Geometrical Regularization of Nonrigid Registration Using Local Anisotropic Structure and Joint Saliency Map

摘要：

Nonrigid image registration is a crucial task to study local structural/volumetric change in many applications. The presence and resection of brain tumor in pre- and intra-operative brain images will greatly distort local anatomical structure and introduce non-corresponding outlier features. This can cause serious conflicts in achieving a smoothly varying deformation field in nonrigid registration. In this paper, a novel regularizing scheme, which is based on local anisotropic structure and Joint Saliency Map weighted regularization, is introduced in registration to aim at handling local complex deformation and outliers. The sparse displacement is regularized to adapt its smoothness as well as orientation according to the local anisotropic structure. Moreover, the Joint Saliency Map guides the assignment of data certainty so that the reliable corresponding structural voxels are emphasized in regularization. The results show that our method is sufficiently accurate and effective to both local large deformation and outliers while maintaining an overall smooth deformation field.

关键词： Geometrical regularization joint saliency map normalized convolution anisotropic applicability function

作者: Jiawei Zhou Binjie Qin

作者单位: Department of Biomedical Engineering Shanghai Jiao Tong University

会议类型: 国际会议

会议名称: Third International Conference on Digital Image Processing(ICDIP 2011)(第三届数字图像处理国际会议)

会议地点: 成都

会议语种:英文

页码: 203-207

在线出版日期: 2011-04-15（万方平台首次上网日期，不代表论文的发表时间）