Recent Advancements in Multimodal Medical Image Fusion Techniques for Better Diagnosis: An overview

Maruturi Haribabu, Velmathi Guriviah, Pratheepan Yogarajah

Research output: Contribution to journalReview articlepeer-review

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Abstract

A Medical imaging plays a vital role in medical diagnosis and clinical treatment. The biggest challenge in the medical field is the correct identification of disease and better treatment. Multi-modal Medical Image Fusion (MMIF) is the process of merging multiple medical images from different modalities into a single fused image. The main objective of the medical image fusion is to obtain a large amount of appropriate information (i.e., features) to improve the quality and makes it more informative for increasing clinical therapy for better diagnosis and clear assessment of medical related problems. The MMIF is generally considered with MRI (Magnetic Resonance Imaging), CT (Computed Tomography), PET (Positron Emission Tomography), SPECT (Single Photon Emission Computed Tomography), MRA (Magnetic Resonance Angiography), T1-weighted MR, T2-weighted MR, X-ray, and ultrasound imaging (Vibro-Acoustography).This review article presents a comprehensive survey of existing medical image fusion methods and have been characterized as six parts: (1) Multi-modality medical images, (2) literature review process, (3) image fusion rules, (4) quality evaluation metrics for assessment of fused image, (5) experimental results on registered datasets and (6) Conclusion. In addition, this review article provides scientific challenges faced in MMIF and future directions for better diagnosis. It is expected that this review will be useful to establish a concrete foundation for developing more valuable fusion methods for medical diagnosis.
Original languageEnglish
JournalCurrent Medical Imaging Formerly Current Medical Imaging Reviews
Volume18
Early online date6 Jun 2022
DOIs
Publication statusPublished (in print/issue) - 21 Sep 2022

Bibliographical note

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Keywords

  • Radiology
  • Nuclear Medicine and imaging
  • multi-modality
  • medical images
  • medical image fusion
  • diagnosis
  • image fusion rules
  • quality assessment metrics

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