Early detection of Alzheimer's disease using histograms in a dissimilarity-based classification framework

Anne Luchtenberg; Rita Simões; Anne-Marie van Cappellen van Walsum; Cornelis H. Slump

doi:10.1117/12.2042670

18 March 2014 Early detection of Alzheimer's disease using histograms in a dissimilarity-based classification framework

Anne Luchtenberg, Rita Simões, Anne-Marie van Cappellen van Walsum, Cornelis H. Slump

Proceedings Volume 9035, Medical Imaging 2014: Computer-Aided Diagnosis; 903502 (2014) https://doi.org/10.1117/12.2042670
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

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%).

Citation Download Citation

Anne Luchtenberg, Rita Simões, Anne-Marie van Cappellen van Walsum, and 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); https://doi.org/10.1117/12.2042670

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