Level Set Region Based Image Segmentation Using Lattice Boltzmann Method

Solving Level Set Equation (LSE) by using the classical methods, mostly based on finite differences approximations, costs a lot of computer time particularly when processing high dimensional large-scale data. In recent years, the Lattice Boltzmann Method (LBM) has attracted much attention as a fast alternative approach for solving LSE. The LBM is explicit and highly parallelizable. In this paper, we use the LBM to solve the LSE, we propose an Unsigned Pressure Force (UPF) based on region attribute, which can effectively and efficiently stop the contour at weak blurred edges. The experiments on synthetic and real images illustrate the performance of the proposed method.

I. INTRODUCTION

In image processing, segmentation is an important and difficult task which aims to partition a given image into several regions or to detect an object of interest. The Level Set Method (LSM) is part of the whole family of Active Contours Methods (ACM). The key idea that started the level set fanfare was the Hamilton Jacobi approach, i.e., a time-dependent equation for a moving surface. This was first done in the seminal work of Osher and Sethian [1]. In two dimensions, LSM represents a closed curve in the plane as the zero level set of a three-dimensional function . For instance, starting with a curve around the object to be detected, the curve moves toward its interior normal and has to stop on the boundary of the object. Two approaches are used to stop the evolving curve on the boundary of desired object, we can either use an edge indicator depending on the gradient of the image like in classical snakes and active contour models ([10], [11], [12], [13]); or use some regional attributes to stop the evolving curve on the actual boundary. The latter is more robust against noise and can detect objects without edges [9].