摘要
脑核磁共振(MR)图像因需要偏移场矫正,传统分割方法很难获得准确的分割结果。针对这一问题,首先构造一组基函数拟合偏移场以保证偏移场的光滑特性,再将其融入到高斯概率密度函数中,结合统计分类准则建立脑MR图像的分割和偏移场矫正的能量方程,最后将该能量方程引入到三相位水平集的变分框架中得到脑MR图像的分割和偏移场矫正的耦合模型。实验表明该方法在得到准确的分割结果同时还可以得到较好的恢复结果。
Due to the correction of the bias field, it is hard to obtain the accurate segmentation results of magnetic resonance（MR） images using traditional methods. In this paper, a set of basis functions is constructed firstly to fit the smoothness bias field; then the information of the bias field is introduced to the Gaussian density function, and according to the statistics classification rule, we define the energy function for the brain MR image segmentation and bias field correction. At last, this energy function is incorporated into a three-phase level set framework to propose our model. Compared with other approaches, our experiments demonstrate that our method not only can obtain accurate segmentation results but also can restore images better.
出处
《中国图象图形学报》
CSCD
北大核心
2011年第11期2017-2023,共7页
Journal of Image and Graphics
基金
国家自然科学基金项目(60802039
61071146
61003209)
高等学校博士点学科点专项基金项目(200802880018)
南京理工大学资助科研重大专项项目(2010ZDJH07)
南京理工大学自主科研专项计划资助项目(2010ZYT070)
南京理工大学优秀博士培养计划项目
江苏省高校自然科学研究项目(10KJB520012)
江苏省研究生培养创新工程项目
关键词
偏移场
三相位
水平集
图像分割
bias field
three-phase
level set
image segmentation
