18 March 2014 Early detection of Alzheimer's disease using histograms in a dissimilarity-based classification framework
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Abstract
Classification methods have been proposed to detect early-stage Alzheimer’s disease using Magnetic Resonance images. In particular, dissimilarity-based classification has been applied using a deformation-based distance measure. However, such approach is not only computationally expensive but it also considers large-scale alterations in the brain only. In this work, we propose the use of image histogram distance measures, determined both globally and locally, to detect very mild to mild Alzheimer’s disease. Using an ensemble of local patches over the entire brain, we obtain an accuracy of 84% (sensitivity 80% and specificity 88%).
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Anne Luchtenberg, Anne Luchtenberg, Rita Simões, Rita Simões, Anne-Marie van Cappellen van Walsum, Anne-Marie van Cappellen van Walsum, Cornelis H. Slump, Cornelis H. Slump, } "Early detection of Alzheimer's disease using histograms in a dissimilarity-based classification framework", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 903502 (18 March 2014); doi: 10.1117/12.2042670; https://doi.org/10.1117/12.2042670
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