Fast Parallel Thinning Algorithm

The Fast Parallel Thinning Algorithm is an efficient image processing technique designed to extract object skeletons (centerlines) from binary images while preserving topological integrity. This algorithm iteratively removes boundary pixels while retaining critical structural points, ensuring connectivity in the thinned curves.

Core Concepts Parallel Processing: Unlike traditional sequential thinning methods, this algorithm simultaneously detects all removable boundary pixels in a single iteration, significantly improving computational efficiency through matrix operations and parallel pixel evaluation. Connectivity Preservation: By applying constraints such as neighborhood relationships or topological numbering, the algorithm prevents excessive deletion that could cause breaks, maintaining skeleton continuity using connectivity checks typically implemented with 8-neighborhood analysis. Iteration Termination: The algorithm terminates when no more pixels can be safely removed, outputting a single-pixel-width skeleton through iterative scanning until convergence.

Advantages and Challenges Advantages: Suitable for real-time processing and large-scale images (e.g., medical imaging or document analysis), while avoiding the "spurious branch" problem common in traditional thinning algorithms through parallel deletion rules. Challenges: Requires balancing parallel efficiency with topological protection, particularly needing special rules when handling intersections or complex branches to prevent structural collapse.

Extended Applications The algorithm can be combined with morphological operations for optimization, or applied to character skeleton extraction in OCR (Optical Character Recognition) systems, improving accuracy in subsequent feature analysis through preprocessing and skeleton-based feature detection.