Background: Image registration is to put two or more images into the same coordinate system with anatomic correspondences. It is the basis for studying across-subject commonality or differences, for studying longitudinal image changes (e.g., disease progression or normal development), for aligning multi-modal images and fusing their complementary information, and for abnormality detection (e.g., comparing abnormal with normal).

Algorithms. We have developed the Deformable Registration via Attribute Matching and Mutual-Saliency Weighting (DRAMMS) deformable (non-rigid) medical image analysis. Its core concepts are two-fold: (a) in places where we can find correspondences, we characterize each voxel with rich texture information (multi-scale and multi-orientation texture features), so we can find correspondences more accurately; (b) in places where correspondences are missing (e.g., when registering histology cut to MRI, or when registering a normal atlas to a tumor-bearing image), the algorithm should automatically sense the missing correspondences, and automatically assign different confidence to different image regions, and let the registration be driven by regions where reliable correspondences can be established. 

Validations. The publicly-available DRAMMS algorithm and software has been extensively validated in different organs (brain, cardiac, prostate, breast images), in normal and disease, in different registration settings (longitudinal, across-subject, multi-modality; 2D and 3D), and in different ages (fetal, neonatal, and up to 100 years). Results were quantitatively compared to 14 other state-of-the-art image registration algorithms, for accuracy, generality, and robustness.

Applications.

Software Release:

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