A Markov Random Field and Adaptive Regularization Embedded Level Set Segmentation Model Solving by Graph Cuts

Dengwei Wang

doi:10.1155/2019/8747385

Abstract

This paper presents a novel Markov random field (MRF) and adaptive regularization embedded level set model for robust image segmentation and uses graph cuts optimization to numerically solve it. Firstly, a special MRF-based energy term in the form of level set formulation is constructed for strong local neighborhood modeling. Secondly, a regularization constraint with adaptive properties is imposed onto the proposed model with the following purposes: reduce the influence of noise, force the power exponent of the regularization process to change adaptively with image coordinates, and ensure the active contour does not pass through the weak object boundaries. Thirdly, graph cuts optimization is used to implement the numerical solution of the proposed model to obtain extremely fast convergence performance. The extensive and promising experimental results on wide variety of images demonstrate the excellent performance of the proposed method in both segmentation accuracy and convergence rate.

Highlights

Image segmentation is the technology and process of dividing an image into several specific regions with unique properties
Among the family of image segmentation algorithms, there is a kind of method that occupies an absolute advantage, that is the geometric active contour models based on level set representation; such methods have a very rigorous mathematical foundation and are capable of handling the topological changes of the contour freely, which are difficult to be solved by the parametric active contour models, and it can simultaneously segment multiple targets and get a smooth and closed target contour
E edge-based models use the gradient information of the image to construct the driving force required for the evolution process. e geodesic active contour (GAC) model [1] proposed by Caselles et al is a typical representative, and it is one of the most successful and popular edge-based models; the driving force of its segmentation is derived from the intrinsic geometric measure of the input image

Summary

Introduction

Image segmentation is the technology and process of dividing an image into several specific regions with unique properties. E edge-based models use the gradient information of the image to construct the driving force required for the evolution process. In order to solve the problem of inhomogeneous image segmentation, Li et al [5] proposed a variational level set model based on local binary fitting (LBF) energy. To solve the above problems, this paper presents a novel MRF [9] and adaptive regularization embedded level set model for robust image segmentation and uses the graph cuts to numerically solve it. Is is due to the high dynamism of the flames and the variability of the topological structure; if parametric curves or surfaces are used to describe this change of flame, it will inevitably encounter great difficulties This problem can be solved well by introducing the level set function framework. The speed function F is usually determined jointly by the image data and the level set function φ

The Proposed Model

Energy Terms

Experimental Results and Discussions

Conclusions