2.2.1 Image dataset

Two datasets are presented in this chapter and used to evaluate the two approaches described in the following. The local database (Table 1) consists in 18F-FDG-PET scans that were collected from the "La Timone" University Hospital, in the Nuclear Medicine Department (Marseille, France). The local database image enrolled 171 adults 50–90 years of age, including 81 patients with AD and 61 health control (HC) and 29 mild cognitive impairment (MCI). HC were free from neurological/psychiatric disease and cognitive complaints and had a normal brain MRI. AD subjects exhibited NINCDS-ADRDA [28] clinical criteria for probable AD.


Table 1.

Demographic and clinical information of subjects of the local database.


#### Table 2.

Demographic and clinical information of subjects of ADNI database.

The second used dataset (Table 2) was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial MRI, PET, other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of MCI and early AD.

Therefore, 272 post-processed baseline FDG-PET data were obtained from ADNI, including 94 subjects with AD, 88 subjects with MCI, and 90 NC subjects.

#### 2.2.2 Image preprocessing

Image comparison with brains from different subjects is difficult due to the complexity and anatomical variations of brain structures. For that purpose image data were preprocessed into three steps: spatial normalization, smoothing, and intensity normalization. Spatial normalization was done by registration at voxel level using SPM8 software [11]. The data was spatially normalized onto the Montreal Neurological Institute atlas (MNI). These images were then smoothed using a Gaussian filter with an 8 mm full width at half maximum (FWHM) to increase the signal-to-noise ratio (SNR) [20]. After spatial normalization, intensity normalization was required in order to perform direct image comparison between different subjects. It consisted in dividing the intensity level of each voxel by the intensity level mean of the brain's global gray matter VOI.

### 2.3 Feature extraction

Each 3D PET brain image was segmented into 116 volumes of interest (VOIs) using an automated anatomical labeling (AAL) atlas. In this research project, the ability of VOIs to best distinguish AD from HC subjects was studied. Different parameter combinations for each VOI were used to select and rank VOIs according to their ability to separate AD group from HC one. The top-ranked VOIs were then introduced into a classifier. Several levels of features were extracted from VOIs. Two approaches have been investigated to achieve this goal.

### 2.3.1 Separation power factor

In the first approach, only features that extract the statistical information from each VOI are computed. First order statistics and the entropy are extracted from the histogram h(x) of each VOI:

$$h(\mathbf{x}) = \frac{number\text{ of }\text{ vowels in a given ROI with }\text{grel level}\,\mathbf{x}}{\text{total number of pixels in the given ROI}} \tag{1}$$

where x is a gray-level value of a voxel belonging to a VOI and lmin and lmax are the minimum and the maximum gray-level values in VOI, respectively.

$$P\_1 = \sum\_{\mathbf{x} = l\_{\rm min}}^{\mathbf{x} = l\_{\rm max}} \varkappa h(\mathbf{x}) \text{ Mean} \tag{2}$$

$$P\_2 = \sum\_{\mathbf{x} = l\_{\rm min}}^{\mathbf{x} = l\_{\rm max}} (\mathbf{x} - P\_1)^2 h(\mathbf{x}) \text{ Variance} \tag{3}$$

Alzheimer's Disease Computer-Aided Diagnosis on Positron Emission Tomography Brain Images… DOI: http://dx.doi.org/10.5772/intechopen.86114

$$P\_x = \sum\_{\mathbf{x} = l\_{\text{min}}}^{\mathbf{x} = l\_{\text{max}}} \frac{(\mathbf{x} - P\_1)^x h(\mathbf{x})}{(P\_1)^{x/2}} \text{ z } \mathbf{e} \{\mathbf{3}, 4\} \text{ Skewness and Kurtsosis} \tag{4}$$

$$P\_{\mathfrak{F}} = \sum\_{\mathfrak{x} = l\_{\text{min}}}^{\mathfrak{x} = l\_{\text{max}}} h(\mathfrak{x}) \log\_2(h(\mathfrak{x})) \text{ Entropy} \tag{5}$$

For a given VOI, we compute a set of parameter values {Pp|pϵ {1…K}} = {P1, P2, P3, P4, P5}. For easier readability, {Pp} is used instead of {Pp|pϵ {1…K}} in the following. HC and AD subjects are plotted in a N feature space, which represents a subset of {Pp}, denoted {Pp}N, N ≤ K among subsets. N-Dimensional sphere (N-D sphere) is created over the group of healthy subjects (HC) (N of length one correspond to an interval, N of length two correspond to a disk, N of length greater than or equal to three correspond to a sphere). The N-D sphere's center is the mass center of healthy subjects' distribution. Figure 2 shows the case of a VOI based on three parameters: the mean, P1; the standard deviation, P2; and the kurtosis, P4. It is a 3D sphere with N = {P1, P2, P4}. At various radii of the N-D sphere, we compute the true positives (TPR) and the false positive rates (FPR).

The ROC curve is created by plotting the true positive rate (TPR) vs. the false positive rate (FPR) for different radii of the N-D sphere settings as it is shown in Figure 3. The SPF is defined as the area under the ROC curve (AUC) and is within the range [0, 1].

#### 2.3.1.1 "Combination matrix" analysis

SPF is taken as a key factor to build "combination matrix." For each VOI, we compute this factor all over the combinations of parameter Pp with a length varying from 1 to K (number of feature parameters K = 5). "Combination matrix" is then built and contains 2K�1 columns.

Each line of this matrix represents a VOI, and each column represents the SPF (noted αv�N or αv�{Pp}N) computed on a subset {Pp}N of N elements of {Pp}.

#### Figure 2.

The separation between AD and HC groups relative to the region "Cingulum\_Post\_Left" with three parameters: the mean, the standard deviation, and the kurtosis.

#### Figure 3.

ROC curve obtained for the region "Cingulum\_Post\_Left" using three parameters: the mean, the standard deviation, and the kurtosis.

The "combination matrix" has then L lines and 2K�1 columns. L = 116 is the number of VOIs.

#### 2.3.1.2 "Combination matrix" 1: mono-parametric analysis

Mono-parametric analysis consists in ranking VOIs according to their higher values of SPF in the "combination matrix." The set of the top-ranked VOIs are selected for the classification step.

#### 2.3.1.3 "Combination matrix" 2: multi-parametric analysis

Multi-parametric analysis for "combination matrix" depends on both the combination of the parameters (subset of {Pp}) for each VOI and the combination of the VOIs, subsets of {Vv,|vϵ {1… L}}.

The procedure begins with the choice of the first two combination VOIs depending on all the possible combinations of VOIs of length 2 ({Vj, Vq}, 1 ≤ j, q ≤ L, j 6¼ q). Thereafter, it consists on an iterative process according to which we add one VOI at each step to a VOIs list, sequential forward selection (SFS) [29].

At each step of the iterative procedure, HC and AD subjects are plotted in a new feature space by combination of parameters for that selection of VOIs, and examine the SPF value based on this combination, which is named cumulated CSPF. The VOIs that provide the best cumulated SPF value are then added to the final VOI combination. Our algorithm stops when the same or a lower cumulated SPF is obtained.

#### 2.3.2 Multilevel representation

In the second approach, we investigate the multilevel feature representation, which considers both region properties (first level), connectivity between any pair Alzheimer's Disease Computer-Aided Diagnosis on Positron Emission Tomography Brain Images… DOI: http://dx.doi.org/10.5772/intechopen.86114

of VOIs (second level), and an overall connectivity between one region and the other regions (third level). The proposed method ranks regions from highly to slightly affected by the disease. A classifier selection strategy is proposed to choose a pair of classifiers with high diversity.
