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Image registration plays an increasingly important role in the field of medical image processing given the plurality
of images often acquired from different sensors, time points, or viewpoints. Landmark-based registration schemes
represent the most popular class of registration methods due to their simplicity and high accuracy. Previous
studies have shown that these registration schemes are sensitive to the number and location of landmarks.
Identifying important landmarks to perform an accurate registration remains a very challenging task. Current
landmark selection methods, such as feature-based approaches, focus on optimization of global transformation
and may have poor performance in recovering local deformation, e.g. subtle tissue changes caused by tumor
resection, making them inappropriate for registering pre- and post-surgery images as a small cancerous region
will be deformed after removing a tumor. In this work, a novel method is introduced to estimate optimal landmark
configurations. An important landmark configuration that will be used as a training landmark set was learned
for an image pair with a known deformation. This landmark configuration can be considered as a collection
of discrete points. A generic transformation matrix between a pair of training landmark sets with different
deformation locations was computed via an iterative close point (ICP) alignment technique. A new landmark
configuration was determined by simply transforming the training landmarks to the current displacement location
while preserving the topological structure of the configuration of landmarks. Two assumptions are made: 1) In
a new pair of images the deformation is approximately the same size and has only been spatially relocated in the
image, and that by a simple affine transformation one can identify the optimal configuration on this new pair of
images; and 2) The deformation is of similar size and shape on the original pair of images. These are reasonable
assumptions in many cases where one seeks to register tumor images at multiple time points following application
of therapy and to evaluate changes in tumor size. The experiments were conducted on 286 pairs of synthetic
MRI brain images. The training landmark configurations were obtained through 2000 iterations of registration
where the points with consistently best registration performance were selected. The estimated landmarks greatly
improved the quality metrics compared to a uniform grid placement scheme and a speeded-up robust features
(SURF) based method as well as a generic free-form deformation (FFD) approach. The quantitative results
showed that the new landmark configuration achieved 95% improvement in recovering the local deformation
compared to 89% for the uniform grid placement, 79% for the SURF-based approach, and 10% for the generic
FFD approach.
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