Quantitative Characterization of Microfiltration Membrane Fouling Using Optical Coherence Tomography with Optimized Image Analysis

Membrane fouling reduces permeate flux and treatment efficiency, yet most diagnostic methods are destructive and require offline analysis. Optical coherence tomography (OCT) enables in situ, real-time visualization; however, quantitative image extraction of thin foulant layers is often limited by manual processing and subjective thresholding. Here, we develop a reproducible OCT image-analysis workflow that combines band-pass filtering, Gaussian smoothing, and unsharp masking with a dual-threshold subtraction strategy for automated fouling-layer segmentation. Seventeen global thresholding algorithms in ImageJ (289 threshold pairs) were benchmarked against SEM-measured cake thickness, identifying Triangle-Moments as the most robust combination. For humic-acid fouling, the OCT-derived endpoint thickness (14.23 ± 1.18 µm) closely agreed with SEM (15.29 ± 1.54 µm). The method was then applied to other microfiltration foulants, including kaolin and sodium alginate, to quantify thickness evolution alongside flux decline. OCT with the optimized image analysis captured rapid early deposition and revealed periods where flux loss continued despite minimal additional thickness growth, consistent with changes in layer permeability and compaction. The proposed framework advances OCT from qualitative visualization to quantitative, real-time fouling diagnostics and supports mechanistic interpretation and improved operational control of membrane systems.