Active Contour Model for Image Segmentation With Dilated Convolution Filter

Abstract

ACMs have been demonstrated to be highly suitable as image segmentation models for computer vision tasks. Among other ACM, the local region-based models show better performance because they extract the local information regarding intensity in the neighborhood and embed it into the energy minimization function to guide the active contour to the boundary of the desired object. However, the online segmentation of noisy and inhomogeneous is still a challenging task for local region-based ACM models. To overcome this challenge, This paper proposes a novel region-based active contour model, named active contour model with local dilated convolution filter (ACLD). The ACLD integrates local image information in the form of a signed pressure force function. Then, a Gaussian kernel is applied using dilated convolution instead of discrete convolution for regularizing the level set formulation. Finally, instead of using a constant stopping condition, the ACLD automatically stops at the object boundaries. The proposed model shows improved image segmentation results visually combined with less computational time in the case of synthetic and natural images compared with the state-of-the-art models. Further, on the ISIC2017 dataset, the ACLD yields segmentation results with the highest accuracy.

An overview of intelligent image segmentation using active contour models

“…Geometric ACMs [59][60][61][62] are mainly on the basis of partial differential equations (PDEs) and variational method, whose essence is to continuously evolve toward the direction of energy minimum under the constraint of image information and give conditions. The segmentation process is generally as follows: a closed curve is initialized on the given image.…”