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Medical & Biological Engineering & Computing

Erschienen in:

01.10.2012 | Original Article

NSCT-based multimodal medical image fusion using pulse-coupled neural network and modified spatial frequency

verfasst von: Sudeb Das, Malay Kumar Kundu

Erschienen in: Medical & Biological Engineering & Computing | Ausgabe 10/2012

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Abstract

In this article, a novel multimodal medical image fusion (MIF) method based on non-subsampled contourlet transform (NSCT) and pulse-coupled neural network (PCNN) is presented. The proposed MIF scheme exploits the advantages of both the NSCT and the PCNN to obtain better fusion results. The source medical images are first decomposed by NSCT. The low-frequency subbands (LFSs) are fused using the ‘max selection’ rule. For fusing the high-frequency subbands (HFSs), a PCNN model is utilized. Modified spatial frequency in NSCT domain is input to motivate the PCNN, and coefficients in NSCT domain with large firing times are selected as coefficients of the fused image. Finally, inverse NSCT (INSCT) is applied to get the fused image. Subjective as well as objective analysis of the results and comparisons with state-of-the-art MIF techniques show the effectiveness of the proposed scheme in fusing multimodal medical images.

Vorheriger Artikel Morphology analysis of vertebral trabecular bone under dynamic loading based on multi-scale theory

Nächster Artikel Erratum to: Rapid automatic assessment of microvascular density in sidestream dark field images

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Titel: NSCT-based multimodal medical image fusion using pulse-coupled neural network and modified spatial frequency
verfasst von: Sudeb Das
Malay Kumar Kundu
Publikationsdatum: 01.10.2012
Verlag: Springer-Verlag
Erschienen in: Medical & Biological Engineering & Computing / Ausgabe 10/2012
Print ISSN: 0140-0118
Elektronische ISSN: 1741-0444
DOI: https://doi.org/10.1007/s11517-012-0943-3

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