基于最优Atlas多模态图像的非刚性配准分割算法
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摘要
提出了一种基于最优Atlas图像搜索和局部加权B样条变换的全自动非刚性分层配准分割感兴趣区域(ROI)方法。实验结果表明,所提算法配准的ROI准确度达到95.6%,归一化互信息值为1.8432,均方根误差为1.12%,相关系数提高了18.33%。相比其他配准方法,所提方案的配准精度及准确度明显提升,对临床辅助诊断有重要意义。
A fully automatic and non-rigid hierarchical registration separation ROI(Region of Interest) method based on optimal Atlas image search and local weighted B spline transform is proposed. The experimental results show that the accuracy of the registration of the proposed algorithm is 95.6%, the normalized mutual information value is 1.8432, the root mean square error is 1.12%, and the correlation coefficient is increased by 18.33%. Compared with other registration methods, the registration accuracy and precision of this registered method have obviously improved, which is of great significance for clinical assistant diagnosis.
A fully automatic and non-rigid hierarchical registration separation ROI(Region of Interest) method based on optimal Atlas image search and local weighted B spline transform is proposed. The experimental results show that the accuracy of the registration of the proposed algorithm is 95.6%, the normalized mutual information value is 1.8432, the root mean square error is 1.12%, and the correlation coefficient is increased by 18.33%. Compared with other registration methods, the registration accuracy and precision of this registered method have obviously improved, which is of great significance for clinical assistant diagnosis.
引文
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[24] Wang J.Research and application of nonrigid medical image registration based on B-spline and mutual information[D].Taiyuan:Taiyuan University of Technology,2017.汪军.基于B样条和互信息的非刚性医学图像配准的研究与应用[D].太原:太原理工大学,2017.
[2] Tektonidis M,Rohr K.Diffeomorphic multi-frame non-rigid registration of cell nuclei in 2D and 3D live cell images[J].IEEE Transactions on Image Processing,2017,26(3):1405-1417.
[3] Bansal R,Staib L H,Chen Z,et al.Entropy-based dual-portal-to-3-DCT registration incorporating pixel correlation[J].IEEE Transactions on Medical Imaging,2003,22(1):29-49.
[4] Kornaropoulos E N,Zacharaki E I,Zerbib P,et al.Optimal estimation of diffusion in DW-MRI by high-order MRF-based joint deformable registration and diffusion modeling[C].IEEE Conference on Computer Vision and Pattern Recognition Workshops,2016:618-625.
[5] Samant S,Nanda P K,Ghosh A.Noisy brain MR and CT image registration using MRF model[C].IEEE Region 10 Conference,2016:1258-1261.
[6] Zhang Y H,Lu P,Liu X Y,et al.A modified MRF segmentation of brain MR images[C].International Congress on Image and Signal Processing,BioMedical Engineering and Informatics,2017:1-5.
[7] Dong L N,Xu H X,Wen X B.Multi-scale joint segmentation and registration of image based on MRF and graph cut[J].Journal of Tianjin University of Technology,2016,32(6):35-41.董丽娜,徐海霞,温显斌.基于MRF和图割的多尺度图像分割和配准同步方法[J].天津理工大学学报,2016,32(6):35-41.
[8] Swierczynski P,Papiez B W,Schnabel J A,et al.A level-set approach to joint image segmentation and registration with application to CT lung imaging[J].Computerized Medical Imaging and Graphics,2017,65:58-68.
[9] Wang C,Shu Q,Yang Y X,et al.Quick registration algorithm of point clouds using structure feature[J].Acta Optica Sinica,2018,38(9):0911005.王畅,舒勤,杨赟秀,等.利用结构特征的点云快速配准算法[J].光学学报,2018,38(9):0911005.
[10] Paquin D,Levy D,Xing L.Hybrid multiscale landmark and deformable image registration[J].Mathematical Biosciences and Engineering,2007,4(4):711-737.
[11] Peng X M,Chen W F,Ma Q.Fast elastic image registration method based on B-splines[J].Computer Engineering and Applications,2006,42(11):186-189.彭晓明,陈武凡,马茜.基于B样条的快速弹性图像配准方法[J].计算机工程与应用,2006,42(11):186-189.
[12] Jia H J,Yap P T,Shen D G.Iterative multi-atlas-based multi-image segmentation with tree-based registration[J].NeuroImage,2012,59(1):422-430.
[13] Hu H.Hippocampus segmentation based on multi-atlas registration[D].Guangzhou:Southern Medical University,2014.胡昊.基于多图谱配准的海马体自动分割方法研究[D].广州:南方医科大学,2014.
[14] Hua R,Pozo J M,Taylor Z A,et al.Multiresolution eXtended free-form deformations (XFFD) for non-rigid registration with discontinuous transforms[J].Medical Image Analysis,2017,36:113-122.
[15] Chen Y J,Zhang J W,Wei Z H,et al.A variational model for simultaneous registration-segmentation to brain MR images[J].Journal of Computer-Aided Design & Computer Graphics,2007,19(2):215-220.陈允杰,张建伟,韦志辉,等.同时配准-分割脑MR图像的耦合变分模型[J].计算机辅助设计与图形学学报,2007,19(2):215-220.
[16] Kuss A,Ksensow K,Verl A.Deformable shape registration using surface-based free-form deformations in robotic welding[C].IEEE International Conference on Advanced Intelligent Mechatronics,2017:885-890.
[17] Pawar A,Zhang Y J,Jia Y,et al.Adaptive FEM-based nonrigid image registration using truncated hierarchical B-splines[J].Computers & Mathematics with Applications,2016,72(8):2028-2040.
[18] Zheng W N,Bo P B,Liu Y,et al.Fast B-spline curve fitting by L-BFGS[J].Computer Aided Geometric Design,2012,29(7):448-462.
[19] Qi P,Zhou W,Han J Z.A method for stochastic L-BFGS optimization[C].IEEE International Conference on Cloud Computing and Big Data Analysis,2017:156-160.
[20] Ji H Z,Li Y S,Dong E Q,et al.A non-rigid image registration method based on multi-level B-spline and L2-regularization[J].Signal,Image and Video Processing,2018,12(6):1217-1225.
[21] Shen T W,Cheung K W.Improved local optimization for adaptive bases non-rigid image registration[C].IEEE International Symposium on Circuits and Systems,2016:1526-1529.
[22] Li Z,Pan X Z,Sun S F,et al.Overview of three dimensional point cloud registration constraints[J].Microcomputer & its Applications,2016,35(23):12-14.李准,潘幸子,孙水发,等.三维点云配准约束条件综述[J].微型机与应用,2016,35(23):12-14.
[23] Wu L Y,Xiong L,Du S Y,et al.Robust and precise affine registration for faces based on fast affine template matching and modified affine iterative closet point algorithm[J].Acta Optica Sinica,2018,38(2):0210004.吴礼洋,熊磊,杜少毅,等.基于快速仿射模板匹配和改进仿射迭代最近点算法的人脸稳健精确仿射配准[J].光学学报,2018,38(2):0210004.
[24] Wang J.Research and application of nonrigid medical image registration based on B-spline and mutual information[D].Taiyuan:Taiyuan University of Technology,2017.汪军.基于B样条和互信息的非刚性医学图像配准的研究与应用[D].太原:太原理工大学,2017.