Brain tumor identification from MRI imaging using fuzzy inference

Abstract

The radiological aspect of brain tumors is most often suggestive of the diagnosis. However, the radiological presentation can be very variable and sometimes misleading. Moreover, other pathologies, tumor or otherwise, may have a similar radiological presentation and which are essentially abscesses or inflammatory lesions. The problem is posed in the interpretation of the magnetic resonance imaging (MRI). In this context, the nature of tissues, which have a non-homogeneous structure, without apparent regularity, whose scheduling varies according to whether it is healthy or not. Particularly statistical methods are used due to the random nature of the tissues. This allows extracting the characteristic parameters that will make it possible to diagnose the nature and gravity of the tumor. These models are structural models (adapted repetition of macrostructures), models by probabilistic laws (for the analysis of microstructures) and since it is difficult to delimit the boundary between the zones of regularity and non-regularity, sometimes we call upon to analysis techniques by treating the data as a multi-fractal signal (turbulence signal analysis techniques). In view of this complexity, an artificial intelligence technique is proposed in this analysis of the texture resulting from MRI imaging. A fuzzy inference system is established. As fuzzy logic deals with uncertainty, its application in this area is adequate. The proposed system consists of four input variables (Texture nature, age, gender, genetic factor) and an output variable that expresses the degree of tumor confirmation. A rule base is established from the recorded values encompassing all possible combinations. The established algorithm permits to introduce randomly values on input factors of the system to read predict the degree of cerebral tumor confirmation.