An improved level set method for brain MR images segmentation and bias correction

作者：Chen Yunjie^*; Zhang Jianwei; Macione Jim

来源：Computerized Medical Imaging and Graphics, 2009, 33(7): 510-519.

DOI：10.1016/j.compmedimag.2009.04.009

• Bias field
• Image segmentation
• Level set

Intensity inhomogeneities cause considerable difficulty in the quantitative analysis of magnetic resonance (MR) images. Thus, bias field estimation is a necessary step before quantitative analysis of MR data can be undertaken. This paper presents a variational level set approach to bias correction and segmentation for images with intensity inhomogeneities. Our method is based on an observation that intensities in a relatively small local region are separable, despite of the inseparability of the intensities in the whole image caused by the overall intensity inhomogeneity. We first define a localized K-means-type clustering objective function for image intensities in a neighborhood around each point. The cluster centers in this objective function have a multiplicative factor that estimates the bias within the neighborhood. The objective function is then integrated over the entire domain to define the data term into the level set framework. Our method is able to capture bias of quite general profiles. Moreover, it is robust to initialization, and thereby allows fully automated applications. The proposed method has been used for images of various modalities with promising results.

• 出版日期2009-10
• 单位南京信息工程大学