Multi feature extraction of CBMIR using Fuzzy Inference System

During recent years, the use of analysis and dynamical system modeling methods to solve biological and medical problems has considerably increased. The application of these techniques to physiological systems can generate a deep and diversified understanding of the general nature of these systems and of the complex processes that govern them. Medical Engineering studies the application of engineering and technology concepts to the development of instrumentation, materials, diagnostic and therapeutic devices, artificial organs, and other medical equipment. A content based approach is followed for medical images of CBIR. The purpose of this study is to access the stability of these methods for medical image retrieval. The methods used texture based retrieval GLCM (gray level co-occurrence matrix) and local binary pattern (LBP). The work developed in this paper establishes a contribution to modeling and simulation efforts in soft sciences, such as biomedical engineering. Fuzzy Inference System (FIS) constitute yet another qualitative reasoning paradigm. Fuzzy controllers have successfully been applied to various medical systems, and they therefore deserve to be mentioned in this context. Some of the more important contributions to the field of fuzzy systems as related to medical systems have been obtained and reported. To this end, a qualitative methodology based on inference and fuzziness is proposed that addresses some of the methodology inherent in dealing with these types of systems. To illustrate the results obtained in this research effort, two kinds of feature extraction technique of biomedical image retrieval are being used and better performance is observed using Fuzzy Inference system with 83.33 value of sensitivity as compared to ANN.