Image Segmentation Algorithm Based on Active Contour Model

Image segmentation algorithms based on active contour models represent a fundamental approach in image processing. These algorithms operate through iterative contour extraction and boundary refinement, enabling precise separation and identification of distinct regions within an image. Implementation typically involves energy functional minimization (e.g., using gradient descent or variational methods) and curve evolution techniques like level-set formulations. The accompanying paper in the compressed package elaborates on algorithmic foundations—including mathematical models for internal/external energy terms, numerical implementation schemes using finite differences, and performance validation through metrics like Dice coefficient and boundary displacement error. Widely applied in computer vision, this methodology supports object detection tasks through dynamic contour adaptation, image analysis via region-based segmentation, and biomedical imaging applications requiring subpixel accuracy.