1. Introduction

Alzheimer's disease (AD) is a degenerative and incurable brain disease which is considered the main cause of dementia in elderly people worldwide. At present, there are around 90 million people who have been diagnosed with AD, and it is estimated that the number of AD patients will reach 300 million by 2050 [1, 2]. The diagnosis of this disease is done by clinical, neuroimaging, and neuropsychological assessments. Neuroimaging evaluation is based on nonspecific features such as cerebral atrophy, which appears very late in the progression of the disease.

Advances in neuroimaging biomarkers help to identify AD in its prodromal stage, mild cognitive impairment (MCI) [3]. Therefore, developing new approaches for early and specific recognition of AD is of crucial importance [4]. Positron emission tomography (PET) is nowadays widely used in neuroscience to characterize brain molecular mechanisms involved in healthy and pathological models [5]. Applied to the brain, PET imaging provides a noninvasive evaluation of various biomarkers, such as the metabolic rate of glucose, the cerebral blood flow, and the neurotransmission, but also the evaluation of some pathological processes such as the neuroinflammation, amyloid deposits, and more recently tubulinassociated unit (TAU) aggregates. In this line, PET using 18-fluoro-deoxy-glucose (18FDG) has been proposed as a biomarker of Alzheimer's disease [6–10]. 18F-FDG-PET provides 3D-volumetric brain imaging of the cerebral metabolic rate of glucose, thought to reflect the synaptic activity and thus the functional brain. However, these abnormalities, evident on average within a group of patients, or in individual cases with advanced disease, can be individually more difficult to confirm in a purely visual interpretation, particularly in the earliest stages of the disease when the current treatment (and those under development) would be yet the most effective. This visual interpretation is in addition subjective and also highly impacted by the experience of the physician. Individual, quantitative, and computer-aided approaches are thus needed for medical management of brain diseases. There has been a growing interest in using the cerebral glucose metabolism rate for AD classification and prediction of conversion from MCI to AD [11–13]. Four main groups of methods have been studied: voxels as feature (VAF)-based [14], discriminative voxel selection-based [15, 16], atlas-based [17–20], and projection-based methods [21]. Recently deep learning and more specifically convolutional neural networks (CNN) and recurrent neural networks (RNN) have been investigated for image classification in the case of Alzheimer's disease computer-aided diagnosis. These approaches show better results than many of the machine learning techniques that have been proposed for recognition tasks [22–27].

In this chapter, a description on the main steps of a computer-aided diagnosis system for AD based on 18F-FDG-PET brain images is given. Section 2 is devoted to data acquisition and preprocessing. Section 3 describes computed features extracted from brain PET images. A feature selection description is reported in Section 4. In Section 5 classification step is explained. Finally a conclusion and future work are given.
