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Adaptive vascular enhancement of flap images in the second near-infrared window based on multiscale fusion and local visual saliency

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Abstract

Near-infrared fluorescence imaging in the second window has emerged as a valuable tool for the non-invasive and real-time assessment of vascular information in skin flaps. Enhancing flap images to provide more accurate flap vascularization information is critical for predicting flap viability. To address the limitations of existing methods in enhancing vessel images, we propose a novel and adaptive technique for enhancing flap microvessel images. Multiple strategies can be employed to effectively enhance the visualization of small-scale vessels. Firstly, the proposed method leverages the multiscale rolling guided filter to acquire the base layer and detail layers at different scales. Furthermore, correlation coefficients are utilized to weigh and fuse the detail layers effectively. To suppress noise amplification while enhancing vascular structures, an improved adaptive gamma correction method based on local visual saliency is introduced. Meanwhile, the bilateral gamma correction is used to enhance the base layer. Finally, the enhanced base layer and detail layer are fused using the weighted fusion strategy. We conducted experiments on skin flap vessel images, retinal fundus images, finger vein images, and low-light images. Our method achieved excellent results in metrics such as NIQE, AMBE, and WPSNR, demonstrating significant advantages in preserving the structural integrity and brightness consistency of the images. The obtained results validate the potential of this method in enhancing vascular images, indicating promising prospects in the field of medicine.

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Funding

This work was supported by the National Natural Science Foundation of China under Grant 82072521.

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Authors and Affiliations

  1. Chinese Academy of Sciences, Shanghai Institute of Technical Physics, Shanghai, 200083, China

    Lu Fang & Fuchun Chen

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Lu Fang

  3. Sports Medicine Institute of Fudan University, Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China

    Huaixuan Sheng, Huizhu Li, Shunyao Li, Sijia Feng, Yunxia Li & Jun Chen

  4. Department of Bone and Joint Surgery, Department of Orthopedics, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001, China

    Mo Chen

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  1. Lu Fang
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  2. Huaixuan Sheng
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  3. Huizhu Li
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  4. Shunyao Li
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  8. Jun Chen
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  9. Fuchun Chen
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Contributions

Lu Fang: Methodology, Software, Writing—original draft. Huaixuan Sheng: Investigation, Resources, Writing—original draft. Huizhu Li: Formal analysis, Resources. Shunyao Li: Formal analysis, Resources. Sijia Feng: Investigation, Resources, Data Curation. Mo Chen: Formal analysis, Resources, Visualization. Yunxia Li: Formal analysis, Resources, Data Curation. Jun Chen: Conceptualization, Supervision, Funding acquisition. Fuchun Chen: Conceptualization, Methodology.

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Correspondence to Fuchun Chen.

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This animal study was approved by Animal Care Committee of the Laboratory Animal from Fudan University.

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Fang, L., Sheng, H., Li, H. et al. Adaptive vascular enhancement of flap images in the second near-infrared window based on multiscale fusion and local visual saliency. SIViP 18, 5797–5810 (2024). https://doi.org/10.1007/s11760-024-03272-4

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