In order to create a single image known as a fused image, a method known as multimodal medical image fusionImage Fusion involves extracting information from many medical images. Clinical experts frequently employ fused image analysis for the rapid identification and treatment of serious disorders. Sugeno fuzzy inference systems (SFISSugeno Fuzzy Inference Systems (SFIS)), which are sophisticated fuzzy systems, are used in this work to combine multimodal medical images. The detailed information included in the input source images can be effectively transferred into the fused output image using SFISSugeno Fuzzy Inference Systems (SFIS)-based fusion. By reducing the amount of memory needed to store numerous photos, Image FusionImage Fusion not only offers superior information but also lowers storage costs. In comparison to current methods, the suggested work is efficient and produces better fused images. Additionally, the quality metrics EntropyEntropy (E), Mutual Information (MI), and Edge-based quality metre (QAB/F) are compared to the fused image. The proposed method's superiority is displayed and supported by both subjective and objective analysis.


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    Titel :

    Multimodal Medical Image Fusion Using the Sugeno Fuzzy Inference System


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Sharma, Sanjay (Herausgeber:in) / Subudhi, Bidyadhar (Herausgeber:in) / Sahu, Umesh Kumar (Herausgeber:in) / Tirupal, T. (Autor:in) / Shanavaj, K. (Autor:in) / Venusri, M. (Autor:in) / Susmitha, D. (Autor:in) / Sireesha, G. (Autor:in)

    Kongress:

    International Conference on Robotics, Control, Automation and Artificial Intelligence ; 2022 November 24, 2022 - November 26, 2022



    Erscheinungsdatum :

    2023-11-18


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch