Active contour model based on fuzzy C-means and local pre-fitting energy for image segmentation

Active contour models (ACM) have achieved remarkable results in image segmentation. However, existing ACMs have some shortcomings, such as over-dependence on the initial contour, complex parameter adjustments, and difficulty in balancing segmentation accuracy and speed. To further improve the performance of ACM, this paper proposes an active contour model based on fuzzy c-means and local pre-fitting function (FLPF). Firstly, a linear weighted image is defined as a sample for the fuzzy c-means (FCM) clustering algorithm to pre-fit the image intensity. A pre-processing operation is proposed to improve the FCM clustering algorithm and increase the computation speed. Then, second-order differential data-driven terms based on the local pre-fitting energy are designed to guide the curve evolution rapidly and adaptively toward the target boundary. In addition, adaptive regularization functions are constructed to optimize and normalize the data-driven terms and level set functions, which improves the robustness of the proposed model. Finally, an improved parameter tuning framework based on the deep learning algorithm YOLOv5 is proposed for the FLPF model to achieve automated parameter adjustments. Compared to the other six models, our model has advantages in segmentation speed and accuracy, reducing the average segmentation time by 77.5% and improving the average segmentation accuracy by more than 7.6% of 10 images.