Stack-Based Image Segmentation Algorithm

The stack-based image segmentation algorithm is an improved region growing method that significantly enhances segmentation speed while reducing computational resource consumption. Compared to traditional region growing approaches, this algorithm utilizes the Last-In-First-Out (LIFO) characteristic of stack data structures to optimize pixel processing order, thereby minimizing redundant calculations.

Core Implementation Strategy: Seed Point Selection: Similar to conventional methods, the algorithm initiates from user-defined or automatically detected seed points. In code implementation, seed points can be stored in an array, with their coordinates serving as initial entries for the stack. Stack Management: Pixels awaiting processing are pushed onto the stack. The algorithm consistently retrieves pixels from the top of the stack for similarity evaluation, prioritizing the most recently added neighboring pixels. This can be implemented using a simple array with push() and pop() operations, where pop() always removes the last-added element. Dynamic Expansion: When the current pixel meets growth criteria (such as grayscale difference thresholds), its unprocessed neighboring pixels are pushed onto the stack, enabling rapid local expansion. Developers can implement boundary checks using 4-connectivity or 8-connectivity neighborhood scanning to ensure efficient region propagation.

Algorithm Advantages: Computational Efficiency: The stack mechanism prevents duplicate pixel checks common in traditional region growing, making it suitable for large-scale image processing. This approach reduces time complexity by avoiding repeated scans of already-processed regions. Low Memory Footprint: Only a single stack needs maintenance instead of a global pixel queue. Memory allocation can be optimized by implementing a dynamically resizing stack that grows only as needed. Broad Adaptability: The algorithm can be adapted for color and multi-channel images by customizing similarity criteria (such as texture patterns or gradient comparisons). Developers can extend the similarity function to include Euclidean distance calculations in color spaces or texture feature matching.

Application Scenarios: Medical image segmentation, remote sensing image analysis, and other domains requiring high real-time performance where computational efficiency is critical.