Active Contours Using Additive Local and Global Intensity Fitting Models for Intensity Inhomogeneous Image Segmentation.

Shafiullah Soomro,Kwang Nam Choi,Toufique Ahmed Soomro,Jeong Heon Kim,Farhan Akram

doi:10.1155/2016/9675249

Abstract

This paper introduces an improved region based active contour method with a level set formulation. The proposed energy functional integrates both local and global intensity fitting terms in an additive formulation. Local intensity fitting term influences local force to pull the contour and confine it to object boundaries. In turn, the global intensity fitting term drives the movement of contour at a distance from the object boundaries. The global intensity term is based on the global division algorithm, which can better capture intensity information of an image than Chan-Vese (CV) model. Both local and global terms are mutually assimilated to construct an energy function based on a level set formulation to segment images with intensity inhomogeneity. Experimental results show that the proposed method performs better both qualitatively and quantitatively compared to other state-of-the-art-methods.

Highlights

Image segmentation is a basic and important problem in the areas of computer vision, pattern recognition, and image processing
The presence of noise, low contrast, and intensity inhomogeneity affects the accuracy of intensity based image segmentation methods
In medical image modalities, such as microscopy, computed tomography (CT), and magnetic resonance imaging (MRI), it manifests itself as a smooth intensity variation across the image during image acquisition process or because of outer interference

Summary

Introduction

Image segmentation is a basic and important problem in the areas of computer vision, pattern recognition, and image processing. Edge based methods deploy a force to deform a curve towards the object boundary of the given image by utilizing image gradient information These methods experience the ill effects of energy leakage. Traditional region based active contour methods work on an assumption that the given image is homogeneous [22, 27] They cannot properly segment images with intensity inhomogeneity. Li et al proposed an intensity inhomogeneous image segmentation method [5] based on LBF method known as multiplicative image segmentation model for intensity inhomogeneity It constructs an energy functional, which includes a bias field that models the smooth variations of intensity inhomogeneity. This paper presents a new region based active contour model for level set formulation with an additive formulation of energy functional using both local and global intensity fitting terms.

Theoretical Foundations

The Proposed Method

Experimental Analysis and Comparison

Discussion

Conclusion