, which can be readily adapted for use with automated high-throughput assay analyses. This method demonstrated robust performance quantifying nanoparticles in different assay formats, including a novel method that quantified extracellular vesicles in patient blood sample to detect pancreatic cancer cases. Based on these results, we believe our LF-DFM quantification method can markedly decrease the analysis time of most nanoparticle-based assays to impact both basic research and clinical analyses.