Genetic mutations leading to amino acid substitutions in proteins can disrupt protein structure and interactions, often resulting in disease. The protein Fig4, implicated in several neurodegenerative disorders, normally localizes to lysosomal or vacuolar membranes through interactions with binding partners. Disease-associated mutations in Fig4 can weaken these interactions, potentially altering its subcellular distribution and affecting cellular physiology. To investigate such changes, we developed an automated image processing method to quantify the membrane association of fluorescently tagged Fig4 proteins in live yeast cells—an evolutionarily conserved, tractable model system. Our method distinguishes between strong and weak membrane association based on fluorescence patterns, using red and green channel overlays to detect signal enrichment at the vacuole membrane. The proposed approach was validated using labeled images and expanded it to analyze 39 additional images, accounting for noise and artifacts such as dead cells or background fluorescence. This technique provides an unbiased, scalable strategy to evaluate how disease-linked mutations influence protein localization, offering insight into the molecular mechanisms underlying inherited disorders involving Fig4.