European Journal of Molecular & Clinical Medicine

Abstract

Graph theory is a branch of discrete mathematics that deals with the connections among
entities. It has been proven to be a very beneficial and powerful mathematical tool and has a
wide range of applications to handle complex problems in various domains. The aim of this
work is two folds: first, to understand the basic notion of graph theory and second, to
emphasis the significance of graph theory through a real-time application used as a
representational form and characterization of brain connectivity network, as is machine
learning for classifying groups depending on the features extracted from images. This
application uses different techniques including preprocessing, correlations, features or
algorithms. This paper illustrates an automatic tool to perform a standard process using
images of the Magnetic Resonance Imaging (MRI) machine. The process includes preprocessing,
building the graph per subject with different correlations, atlas, relevant feature
extraction according to the literature, and finally providing a set of machine learning
algorithms that can produce analyzable results for physicians or specialists. Further, to
demonstrate the importance of graph theory, this article addresses the most common
applications for graph theory in various fields.