Developing the Computational Image Processing Methodfor Quantitative Analysis of Nanopore Structure Obtained from HS-AFM (AFMnanoQ)

High-Speed Atomic Force Microscopy (HS-AFM) enables imaging of biological structures and dynamics with nanometer spatial and millisecond temporal resolution. AFM images contain three-dimensional (3D) surface information, comprising two-dimensional (2D) lateral (x-y) and one-dimensional (1D) height (z) encoded in pixel intensity. This dynamic structure poses significant challenges for instance boundary detection and morphological analysis. To address this, we develop AFMnanoSALQ, a feature-driven computational framework for semi-automatic labeling and quantitative (SALQ) detection and morphological measurement of HS-AFM data. Unlike conventional methods that rely solely on either visual or geometric features for 2D boundary detection, AFM- nanoSALQ integrates both to extract 3D morphology. It requires neither annotated data nor intensive training, enabling fast deployment at minimal cost. With performance comparable to typical deep-learning models, AFMnanoSALQ facilitates semi-automatic labeling, making it a practical tool for preliminary data inspection and accelerating the creation of training datasets. As a case study, we focus on -hemolysin (HL), a {beta}-barrel pore-forming toxin secreted by Staphylococcus aureus, using both synthetic and experimental AFM data. AFMnanoSALQ provides a foundation for future deep learning studies, enabling both dataset generation and cross-validation between feature-driven and data-driven approaches.