胸部多模MRI图像的结构补偿配准方法
|
摘要
针对胸部多模磁共振(MRI)图像间结构失配问题,提出一种基于结构补偿的配准方法.首先基于相似变换约束进行浮动图像与参考图像的预配准;然后结合图像分割和形态学处理方法对预配准图像进行结构补偿,构造与参考图像组织结构一致的浮动模板;再将浮动模板与参考图像非刚性配准,提取配准变形场;最后将变形场应用于预配准图像,得到配准结果.以失配组织的Jaccard因子作为评价标准,采用模体图像和临床图像对提出的方法进行验证.结果表明在结构失配的胸部多模MRI图像中,本文方法比传统互信息法具有更好的配准性能.
A structure compensation-based registration method was proposed for chest multimodal magnetic resonance imaging(MRI) images with mismatched structures. First of all, floating image was pre-registered to reference image in similarity transformation. Then the pre-registration image was compensated by segmenting and morphological processing methods, a floating template, whose tissue structures matched with the reference image's structures, was generated. After that, the floating template was non-rigidly registered to the reference image, the deformation field was extracted from the registration. Finally, the deformation field was applied to the pre-registration image, the registered image was generated. The Jaccard index of the mismatched structures was used as measurement, frame models and clinical images were used to validate the proposed method. Results indicate that the proposed method is more effective than the traditional mutual information based method in chest multimodal MRI images with mismatched structures.
A structure compensation-based registration method was proposed for chest multimodal magnetic resonance imaging(MRI) images with mismatched structures. First of all, floating image was pre-registered to reference image in similarity transformation. Then the pre-registration image was compensated by segmenting and morphological processing methods, a floating template, whose tissue structures matched with the reference image's structures, was generated. After that, the floating template was non-rigidly registered to the reference image, the deformation field was extracted from the registration. Finally, the deformation field was applied to the pre-registration image, the registered image was generated. The Jaccard index of the mismatched structures was used as measurement, frame models and clinical images were used to validate the proposed method. Results indicate that the proposed method is more effective than the traditional mutual information based method in chest multimodal MRI images with mismatched structures.
引文
[1]WangDaohui.Theclinical-radiologicalandpathological analysisofpneumonic-typeadenocarcinomaofthelung[D].Hangzhou:Zhejiang University, 2016(in Chinese)(王道会.肺炎型肺腺癌的临床、影像及病理特征分析[D].杭州:浙江大学, 2016)
[2]Nagle S K, Puderbach M, Eichinger M, et al. Magnetic resonance imaging of the lung:cystic fibrosis[M]//Medical Radiology. Heidelberg:Springer, 2017:1-15
[3]Callahan M J, MacDougall R D, Bixby S D, et al. Ionizing radiation from computed tomography versus anesthesia for magnetic resonance imaging in infants and children:patient safety considerations[J]. Pediatric Radiology, 2017, 48(1):21-30
[4]Stahl M, Wielpütz M O, Graeber S Y, et al. Comparison of lung clearanceindexandmagneticresonanceimagingforassessment of lung disease in children with cystic fibrosis[J]. American Journal of Respiratory and Critical Care Medicine, 2017,195(3):349-359
[5]Zhao Yongming, Zhang Su, Chen Yazhu. Medical image registrationalgorithminimage-guidednoninvasivesurgery[J].JournalofComputerAidedDesign&ComputerGraphics,2005, 17(12):2665-2669(in Chinese)(赵永明,张素,陈亚珠.非介入式手术导航中医学图像配准算法[J].计算机辅助设计与图形学学报,2005,17(12):2665-2669)
[6]Ferrante E, Paragios N. Slice-to-volume medical image registration:asurvey[J].MedicalImageAnalysis,2017,39:101-123
[7]Yang F, Ding M Y, Zhang X M, et al. Non-rigid multi-modal medical image registration by combining L-BFGS-B with cat swarmoptimization[J].InformationSciences,2015,316:440-456
[8]KasiriK,FieguthP,ClausiDA.Self-similaritymeasurefor multi-modalimageregistration[C]//ProceedingsoftheIEEE International Conference on Image Processing. Los Alamitos:IEEE Computer Society Press, 2016:4498-4502
[9]Pradhan S, Patra D. Enhanced mutual information based medical image registration[J]. IET Image Processing, 2016, 10(5):418-427
[10]Legg P A, Rosin P L, Marshall D, et al. Feature neighbourhood mutual information for multi-modal image registration:an application to eye fundus imaging[J]. Pattern Recognition, 2015,48(6):1937-1946
[11]Sakai T, Sugiyama M, Kitagawa K, et al. Registration of infraredtransmissionimagesusingsquared-lossmutualinformation[J]. Precision Engineering, 2015, 39:187-193
[12]Viola P, Wells III W M. Alignment by maximization of mutual information[J]. International Journal of Computer Vision, 1997,24(2):137-154
[13]Chan T F, Vese L A. Active contours without edges[J]. IEEE Transactions on Image Processing, 2001, 10(2):266-277
[14]Rueckert D, Sonoda L I, Hayes C, et al. Nonrigid registration usingfree-formdeformations:applicationtobreastMRimages[J]. IEEE Transactions on Medical Imaging, 1999, 18(8):712-721
[15]Johnson H J, McCormick M M, Ibanez L. The ITK software guide book 1:introduction and development guidelines-volume 1[M]. New York:Kitware Inc, 2015
[16]Shamonin D P, Bron E E, Lelieveldt B P F, et al. Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer’s disease[J]. Frontiers in Neuroinformatics,2014, 7:Article No.50
[17]Heinrich M P, Jenkinson M, Bhushan M, et al. MIND:modality independent neighbourhood descriptor for multi-modal deformable registration[J]. Medical Image Analysis, 2012, 16(7):1423-1435
[2]Nagle S K, Puderbach M, Eichinger M, et al. Magnetic resonance imaging of the lung:cystic fibrosis[M]//Medical Radiology. Heidelberg:Springer, 2017:1-15
[3]Callahan M J, MacDougall R D, Bixby S D, et al. Ionizing radiation from computed tomography versus anesthesia for magnetic resonance imaging in infants and children:patient safety considerations[J]. Pediatric Radiology, 2017, 48(1):21-30
[4]Stahl M, Wielpütz M O, Graeber S Y, et al. Comparison of lung clearanceindexandmagneticresonanceimagingforassessment of lung disease in children with cystic fibrosis[J]. American Journal of Respiratory and Critical Care Medicine, 2017,195(3):349-359
[5]Zhao Yongming, Zhang Su, Chen Yazhu. Medical image registrationalgorithminimage-guidednoninvasivesurgery[J].JournalofComputerAidedDesign&ComputerGraphics,2005, 17(12):2665-2669(in Chinese)(赵永明,张素,陈亚珠.非介入式手术导航中医学图像配准算法[J].计算机辅助设计与图形学学报,2005,17(12):2665-2669)
[6]Ferrante E, Paragios N. Slice-to-volume medical image registration:asurvey[J].MedicalImageAnalysis,2017,39:101-123
[7]Yang F, Ding M Y, Zhang X M, et al. Non-rigid multi-modal medical image registration by combining L-BFGS-B with cat swarmoptimization[J].InformationSciences,2015,316:440-456
[8]KasiriK,FieguthP,ClausiDA.Self-similaritymeasurefor multi-modalimageregistration[C]//ProceedingsoftheIEEE International Conference on Image Processing. Los Alamitos:IEEE Computer Society Press, 2016:4498-4502
[9]Pradhan S, Patra D. Enhanced mutual information based medical image registration[J]. IET Image Processing, 2016, 10(5):418-427
[10]Legg P A, Rosin P L, Marshall D, et al. Feature neighbourhood mutual information for multi-modal image registration:an application to eye fundus imaging[J]. Pattern Recognition, 2015,48(6):1937-1946
[11]Sakai T, Sugiyama M, Kitagawa K, et al. Registration of infraredtransmissionimagesusingsquared-lossmutualinformation[J]. Precision Engineering, 2015, 39:187-193
[12]Viola P, Wells III W M. Alignment by maximization of mutual information[J]. International Journal of Computer Vision, 1997,24(2):137-154
[13]Chan T F, Vese L A. Active contours without edges[J]. IEEE Transactions on Image Processing, 2001, 10(2):266-277
[14]Rueckert D, Sonoda L I, Hayes C, et al. Nonrigid registration usingfree-formdeformations:applicationtobreastMRimages[J]. IEEE Transactions on Medical Imaging, 1999, 18(8):712-721
[15]Johnson H J, McCormick M M, Ibanez L. The ITK software guide book 1:introduction and development guidelines-volume 1[M]. New York:Kitware Inc, 2015
[16]Shamonin D P, Bron E E, Lelieveldt B P F, et al. Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer’s disease[J]. Frontiers in Neuroinformatics,2014, 7:Article No.50
[17]Heinrich M P, Jenkinson M, Bhushan M, et al. MIND:modality independent neighbourhood descriptor for multi-modal deformable registration[J]. Medical Image Analysis, 2012, 16(7):1423-1435