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An adaptive anchored neighborhood regression method for medical image enhancement

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Abstract

Chinese Government has launched ambitious healthcare reform aiming to provide better healthcare services for both urban and rural residents via remote diagnosis. Recently, the requirement of high-resolution (HR) images becomes more urgent in the medical field, especially for remote diagnosis. Remote diagnosis is an important means of the Internet of Medical Things (IoMT), which senses patients’ health status according to the medical images and transfers clinical data. Owing to the superiority of reconstruction speed and quality, adjusted anchored neighborhood regression has attracted much attention. However, the hypercells formed by neighborhoods may not center on atoms, and hence it is not accurate to group the neighborhood centered on atoms. In this paper, we propose an adaptive medical image enhancement method. Specifically, we cluster training samples into neighborhoods centered on patches. The LR space is re-divided by replacing dictionary atoms with cluster patch centers as the center of hypercells. By this means, the neighborhood anchored in the patch is defined by computing its K nearest patches, and then applied to pre-compute the projection to map low-resolution patch onto the HR domain. Average quantitative results show that our method is superior to the compared methods in two common medical datasets by 1.19% and 2.32%, respectively, while maintaining the similar running time.

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Notes

  1. https://www.healthcare.siemens.com

  2. http://www.med.harvard.edu/AANLIB/home.html.

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Acknowledgments

This work is sponsored by Key Research and Development Project of Science and Technology Commission Foundation of Sichuan Province (2018FZ0036) and the National Natural Science 506 Foundation of China (grant no. 61711540303 and 61701327).

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Authors and Affiliations

  1. West China School of Public Health, University of Sichuan, Sichuan, China

    Lihua Jiang & Xiao Ma

  2. College of Electronics and Information Engineering, University of Sichuan, Sichuan, China

    Shuang Ye, Xiaomin Yang & Lu Lu

  3. Dipartimento di Informatica (DI), Università degli Studi di Milano, Via Celoria 18, Milano, MI, 20133, Italy

    Awias Ahmad

  4. School of Electronic Engineering, Xidian University, Xi’an, China

    Gwanggil Jeon

  5. Department of Embedded Systems Engineering, Incheon National University, Incheon, Korea

    Gwanggil Jeon

Authors
  1. Lihua Jiang
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  2. Shuang Ye
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  3. Xiaomin Yang
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  4. Xiao Ma
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  5. Lu Lu
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  6. Awias Ahmad
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  7. Gwanggil Jeon
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Correspondence to Xiaomin Yang or Lu Lu.

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Jiang, L., Ye, S., Yang, X. et al. An adaptive anchored neighborhood regression method for medical image enhancement. Multimed Tools Appl 79, 10533–10550 (2020). https://doi.org/10.1007/s11042-019-08353-y

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  • DOI: https://doi.org/10.1007/s11042-019-08353-y

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