Specific Algorithms and Demonstrations for Region Filling

Region filling is a crucial technique in digital image processing, primarily used for repairing or filling enclosed areas within images. MATLAB provides a comprehensive set of tools and functions to implement region filling, with core algorithms typically based on morphological operations and connected component analysis.

### Basic Region Filling Algorithms

Seed Point Filling (Flood Fill) This algorithm starts from a given seed point and propagates outward to fill the region until encountering boundary colors. MATLAB's `imfill` function employs this filling method by default, making it suitable for filling enclosed areas in binary or grayscale images. The implementation uses queue/stack-based pixel traversal with boundary condition checks.

Morphological Filling This approach utilizes morphological operations (such as dilation and erosion) to fill holes. The core concept involves first identifying region boundaries, then performing iterative dilation operations combined with mask constraints to ensure filling only affects target regions. Key functions include `imfill` with 'holes' option and morphological reconstruction using `imreconstruct`.

Connected Component-Based Filling By computing connected components (using functions like `bwconncomp`), target pixels for filling can be identified, followed by logical operations to complete the filling process. This method effectively handles multiple disconnected regions through component labeling and region property analysis.

### Demonstration Approaches

Simple Filling Using `imfill` Suitable for binary images, where filling can be accomplished by simply specifying the target objects. Basic implementation: `filled_image = imfill(binary_image, 'holes')` or `filled_image = imfill(binary_image, locations)` for specific seed points.

Advanced Filling with Morphological Operations For complex images, preprocessing steps like edge detection or binarization can be applied first, followed by filling using structural elements (such as disk-shaped `strel`) to ensure smooth filling results. Example workflow: `se = strel('disk', radius); filled = imclose(binary_image, se)`.

Custom Filling Logic For irregular regions, developers can implement traversal algorithms to manually control filling conditions, such as constraint-based filling using pixel values or gradient changes. This typically involves iterative pixel scanning with conditional checks using MATLAB's matrix indexing capabilities.

Leveraging MATLAB's matrix computation advantages, region filling can be efficiently implemented and applied to various domains including medical imaging, remote sensing, and computer vision applications.