Medical Image Registration using a Graph Theoretic Method
Medical Image Processing
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Image registration is an important problem for image processing and computer vision with many applications. IR techniques attempt to map corresponding features between two images. The problem is particularly difficult in medical image analysis as anatomy may be subject to elastic deformation. This research considers this problem in the context of graph matching. Firstly, weighted Region Adjacency Graphs (RAGs) are constructed from each image using an approach based on the morphological watershed. Over-segmentation problems arising from the watershed technique are solved by revisiting the conce...
Image registration is an important problem for image processing and computer vision with many applications. IR techniques attempt to map corresponding features between two images. The problem is particularly difficult in medical image analysis as anatomy may be subject to elastic deformation. This research considers this problem in the context of graph matching. Firstly, weighted Region Adjacency Graphs (RAGs) are constructed from each image using an approach based on the morphological watershed. Over-segmentation problems arising from the watershed technique are solved by revisiting the concept of watershed saliency based on a graph of the mosaic image. Secondly, The vertices of the RAG represent salient regions in the image and the (weighted) edges represent the relationship (bonding) between each region. Correspondences between images are then determined using a weighted graph matching method. Finally, Graph matching is considered one of the most complex problems in computer science, due to its combinatorial nature.
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