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Segmentation-Based Fusion of CT and MR Images.

In this paper, a segmentation-based image fusion method is proposed for the fusion of MR and CT images to obtain a high contrast fused image that contains complementary information from both input images. The proposed method uses the fuzzy C-mean method to extract information about the skull from th...

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Published in:Journal of Digital Imaging Vol. 37; no. 5; pp. 2635 - 2649
Main Authors: Gupta, Pragya, Jain, Nishant
Format: algorithm diagnostic images equations & formulas research tables/charts Journal Article
Published: Springer Nature Oct2024
Online Access:View this record in EBSCOhost
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      dt: Oct2024
      vid: 37
      iid: 5
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10278-024-01078-x
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        atl: Segmentation-Based Fusion of CT and MR Images.
      aug:
        au:
          Gupta, Pragya
          Jain, Nishant
        affil: https://ror.org/00hshrf16 Department of Electronics and Communication Engineering, Jaypee University of Information Technology, 173234, Waknaghat, Solan, India
      sug:
        subj:
          Tomography, X-Ray Computed
          Magnetic Resonance Imaging
          Human
          Skull Radiography
          Brain Neoplasms Radiography
          Sarcoma Diagnosis
          Descriptive Statistics
          Female
          Adult
          Male
          Adult: 19-44 years
          Female
          Male
      ab: In this paper, a segmentation-based image fusion method is proposed for the fusion of MR and CT images to obtain a high contrast fused image that contains complementary information from both input images. The proposed method uses the fuzzy C-mean method to extract information about the skull from the CT image. This skull information is used to extract soft tissue information from the MR image. Both the skull information and the soft tissue information are then fused using the fusion rule. The efficiency of the proposed method over other state-of-the-art fusion methods is analyzed and compared using qualitative and quantitative analysis methods. Qualitative analysis shows the improvement in the contrast between the bone and the soft tissue using the proposed method over other state-of-the-art methods without introducing any artifacts or distortions. Classical and gradient-based quantitative analysis also show significant improvement in the fused image obtained using the proposed method over the five state-of-the-art methods. The percentage improvement in the standard deviation, average gradient, entropy, spatial frequency, QABF, and LABF of the proposed method over the best value obtained by the five state-of-the-art methods is 27.11%, 12.06%, 23.64%, 11.30%, 5.59%, and 13.70% respectively.
      pubtype: Academic Journal
      doctype:
        algorithm
        diagnostic images
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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