Medical image fusion based on multi-scale transform and sparse representation

Qiaoqiao Li; Weilan Wang; Shi Yan

doi:10.1117/12.2680002

27 June 2023 Medical image fusion based on multi-scale transform and sparse representation

Qiaoqiao Li, Weilan Wang, Shi Yan

Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 127051T (2023) https://doi.org/10.1117/12.2680002
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

Multi-modality image fusion is an effective technique to fuse the complementary information from multi-modality images into a fused image. In this paper, a new fusion method is proposed based on multi-scale transform and sparse representation. In the sparse representation-based method, the key point of it is to construct a good dictionary. In this method, a set of adaptive sub-dictionaries are constructed instead of a single dictionary. First, the source images are decomposed by nonsubsampled contourlet transform (NSCT) to obtain the low-pass coefficients and high-pass coefficients. Second, the sparse representation-based method is applied to fuse the low-pass coefficients. In this processing, low-pass images are divided into classified patches which are based on the patch oriented gradient. A set of sub-dictionaries are constructed from the classified patches. For the high-pass coefficients, we choose the "max-absolute" rule to fuse them. Third, combining the fused low-pass coefficients and the fused high-pass coefficients we can obtain the fused coefficients. In the last, the fused image is reconstructed by using inverse NSCT. Experimental results have shown the effectiveness of the proposed method.

Citation Download Citation

Qiaoqiao Li, Weilan Wang, and Shi Yan "Medical image fusion based on multi-scale transform and sparse representation", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 127051T (27 June 2023); https://doi.org/10.1117/12.2680002

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KEYWORDS

Image fusion

Associative arrays

Histograms

Image processing

Medical imaging

Image classification

Principal component analysis

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