Dr. Tao Wan Profile

Dr. Tao Wan

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Publications (1)

Proceedings Article | 13 March 2013 Paper

A novel point-based nonrigid image registration scheme based on learning optimal landmark configurations

Tao Wan, B. Nicolas Bloch, Shabbar Danish, Anant Madabhushi

Proceedings Volume 8669, 866934 (2013) https://doi.org/10.1117/12.2007153

KEYWORDS: Image registration, Brain, Neuroimaging, Magnetic resonance imaging, Tumors, Medical imaging, Brain mapping, Tissues, Computer simulations, Image processing

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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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