Image Processing and Analysis for Biotechnology and Bioprocess Engineering

This research area reviews and supports the use of advanced image‑processing methods in biotechnology and bioprocess engineering. As high‑performance computers and computer‑vision algorithms improve, non‑invasive optical monitoring has become a key tool for real‑time bioprocess analysis. The project evaluates several image‑analysis workflows, such as: – cell segmentation – morphological classification – biomass estimation By working with digital microscopy and in‑situ imaging data, the framework advises on the best choices for preprocessing, feature extraction, and machine‑learning models to track cell‑culture behavior. It puts special focus on comparing classic thresholding techniques with deep‑learning segmentation networks under different bioreactor lighting and turbidity conditions. The goal is to create a solid protocol for measuring

algorithm accuracy, computational speed, and scalability in an industrial bioprocess setting. In the end, this guidance helps researchers add automated optical inspection into closed‑loop bioreactor control, strengthening process‑analytical‑technology (PAT) systems.

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