A new binary level set model using L0 regularizer for image segmentation

In this paper, a new model of image segmentation is proposed by considering L-0 regularizer term. A new energy function is formulated by utilizing Local Gaussian Distribution model based on binary level set function followed by introducing a L-0 gradient regularizer as regularizing term. Instead of zero level set, the binary level set function is applied to differentiate between foreground and background regions. The regularization function is used to calculate the interfaces between foreground sub-regions and regularization term L-0 which helps to evolve the curve. The proposed energy function is solved using minimization algorithm to achieve promising results in terms of segmentation accuracy. The different sets of experiments are performed on real and medical images. The proposed model provides higher segmentation accuracy results in less computational time compared to the other state-of-the-art models. Further, the results are found to be superior as compared to other existing models in terms of robustness to noise and intensity inhomogeneity. (C) 2020 Elsevier B.V. All rights reserved.