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, alongside morphological measurement of HS-AFM data. Unlike conventional methods that rely solely on either visual or geometric features for 2D boundary detection, AFMnanoSALQ 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 training datasets creation. As a case study, we focus on α-hemolysin (αHL), a β-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.