Edge-Based Local and Global Energy Active Contour Model Driven by Signed Pressure Force for Image Segmentation

Image segmentation is a tedious task that suffers from constraints, such as blurred or weak edges and intensity inhomogeneity. Active contour models (ACMs), including edge-and region-based methods, are extensively used for image segmentation. Each of these methods has its pros and cons that affect image-segmentation accuracy and CPU processing time. This study combines local and global region-based fitting energies and uses statistical image information to drag contours toward object boundaries, thus overcoming image inhomogeneity. The bias field, the region affected by image artifacts, is calculated and added with the local fitting energy model to capture inhomogeneous object boundaries. Furthermore, the combined local and global statistical information is appended with the edge-indicator function to rapidly move the contour over objects with strong edges, thereby avoiding boundary leakage. A region-based length term is driven by the signed pressure force (SPF) function that evolves the curve on either the outer or inner side of the object, depending on its sign. The SPF function contributes to achieving a smoother version of energy minimization over gradient descent flow. The proposed ACM is applied to multiple synthetically generated, and medical images, together with online available public databases: the PH2 database, the skin-cancer-mnist-ham10000 THUS10000 database, and the specific images from PascalVOC2007 database. All the experiments confirm the better segmentation accuracy and improved time potency of the proposed methodology over previous level set-based approaches.