Functional magnetic resonance imaging: fuzzy inference analysis

Abstract

Aim: Nowadays, functional MRI is widely used in the study of brain functions. If it has the advantage of being non-invasive and allows delimiting the zones that are activated at each stimulus in 3D, it presents several deficiencies. On the psychotic studies, the zones which activate induce the analyst in error in view of the complexity and the differences between individuals and their states of anxiety. Even minimal movements of the head influence the result; the response of the vascular system signal is delayed after the stimulus...etc. A heavy numerical processing in particular in statistical analyzes is necessary to refine the images. Despite this, difficulties persist. Method:In this study, a fuzzy logic system in this analysis is proposed. Viewing the complexity of the system, the variables that define the constructed image are considered as inaccurate variables and therefore fuzzy variable. The motor and emotional stimulus, the parasites movements, the anxiety state are considerate as inputs system. The quality of image constructed is the output system. The data base constructed permits adjusting input variables for optimal image. Conclusion:The proposed system allows defining the optimal interaction of the different factors for an optimal image. Considering the variables of input and output as fuzzy variables thus imprecise, this makes it possible to overcome the deficiencies of the system

Laryngeal cancer imaging and its extension evaluation: Intelligent analysis of 100 cases diagnosed at Setif Hospital in Algeria

“…A fuzzy inference analysis is applied. [26] Fuzzy inference Historically, the principles of fuzzy logic are presented for the 1 st time in 1965 by Zadeh. [27] This mode of reasoning is an imitation of human reasoning.…”