A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histogram Analysis Using a Scale-Space Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video parsing, retrieval and browsing: an integrated and content-based solution
Proceedings of the third ACM international conference on Multimedia
Generalized Affine Invariant Image Normalization
IEEE Transactions on Pattern Analysis and Machine Intelligence
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Deformable templates using large deformation kinematics
IEEE Transactions on Image Processing
Multisource data registration based on NURBS description of contours
International Journal of Remote Sensing
Diffusion Tensor Image Registration Using Tensor Geometry and Orientation Features
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Mapping Growth Patterns and Genetic Influences on Early Brain Development in Twins
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Multimodal Image Registration by Information Fusion at Feature Level
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Learning best features and deformation statistics for hierarchical registration of MR brain images
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Neonatal brain MRI segmentation by building multi-region-multireference atlases
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Image histogram constrained SIFT matching
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
A robust hybrid method for nonrigid image registration
Pattern Recognition
Improved segmentation of meteorite micro-CT images using local histograms
Computers & Geosciences
MBIA'12 Proceedings of the Second international conference on Multimodal Brain Image Analysis
Constructing fiber atlases for functional ROIs via fMRI-Guided DTI image registration
MBIA'12 Proceedings of the Second international conference on Multimodal Brain Image Analysis
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We previously presented an image registration method, referred to hierarchical attribute matching mechanism for elastic registration (HAMMER), which demonstrated relatively high accuracy in inter-subject registration of MR brain images. However, the HAMMER algorithm requires the pre-segmentation of brain tissues, since the attribute vectors used to hierarchically match the corresponding pairs of points are defined from the segmented image. In many applications, the segmentation of tissues might be difficult, unreliable or even impossible to complete, which potentially limits the use of the HAMMER algorithm in more generalized applications. To overcome this limitation, we have used local spatial intensity histograms to design a new type of attribute vector for each point in an intensity image. The histogram-based attribute vector is rotationally invariant, and importantly it also captures spatial information by integrating a number of local intensity histograms from multi-resolution images of original intensity image. The new attribute vectors are able to determine the corresponding points across individual images. Therefore, by hierarchically matching new attribute vectors, the proposed method can perform as successfully as the previous HAMMER algorithm did in registering MR brain images, while providing more generalized applications in registering images of various organs. Experimental results show good performance of the proposed method in registering MR brain images, DTI brain images, CT pelvis images, and MR mouse images.