Image Segmentation Algorithms

Implementation of Image Segmentation Using Two Algorithms

Watershed Algorithm:

Watershed segmentation is a mathematical morphology-based approach rooted in topological theory. Its fundamental concept treats digital images as topological terrain in geodesy, where pixel intensity values represent elevation altitudes. Local minima and their influence zones are termed catchment basins, with their boundaries forming watershed lines. The algorithm's mechanism can be visualized through an immersion simulation: pierce holes at each local minimum, gradually immerse the model in water, and as the water level rises, the influence zones expand outward. Barriers constructed at convergence points between catchment basins ultimately form the watershed segmentation. In MATLAB implementation, key functions like watershed() process gradient magnitude images, while preprocessing steps often involve morphological operations (imopen, imclose) to control over-segmentation by modifying regional minima using imextendedmin and imimposemin functions.

Genetic Algorithm:

Genetic Algorithms (GA) represent a global adaptive probabilistic search technique inspired by natural selection principles and genetic mechanisms from biological evolution, incorporating survival-of-the-fittest selection, crossover recombination, and mutation operations. The algorithm initiates with a randomly generated population of encoded solutions (chromosomes), where each chromosome's genotype (internal gene combination) determines its phenotypic expression. Implementation requires initial encoding from phenotype to genotype space. Through iterative generations, individuals are selected based on fitness evaluations (typically segmentation quality metrics like inter-region contrast), followed by genetic operators: crossover (combining parent chromosomes using techniques like single-point crossover) and mutation (introducing random variations through bit-flipping). This evolutionary process progressively yields populations with improved adaptability, where the decoded optimal chromosome from the final generation serves as the approximate solution. Common GA implementations for image segmentation involve encoding pixel memberships or region boundaries, with fitness functions evaluating segmentation coherence.

Beyond these algorithms, alternative image segmentation methods include clustering algorithms based on color/texture features (e.g., k-means clustering with color-space transformations) and edge-detection-based approaches (utilizing operators like Canny or Sobel with edge-linking techniques). Each algorithm possesses distinct advantages and application scopes, where selection depends on specific requirements such as image characteristics (textured vs. smooth regions), computational constraints, and desired segmentation granularity.