MATLAB-Based Image Segmentation System with GUI Interface

This article presents a comparative analysis of multiple image segmentation algorithms implemented in MATLAB. The study covers threshold-based methods (including iterative thresholding, OTSU iteration, and histogram thresholding) and region-based approaches (region growing and split-and-merge techniques). The system features a complete GUI interface that enables interactive algorithm testing and parameter optimization. Let's examine each algorithm and interface component in detail.

First, we introduce threshold-based segmentation methods. These techniques separate image regions by setting appropriate threshold values. The iterative thresholding method employs an optimization loop that continuously updates the threshold value until convergence criteria are met, typically implemented using MATLAB's while-loops and mean intensity calculations. OTSU's method automatically determines the optimal threshold by maximizing inter-class variance through histogram analysis, leveraging MATLAB's graythresh function for efficient computation. Histogram thresholding analyzes the image's intensity distribution to identify natural valleys as separation points, often utilizing MATLAB's histcounts and findpeaks functions for automatic threshold detection.

Next, we explore region-based segmentation approaches. These methods partition images into homogeneous regions based on similarity criteria. The region growing algorithm starts with seed points and progressively merges neighboring pixels satisfying similarity constraints, implemented using queue structures and neighborhood connectivity checks (4-connected or 8-connected). The split-and-merge technique employs a quad-tree decomposition strategy where regions are recursively divided until homogeneity criteria are met, then adjacent similar regions are merged back together using MATLAB's regionprops and bwconncomp functions for efficient region analysis.

Finally, we present the GUI interface implementation. The graphical user interface provides intuitive controls for algorithm selection, parameter adjustment, and real-time result visualization. Built using MATLAB's App Designer or GUIDE framework, the interface incorporates image display components (axes), interactive sliders for threshold adjustments, and push buttons for executing different segmentation algorithms. The GUI enables side-by-side comparison of different methods and facilitates parameter tuning through immediate visual feedback.

Through detailed examination of these algorithms and interface components, we gain comprehensive understanding of MATLAB-based multi-algorithm image segmentation comparison. This implementation demonstrates practical application of image processing theory while providing a framework for method evaluation and parameter optimization. We hope this article offers valuable insights for image segmentation research and application development!