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首页> 外文会议>International Conference on Signal Processing, Communication, Power and Embedded System >Multi Feature Extraction of CBMIR using Fuzzy Inference System
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Multi Feature Extraction of CBMIR using Fuzzy Inference System

机译：CBMIR使用模糊推理系统的多特征提取

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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.
机译：在近年来，使用分析和动态系统建模方法来解决生物和医学问题的情况大大增加。这些技术在生理系统中的应用可以生成对这些系统的一般性质的深度和多样化的理解，以及管理它们的复杂过程。医疗工程研究工程技术概念在仪器开发，材料，诊断和治疗装置，人工器官和其他医疗设备的应用。遵循基于内容的CBIR的医学图像的方法。本研究的目的是访问这些方法的稳定性进行医学图像检索。该方法使用基于纹理的检索GLCM（灰度共发生矩阵）和局部二进制模式（LBP）。本文制定的工作对软科学的建模和仿真努力建立了贡献，例如生物医学工程。模糊推理系统（FIS）构成了另一种定性推理范式。模糊控制器已经成功地应用于各种医疗系统，因此在这方面，他们已经提及了与医疗系统相关的模糊系统领域的一些更重要的贡献已经获得并报道。为此，提出了一种基于推理和模糊性的定性方法，以解决处理这些类型的系统中固有的一些方法。为了说明在该研究中获得的结果，使用了两种生物医学图像检索的特征提取技术，并且使用具有83.33的灵敏度值的模糊推理系统观察到更好的性能。

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