Comparison of active contour models for image segmentation in X-ray coronary angiogram images

Abstract

Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. However, most present snake models cannot provide better capture range and evolution stop mechanism. This project presents a new external force for active contours, largely solving both problems. An extension of the gradient vector flow snake (GVF snake) method is presented. First, the adaptive balloon force has been developed to increase the GVF snake's capture range and convergence speed. Then, a dynamic GVF force is introduced to provide an efficient evolution-stop mechanism. In this way, we prevent the snake from breaking through the correct surface and locking to other salient feature points. The active contour models have been applied on X-ray coronary angiogram images. The segmentation results demonstrate the potential of improved GVF method is comparison with all previous active contour methods. Texture parameters have been calculated and results are compared with all active contour models.

Keywords:

• Image segmentation
• Level set
• GVF
• Adaptive balloon force
• Dynamic force
• Improved GVF
• Snakes
• Medical images