# Supervoxels-Based Histon as a New Alzheimer's Disease Imaging Biomarker
As a result, in the last two decades, the use of imaging modalities has gone from being a mere secondary instrument in the diagnosis of Alzheimer's to one of the main tools. Major advances in neuroimaging have provided opportunities to study neurological-related diseases. Thus, resting-state functional magnetic resonance imaging (fMRI) [10], imaging the intrinsic functional brain connectivity, or the use of PET radiotracers developed to allow the in vivo visualization of tau aggregates and A\(\beta\) plaques ([11; 12]), have become relevant biomarkers for Alzheimer's disease research. However, despite these advances, FDG positron emission tomography (PET) and magnetic resonance imaging (MRI) are still extensively used in AD-related studies, especially the latter, given its wide availability, non-invasive nature and relative absence of patient discomfort. However, early-stage changes in Alzheimer's disease are subtle and it is difficult to distinguish patterns by conventional radiological evaluation. Therefore, it remains difficult to establish reliable biomarkers for the diagnosis and monitoring of disease progression, especially in the early stages. This has led to the development of numerous automatic methods for the assessment of brain atrophy. There is a large body of research published on MRI neuroimaging-based computer-aided classification of AD (see [13] or [14]). Taking into account the work presented in [15], these methods can be grouped into three categories, depending on the type of characteristics that are used to assess the structural variation and how they are extracted. Thus, there are methods based on density maps, either using the whole brain as a unit or relying on a parcellation; methods derived from the study of the cortical surface, also in an overall or local way; and methods based only on the examination of specific regions of the brain. Density mapping methods look for atrophy patterns by using white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) mapping generated by voxel-based morphometry (VBM) methods [16]. The direct classification of these features is carried on using either support vector machines (SMV) in [17] and [18] or the programming boosting method (LPBM) [19]. Other works rely on different kinds of feature reduction methods, supervised or unsupervised, in order to reduce the height dimensionality of the feature space. In [20], WM and GM density map dimensions are reduced by the mean of principal component analysis (PCA) and the result is used to train an SVM based classifier. In [21], the reduction in the dimensions of GM maps is proposed only using the intensity distribution of voxels of GM maps as features. Another way of feature selection relies on the use of a cerebral partitioning atlas to obtain regional measurements of the anatomical features to question the presence of abnormal tissue areas as in [22] or [23]. Methods derived from the cortical surface use subtle changes extracted at the vertex-level from a cortical surface, represented primarily as cortical thickness measurements. As in density mapping methods, these measurements can be used directly [24] or processed for the reduction of dimensionality. Some examples on the latter category are found in the work presented in [25] where the cortical surface is modeled using three-dimensional meshes and the cortical thickness is extracted by parametrizing these meshes or the method presented in [26], where thickness data from the cortical surface data are converted into a frequency domain and the dimensionality is reduced by filtering out high-frequency components. The methods of the third category analyse specific regions in the brain. The main approach in these methods implies the use of biomarkers extracted from the hippocampus [27; 28] as volume and/or shape or textural features.