ETACM: an encoded-texture active contour model for image segmentation with fuzzy boundaries

Active contour models (ACMs) have been widely used in image segmentation to segment objects. However, when it comes to segmenting images with severe intensity inhomogeneity, most current frameworks do not perform well, which can make it difficult to achieve the desired results. To address this issue, a decision-making model is proposed, which involves using enhanced local direction pattern (ELDP) and local directional number pattern (LDNP) texture descriptors to create an encoded-texture ACM. The principal component analysis (PCA) is then used to optimize the two encoded images and reduce the correlations before they are fused. To further improve the performance of the encoded-texture ACM, a function of minimizing energy globally (FMEG) is suggested by applying the vector-valued exploration technique from a non-convex surface to region-based ACMs. This approach enables the development of a model capable of directly building complex decision boundaries. The experimental results show that the proposed encoded-texture ACM outperforms many recent frameworks in terms of robustness and accuracy for segmenting images with intensity inhomogeneity, fuzzy boundaries, and noise. Therefore, the suggested approach provides a more effective and efficient solution to the problem of image segmentation, particularly for challenging images.