A hybrid active contour model based on pre-fitting energy and adaptive functions for fast image segmentation

In this study, a hybrid active contour model driven by pre-fitting energy with an adaptive edge indicator function and an adaptive sign function is proposed. The key idea of employing the pre-fitting energy is to define two pre-fitting functions to calculate mean intensities of two sub-regions separated from the selected local region based on pre-calculated median intensity of the selected local region before the curve evolves, which saves a huge amount of computation cost. In addition, a brand-new single-well potential function and its associated evolution speed function are put forward to enable evolution process to converge faster as well as more robust. Experimental outcomes indicate that this model is competent to obtain motion boundaries of different targets effectively and efficiently. Compared with traditional models and recently developed models, this model not only reduces the CPU running time and iteration number significantly as well as improves segmentation accuracies (Dice similarity coefficient (DSC) and Jaccard similarity coefficient (JSC)), but also exhibits insensitivity to initialization.(c) 2022 Elsevier B.V. All rights reserved.