Accurate evaluation of fungal growth dynamics requires robust and quantitative image analysis methods. This project develops computational image-processing tools for the UMass Biofluids Lab to analyze timelapse microscopy of fungal mycelial growth. The pipeline will implement segmentation, skeletonization, and topological mapping algorithms to generate high-fidelity structural representations of fungal networks while preserving connectivity and branching architecture. From these processed images, the system will extract quantitative descriptors of network dynamics, including hyphal extension rate, tip growth directionality, branching frequency, junction density, and temporal structural evolution. By transforming complex timelapse microscopy image sets into dynamic growth quantities, this framework enables objective comparison across experimental conditions and supports data driven interpretation of fungal growth behavior.