3. Conclusion

In this chapter, two novel methods for VOI ranking are developed to classify brain PET images better. The first approach consists in ranking VOIs using ROC curves and quantifies the ability of a VOI to classify HC from AD subjects thanks to the area under curve for AUC.

The second approach which uses multilevel features is proposed to address the PET brain classification problem. Three levels of features are extracted from PET brain images and ranked in order to feed a SVM. Different models are trained by using different types of features. The final decision is made through the majority voting of different models' outputs. According to experiments on ADNI dataset, the proposed method can improve the performance of AD and MCI diagnosis when compared with those state-of-the-art methods which are also developed under FDG-PET.

To go further in computer-aided diagnosis tasks, other features like texture and gradient computed on VOIs have to be joined to first order statistical parameters in order to enrich information. Modern machine learning based on deep learning on neural network will be included in our future work.
