Methods for personalizing medical treatment are the focal point of contemporary biomedical research. In cancer care, we can analyze the effects of therapies at the level of individual cells. Quantitative characterization of treatment efficacy and evaluation of why some individuals respond to specific regimens, whereas others do not, requires additional approaches to genetic sequencing at single time points. Methods for the analysis of changes in phenotype, such as in vivo and ex vivo morphology and localization of cellular proteins and organelles can provide important insights into patient treatment options. Novel therapies are needed to extend survival in metastatic castration-resistant prostate cancer (mCRPC). Prostate-specific membrane antigen (PSMA), a cell surface glycoprotein that is commonly overexpressed by prostate cancer (PC) cells relative to normal prostate cells, provides a validated target. We developed a software for image analysis designed to identify PSMA expression on the surface of epithelial cells in order to extract prognostic metrics. In addition, our software can deliver predictive information and inform clinicians regarding the efficacy of PC therapy. We can envisage additional applications of our software system, beyond PC, as PSMA is expressed in a variety of tissues. Our method is based on image denoising, topologic partitioning, and edge detection. These three steps allow to segment the area of each PSMA spot in an image of a coverslip with epithelial cells. Our objective has been to present the community with an integrated, easy to use by all, tool for resolving the complex cytoskeletal organization and it is our goal to have such software system approved for use in the clinical practice.