Active contour model with adaptive weighted function for robust image segmentation under biased conditions

The segmentation of images under biased conditions such as low contrast, high-intensity inhomogeneity, and noise is a challenge for any image segmentation model. The ideal image segmentation model must be capable of segmenting images with maximum accuracy and a minimum false-positive rate under biased conditions. In this paper, we propose a region-based active contour model (ACM), called global signed pressure and K-means clustering based on local correntropy with the exponential family (GSLCE), to address segmentation challenges under biased conditions. An adaptive weighted function is formulated based on the global and local image differences such that a single weighted function can drive both the global and local intensities. Further, the Riemannian steepest descent method is used for convergence of the proposed GSLCE energy function, and a Gaussian kernel is applied for spatial smoothing to obviate the computationally expensive level-set re-initialization. The experimental results show that, compared with state-of-the-art ACMs, the proposed GSLCE model obtained the best visual image segmentation results for synthetic and real images under biased conditions. Further, the qualitative and quantitative experimental results validate that the proposed model outperforms the state-of-the-art ACMs by yielding higher values of performance metrics. Moreover, the proposed GSLCE model requires substantially less processing time compared to the state-of-the-art ACMs.