This paper presents a relevance vector regression(RVR) based on parametric approach to the bias field estimation in brain magnetic resonance(MR) image segmentation. Segmentation is a very important and challenging tas...This paper presents a relevance vector regression(RVR) based on parametric approach to the bias field estimation in brain magnetic resonance(MR) image segmentation. Segmentation is a very important and challenging task in brain analysis,while the bias field existed in the images can significantly deteriorate the performance.Most of current parametric bias field correction techniques use a pre-set linear combination of low degree basis functions, the coefficients and the basis function types of which completely determine the field. The proposed RVR method can automatically determine the best combination for the bias field, resulting in a good segmentation in the presence of noise by combining with spatial constrained fuzzy C-means(SCFCM)segmentation. Experiments on simulated T1 images show the efficiency.展开更多
基金National Natural Science Foundation of Chinagrant number:10971190+1 种基金National Natural Science Foundation of Chinagrant number:11001239 and 11101365
文摘This paper presents a relevance vector regression(RVR) based on parametric approach to the bias field estimation in brain magnetic resonance(MR) image segmentation. Segmentation is a very important and challenging task in brain analysis,while the bias field existed in the images can significantly deteriorate the performance.Most of current parametric bias field correction techniques use a pre-set linear combination of low degree basis functions, the coefficients and the basis function types of which completely determine the field. The proposed RVR method can automatically determine the best combination for the bias field, resulting in a good segmentation in the presence of noise by combining with spatial constrained fuzzy C-means(SCFCM)segmentation. Experiments on simulated T1 images show the efficiency.
