3D Local Binary Pattern for PET image classification by SVM, Application to early Alzheimer disease diagnosis

Christophe Montagne 1 Andreas Kodewitz 1 Vincent Vigneron 1 Virgile Giraud 1 Sylvie Lelandais 1
1 IBISC - Informatique, Biologie Intégrative et Systèmes Complexes
Abstract : The early diagnostic of Alzheimer disease by non-invasive technique becomes a priority to improve the life of patient and his social environment by an adapted medical follow-up. This is a necessity facing the growing number of affected persons and the cost to our society caused by dementia. Computer based analysis of Fluorodeoxyglucose PET scans might become a possibility to make early diagnosis more efficient. Temporal and parietal lobes are the main location of medical findings. We have clues that in PET images these lobes contain more information about Alzheimer's disease. We used a texture operator, the Local Binary Pattern, to include prior information about the localization of changes in the human brain. We use a Support Vector machine (SVM) to classify Alzheimer's disease versus normal control group and to get better classification rates focusing on parietal and temporal lobes.
Keywords : Alzheimer disease Feature extraction Local binary pattern Machine learning Positron emission tomographic images
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Christophe Montagne, Andreas Kodewitz, Vincent Vigneron, Virgile Giraud, Sylvie Lelandais. 3D Local Binary Pattern for PET image classification by SVM, Application to early Alzheimer disease diagnosis. 6th International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS 2013), Feb 2013, Barcelona, Spain. pp.145--150. ⟨hal-00762837⟩

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