A novel multimodality anatomical image fusion method based on contrast and structure extraction

Arbab Sufyan, Muhammad Imran*, Syed Attique Shah, Hamayoun Shahwani, Arbab Abdul Wadood

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    26 Citations (SciVal)


    Image modalities, such as computed tomography (CT), magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), and so on, reflect various levels of details about objects of interest that help medical practitioners to examine patients' diseases from different perspectives. A single medical image, at times, may not be sufficient for making a critical decision; therefore, providing detailed information from a different perspective may help in making a better decision. Image fusion techniques play a vital role in this regard by combining important details from different medical images into a single, information enhanced image. In this article, we present a novel weighted term multimodality anatomical medical image fusion method. The proposed method, as a first step, eliminates the distortions from the source images and afterward, extracts two pieces of crucial information: the local contrast and the salient structure. Both the local contrast and salient structure are later combined to obtain the final weight map. The obtained weights are then passed through a fast guided filter to remove the discontinuities and noise. Lastly, the refined weight map is fused with source images using pyramid decomposition to get the final fused image. The proposed method is accessed and compared both qualitatively and quantitatively with state-of-the-art techniques. The result illustrates the performance superiority and efficiency of the proposed method.
    Original languageEnglish
    Pages (from-to)324-342
    Number of pages19
    JournalInternational Journal of Imaging Systems and Technology
    Issue number1
    Publication statusPublished (VoR) - 27 Aug 2021


    • Image Fusion
    • Image Processing
    • Medical image analysis
    • medical informatics
    • Multimodality


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