This study is the result of a collaborative project between an engineering school Ecole Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne, Lab-STICC UMR CNRS 6285) and a Medical Research Group “Groupe d’Etude de Thrombose de Bretagne Occidentale” (EA 3878 GETBO) associated with the INvestigation Network On Venous Thrombo-Embolism (INNOVTE) and the clinical investigation center (CIC Inserm 1408) of the Brest University Hospital (CHU - Brest). Our study considers the abnormalities of salivary glands. It focuses on the detection and characterization of the syndrome of Gougerot-Sjögren. To reach our goals, we are collecting ultrasonography (echogenicity) and elastography (stiffness) images of the salivary glands of several patients. This paper resumes our proposed preprocessing steps and approaches used to create and preprocess our database using the Canon Aplio system. Our approaches to characterize the salivary glands with ultrasound images are also described. We propose to extract features from these kinds of ultrasound images using two approaches: one based on wavelets (the scattering operators) and another one based on high-order statistics (multicorrelations). Then, the obtained features are analyzed using several classification technics (principal component analysis, k-means, and spectral clustering). Experimental results are presented and discussed. We should highlight the fact that our approach can reach an outstanding result that can be compared to experts’ outcomes.