Image Segmentation Using CV Model with Prior Shape Constraints and Narrowband Method

In the field of image processing and computer vision, the CV model (Chan-Vese model) based segmentation method represents a classical region-based segmentation technique, particularly suitable for scenarios where target and background share similar grayscale values but possess regular shape boundaries. This method integrates prior shape constraints with narrowband level set approaches, significantly enhancing segmentation accuracy and computational efficiency.

The core principle of the CV model relies on energy minimization criteria, where the evolution curve approximates target boundaries by optimizing level set functions. Unlike traditional edge-based methods, it doesn't depend on gradient information but utilizes regional grayscale statistics for segmentation. When incorporating prior shape constraints, the model learns approximate geometric features of targets (such as inherent shapes in medical images like hearts or blood vessels), effectively avoiding local minima issues and demonstrating stronger robustness.

The narrowband method serves as an acceleration strategy for the CV model. While traditional level set methods require computation across the entire image domain, the narrowband approach focuses only on regions near the evolving curve, dynamically updating narrowband ranges to reduce computational load. For instance, in medical image segmentation, prior shapes can be initialized as elliptical contours of organs, with narrowband computations confined to 10-15 pixel widths around contours, maintaining precision while achieving speed improvements of tens of times.

Typical implementation scenarios include: Left ventricle segmentation in medical images, where prior shapes are set as annular structures Road extraction from aerial imagery, combining narrowband method with linear road prior knowledge Industrial part defect detection, incorporating standard part templates as shape constraints

Key implementation aspects involve initializing level set functions using signed distance functions and updating them through finite difference schemes. The energy functional typically combines region-based terms (mean intensities inside/outside curves) with shape penalty terms. The method's advantage lies in its adaptability to blurred boundaries and noise, though practitioners should note that prior shape matching degree significantly impacts final results. Future developments could integrate deep learning for automated prior feature extraction, further enhancing generalization capabilities.