Quantile-based classification of Alzheimer's disease, frontotemporal dementia and asymptomatic controls from SPECT data
Geller D, Platsch G, Kornhuber J, Kuwert T, Merhof D (2017)
Publication Type: Conference contribution
Publication year: 2017
Publisher: Institute of Electrical and Electronics Engineers Inc.
Book Volume: 2017-January
Conference Proceedings Title: 2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop, NSS/MIC/RTSD 2016
Event location: Strasbourg, FRA
ISBN: 9781509016426
Abstract
Nuclear imaging techniques, namely single photon emission computed tomography (SPECT) and positron emission tomography (PET), are commonly used for the study of neurodegenerative diseases such as Alzheimer's disease (AD) and frontotemporal dementia (FTD). Many methods have been proposed to identify different types of dementia based on SPECT and PET images. In order to cope with the low number of datasets compared to the high number of independent variables (voxels of the dataset), they either perform a dimensionality reduction prior to classification, which implies identical influence of all available datasets, or try to extract the relevant variables for the prediction, which may be affected by statistical fluctuation resulting from mislabeled data or intrinsic noise within data samples. In order to overcome these limitations, this paper presents an alternative method for classification of SPECT image data of asymptomatic controls (HC), AD and FTD participants. The proposed method produces a voxel mask that weights or ignores voxels according to their relevance for classification. The algorithm is based on quantiles and is less sensitive to the non-Gaussian statistical distribution of the classes to separate, which is a very desirable in case of dementia classification. Special care is taken to assess the robustness of the proposed approach. The classification accuracy assessed with bootstrap resampling is presented and the robustness against outliers and misdiagnosed training samples is investigated and compared with a PCA-MVA based approach. As a result, the proposed approach shows comparable results with respect to robustness, but better classification accuracy than PCA-based approaches.
Authors with CRIS profile
Johannes Kornhuber
Lehrstuhl für Psychiatrie und Psychotherapie
Torsten Kuwert
Lehrstuhl für Klinische Nuklearmedizin
Involved external institutions
Germany (DE)How to cite
APA:
Geller, D., Platsch, G., Kornhuber, J., Kuwert, T., & Merhof, D. (2017). Quantile-based classification of Alzheimer's disease, frontotemporal dementia and asymptomatic controls from SPECT data. In 2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop, NSS/MIC/RTSD 2016. Strasbourg, FRA: Institute of Electrical and Electronics Engineers Inc..
MLA:
Geller, Dieter, et al. "Quantile-based classification of Alzheimer's disease, frontotemporal dementia and asymptomatic controls from SPECT data." Proceedings of the 2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop, NSS/MIC/RTSD 2016, Strasbourg, FRA Institute of Electrical and Electronics Engineers Inc., 2017.
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