An Efficient Multi-Scale Local Binary Fitting-Based Level Set Method for Inhomogeneous Image Segmentation

Dengwei Wang

doi:10.1155/2018/6269214

Abstract

An efficient level set model based on multiscale local binary fitting (MLBF) is proposed for image segmentation. By introducing multiscale idea into the LBF model, the proposed MLBF model can effectively and efficiently segment images with intensity inhomogeneity. In addition, by adding a reaction diffusion term into the level set evolution (LSE) equation, the regularization of the level set function (LSF) can be achieved, thus completely eliminating the time-consuming reinitialization process. In the implementation phase, in order to greatly improve the efficiency of the numerical solution of the level set segmentation model, we introduce three strategies: The first is the additive operator splitting (AOS) solver which is used for breaking the restrictions on time step; the second is the salient target detection mechanism which is used to achieve full automatic initialization of the LSE process; the third is the sparse filed method (SFM) which is used to restrict the groups of pixels that need to be updated in a small strip region. Under the combined effect of these three strategies, the proposed model achieves very high execution efficiency in the following aspects: contour location accuracy, speed of evolution convergence, robustness against initial contour position, and robustness against noise interference.

Highlights

In the process of researching and applying images, people tend to be interested only in certain parts of the image, often referred to as target or foreground; they generally correspond to specific regions of the image that have unique properties
In order to remove the restriction on the time step and obtain fast convergence, we introduce the fast additive operator splitting (AOS) [34] scheme to solve the terms which are marked by operator div in (17); the existence of δ φ leads some differences between our terms and the processing objects of AOS
Some of these images come from the literatures on image segmentation, and some come from the Internet

Summary

Introduction

In the process of researching and applying images, people tend to be interested only in certain parts of the image, often referred to as target or foreground; they generally correspond to specific regions of the image that have unique properties. We propose a level set model based on MLBF by introducing the idea of multiscale modeling into the original LBF model and apply it to the practice of inhomogeneous image segmentation. An implicit scheme called AOS [20] is utilized to break the time-step limitation of traditional explicit schemes Under this numerical implementation strategy, the iterative process can take a larger time step, so the evolution curve can quickly converge to the real target contour. By performing a CV [12] model-based segmentation operation on the output of the salient object detection algorithm, we can get the initial curve required for the evolution process.

Background

The Proposed Segmentation Model

Implementation Strategies

Experimental Results and Discussions

Input image

Conclusion