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Diagnostic features of Alzheimer's disease extracted from PET sinograms

Texture analysis of positron emission tomography (PET) images of the brain is a very difficult task, due to the poor signal to noise ratio. As a consequence, very few techniques can be implemented successfully. We use a new global analysis technique known as the Trace transform triple features. This technique can be applied directly to the raw sinograms to distinguish patients with Alzheimer's disease (AD) from normal volunteers. FDG-PET images of 18 AD and 10 normal controls obtained from the same CTI ECAT-953 scanner were used in this study. The Trace transform triple feature technique was used to extract features that were invariant to scaling, translation and rotation, referred to as invariant features, as well as features that were sensitive to rotation but invariant to scaling and translation, referred to as sensitive features in this study. The features were used to classify the groups using discriminant function analysis. Cross-validation tests using stepwise discriminant function analysis showed that combining both sensitive and invariant features produced the best results, when compared with the clinical diagnosis. Selecting the five best features produces an overall accuracy of 93% with sensitivity of 94% and specificity of 90%. This is comparable with the classification accuracy achieved by Kippenhan et al (1992), using regional metabolic activity.