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This study will mainly concentrate on the novel mechanism used in the parallel and distributed computing model dynamic threshold neural P systems which involves the cooperative peaking of neurons inside a logical region. DTNP systems are relatively new and have a few intriguing properties. We intended to evaluate if these properties might be merged with existing NSCT in order to progress a special picture fusion technique for MMMI-multiple modality medical imaging which provides many advantages of NSCT-based multi-modal medical picture systems. The low-frequency NSCT coefficients will be extracted from the NSCT output for the DTNP process input used in the fusion rules. Additionally, WLE-INSML advantages are used for the extraction of high-frequency NSCT coefficients, and these coefficients are then used to create the fusion rules. A 12 pairs of multi-modality medical image pairs from an open dataset are used to evaluate the proposed fusion approach. The results of the quantitative as well as qualitative trials show the advantages of the suggested fusion process in terms of visual quality and fusion performance.

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Multi-modal Medical Novel Image Fusion by Using DTNP Systems

  • 会议论文

  • 发表日期：

    2025年

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  • G. Obulesu,
  • M. Ravi Kishore,
  • Shaik Karimullah,
  • Fahimuddin Shaik,
  • CH. Naga Raju

摘要

This study will mainly concentrate on the novel mechanism used in the parallel and distributed computing model dynamic threshold neural P systems which involves the cooperative peaking of neurons inside a logical region. DTNP systems are relatively new and have a few intriguing properties. We intended to evaluate if these properties might be merged with existing NSCT in order to progress a special picture fusion technique for MMMI-multiple modality medical imaging which provides many advantages of NSCT-based multi-modal medical picture systems. The low-frequency NSCT coefficients will be extracted from the NSCT output for the DTNP process input used in the fusion rules. Additionally, WLE-INSML advantages are used for the extraction of high-frequency NSCT coefficients, and these coefficients are then used to create the fusion rules. A 12 pairs of multi-modality medical image pairs from an open dataset are used to evaluate the proposed fusion approach. The results of the quantitative as well as qualitative trials show the advantages of the suggested fusion process in terms of visual quality and fusion performance.