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Multimodal image fusion improves the quality of fused images considerably. Preserving all important information from the source images using image fusion techniques without introducing additional distortions or flaws is possible. The multimodal fusion of medical images provides accurate clinical analysis and surgical development by providing an informative and qualitative image. The principal component analysis (PCA) with discrete wavelet transform (DWT) is the most common and effective approach for medical image fusion. Based on the prominent information in the input images, the DWT extracts the principal component rather than only taking average values of low-frequency components. A hybrid-PCNN and KNN method is also proposed for selecting which combination of modalities results in the best discrimination between groups. An MRI and PET fusion study of glioma diseases illustrates the fusion process. A preliminary clinical validation is achieved, and the results are presented and evaluated.

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Multimodality Fusion Technique Using Hybrid Pulse-Coupled Neural Network with Supervised Learning Classifier for Medical Images Diagnosis

  • 会议论文

  • 发表日期：

    2025年

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  • Khulood Abdel-Khaliq Al-Salim,
  • Mohammed Ayad Alkhafaji,
  • Ahmed J. Obaid

摘要

Multimodal image fusion improves the quality of fused images considerably. Preserving all important information from the source images using image fusion techniques without introducing additional distortions or flaws is possible. The multimodal fusion of medical images provides accurate clinical analysis and surgical development by providing an informative and qualitative image. The principal component analysis (PCA) with discrete wavelet transform (DWT) is the most common and effective approach for medical image fusion. Based on the prominent information in the input images, the DWT extracts the principal component rather than only taking average values of low-frequency components. A hybrid-PCNN and KNN method is also proposed for selecting which combination of modalities results in the best discrimination between groups. An MRI and PET fusion study of glioma diseases illustrates the fusion process. A preliminary clinical validation is achieved, and the results are presented and evaluated.