Accurate characterization of the geometric and surface properties of pressure ulcers is important for supporting therapeutic decisions and wound coverage planning. Three-dimensional scanning technologies provide new possibilities for capturing wound geometry in a digital form and enable more detailed spatial analysis than traditional two-dimensional approaches. Despite these advances, practical application remains challenging, particularly with respect to system integration, usability, and long-term extensibility of the complete processing pipeline in real-world environments. An essential aspect of the present work is the incorporation of artificial intelligence methods into the wound analysis workflow, with a particular focus on the automatic detection of wound boundaries. This AI-based contour identification component is currently under active development and represents a key functional element of the processing pipeline, as accurate and consistent wound segmentation is fundamental for downstream geometric evaluation and coverage planning. Beyond its role in boundary detection, the integration strategy is designed to ensure that AI techniques can be extended to additional stages of the system in the future, enabling more advanced automation and improved clinical decision support. This study presents the integration and optimization of the system, building on processing components developed in earlier stages of the research. The processing pipeline integrates steps from 3D data capture to wound coverage planning. In its current state, the system combines steps requiring manual user interaction with automated algorithmic procedures. This hybrid approach reflects current practical constraints while allowing targeted automation where reliable solutions are available—including the incremental incorporation of AI-supported modules such as wound boundary detection. A key objective of the present work is to integrate these components into a unified system and to optimize individual processing steps in order to improve transparency, efficiency, and robustness across the pipeline. A modular system architecture forms a central design principle of the proposed approach. Individual functional units are implemented as separate modules that can be developed, tested, and refined independently. This structure allows modules to be replaced or extended without affecting the entire system and supports flexibility in adapting to different hardware configurations or algorithmic strategies, including the future addition of AI-enhanced processing stages. Scanning, processing, analysis, and visualization components can therefore evolve separately while maintaining consistent data flow and stable system behavior over time. Compared to the initial wound coverage baseline version, further developments are introduced across multiple stages of the processing pipeline. These include the consolidation of selected functional units, incremental increases in automation—including the introduction of machine-learning-based wound boundary detection—and targeted improvements to the user interface. The graphical user interface is designed to support both clinical and engineering users by providing an interpretable and structured interaction environment, while manual control is preserved at stages where full automation is not yet feasible or clinically justified. The operating principles and application-specific characteristics of the algorithms used in the processing steps are presented with an emphasis on clarity and accessibility. Algorithmic concepts are described at a level that supports understanding of their role within the system, without relying on extensive implementation detail or complex mathematical formulations. Relevant recent literature is reviewed, and potential directions for future development are discussed, with particular attention to expanding AI integration, increasing automation, and enhancing clinical applicability.