The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
Adaptive Stochastic Gradient Descent Optimisation for Image Registration
International Journal of Computer Vision
Optimization of mutual information for multiresolution image registration
IEEE Transactions on Image Processing
A Classifier to Detect Tumor Disease in MRI Brain Images
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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This article studies the possibility of detecting dementia in an early stage, using nonrigid registration of MR brain scans in combination with dissimilarity-based pattern recognition techniques. Instead of focussing on the shape of a single brain structure, we take into account the shape differences within the entire brain. Imaging data was obtained from a longitudinal, population based study of the elderly. A set of 29 subjects was identified, who were asymptomatic at the time of scanning, but were diagnosed as having dementia within 0.7 to 5 years after the scan, and a set of 29 age and gender matched healthy controls were selected. Each subject was registered to all other subjects, using a nonrigid registration algorithm. Based on statistics of the deformation field in the brain, a dissimilarity measure was calculated between each pair of subjects, yielding a 58 × 58 dissimilarity matrix. A kNN classifier was trained on the dissimilarity matrix and the performance was tested in a leave-one-out experiment. A classification accuracy of 81 % was attained (spec. 83%, sens. 79%). This demonstrates the potential of whole-brain intersubject dissimilarities to aid in early diagnosis of dementia.