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Information Journal Paper

Title

A NEW HYBRID FUZZY INTELLIGENT FILTER FOR MEDICAL IMAGE NOISE REDUCTION

Pages

  47-53

Abstract

 Medical imaging comprises different imaging modalities and processes to image human body for diagnostic and treatment purposes and, therefore has an important role in the improvement of public health in all population groups. In this paper, we present an intelligent hybrid NOISE REDUCTION FILTER which is based on NEURO-Fuzzy systems. It is especially beneficial in medical image noise reduction. First stage we feed the input image into four general NOISE REDUCTION FILTERs in parallel. These general filters are: mean filter, median filter, weighted median filter and the adaptive median filter. At the second stage we give the output of the above filters as input into a NEURO-Fuzzy system. As expected, the ability of NEURO-Fuzzy systems in encoding human knowledge and using non-deterministic knowledge, allow us to achieve much more noise reduction from the input images. We implement the proposed method and use it for reduction of noise from a set of MEDICAL IMAGES affected with high noise density. Experimental results show that the idea is considerably effective.

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  • Cite

    APA: Copy

    ALIAKBARI DEHKORDI, SOMAYE, GHASEMZADEH, MOHAMMAD, & DERHAMI, VALI. (2014). A NEW HYBRID FUZZY INTELLIGENT FILTER FOR MEDICAL IMAGE NOISE REDUCTION. COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING), 5(3), 47-53. SID. https://sid.ir/paper/202944/en

    Vancouver: Copy

    ALIAKBARI DEHKORDI SOMAYE, GHASEMZADEH MOHAMMAD, DERHAMI VALI. A NEW HYBRID FUZZY INTELLIGENT FILTER FOR MEDICAL IMAGE NOISE REDUCTION. COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING)[Internet]. 2014;5(3):47-53. Available from: https://sid.ir/paper/202944/en

    IEEE: Copy

    SOMAYE ALIAKBARI DEHKORDI, MOHAMMAD GHASEMZADEH, and VALI DERHAMI, “A NEW HYBRID FUZZY INTELLIGENT FILTER FOR MEDICAL IMAGE NOISE REDUCTION,” COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING), vol. 5, no. 3, pp. 47–53, 2014, [Online]. Available: https://sid.ir/paper/202944/en

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