A Modified Brain MR Image Segmentation and Bias Field Estimation Model Based on Local and Global Information.

Wang Cong,Jin Li,Xingcheng Ma,Hong Liang,Jianhua Song,Lei Wang,Kuan Luan

doi:10.1155/2016/9871529

Wang Cong, Jin Li + Show 5 more

Open Access

https://doi.org/10.1155/2016/9871529

Copy DOI

Abstract

Because of the poor radio frequency coil uniformity and gradient-driven eddy currents, there is much noise and intensity inhomogeneity (bias) in brain magnetic resonance (MR) image, and it severely affects the segmentation accuracy. Better segmentation results are difficult to achieve by traditional methods; therefore, in this paper, a modified brain MR image segmentation and bias field estimation model based on local and global information is proposed. We first construct local constraints including image neighborhood information in Gaussian kernel mapping space, and then the complete regularization is established by introducing nonlocal spatial information of MR image. The weighting between local and global information is automatically adjusted according to image local information. At the same time, bias field information is coupled with the model, and it makes the model reduce noise interference but also can effectively estimate the bias field information. Experimental results demonstrate that the proposed algorithm has strong robustness to noise and bias field is well corrected.

Highlights

As an important medical treatment technique, magnetic resonance imaging (MRI) has greatly enhanced the efficiency of the doctor’s diagnosis and avoided the numerous anatomical surgeries or diagnostic laparotomies
Li et al [17] presented a scheme of bias field estimation and image segmentation based on coherent local intensity clustering (CLIC); the Gaussian kernel function is incorporated into the weighting of the local neighborhood in the model to measure the spatial distance between neighbor pixels in fuzzy C-means (FCM) objective function
The proposed method is applied to the 1.5T- and 3T-weighted brain MR images, and Figure 1 illustrates the experimental results

Summary

Introduction

As an important medical treatment technique, magnetic resonance imaging (MRI) has greatly enhanced the efficiency of the doctor’s diagnosis and avoided the numerous anatomical surgeries or diagnostic laparotomies. Zhao et al [11] proposed an efficient fuzzy clustering scheme, in which a nonlocal constraint item is introduced into the objective function of the improved FCM, and the global structure information of the image plays a very important role in the process of image segmentation. Li et al [17] presented a scheme of bias field estimation and image segmentation based on coherent local intensity clustering (CLIC); the Gaussian kernel function is incorporated into the weighting of the local neighborhood in the model to measure the spatial distance between neighbor pixels in FCM objective function. CLIC model can correct the bias field, this algorithm only utilizes grayscale distribution information in local neighborhood without considering global information of the whole image. The bias field was coupled with the model as a multiplicative additional field, and bias field can be effectively estimated from MR images to reduce the impact of intensity inhomogeneity for image segmentation

Related Work

The Proposed Method

Experimental Results

Conclusion