A workflow for quality control in surface texture analysis applied to teeth and tools. (December 2022) - Calandra, Ivan
Highlights: DMTA and QAMA require high-quality 3D data (height maps). A good-practice workflow for quality control in DMTA and QAMA is proposed. This workflow should be applied routinely before running the quantitative analysis. Quality control improves the robustness of the results and their interpretations. Abstract: Quantification and automation represent important methodological developments in dental microwear analysis and in artifact microwear (use-wear) analysis in order to address the issues of subjectivity and reproducibility of the traditional methods, increase discrimination power, and improve pattern recognition. However, automatic and quantitative methods (dental microwear texture and quantitative artifact microwear analyses) require high-quality data to work with. Surface data acquired with confocal microscopy can reach incredible levels of accuracy and resolution; yet, they are not without issues. In this paper, in an approach similar to quality control in industry, I propose a good-practice workflow for controlling the quality of the acquired and processed 3D data (height maps). I also suggest tools to deal with some of the issues commonly encountered. This workflow should be applied routinely to every height map after acquisition and processing, so that only high-quality height maps are analyzed. In turn, the systematic quality control of the 3D data improves the robustness of the quantitative results and of their interpretations.