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MIE-NSCT: Adaptive MRI Enhancement Based on Nonsubsampled Contourlet Transform

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  • Caiwei Liu
  • Guohua Zhao
  • Jiale Dong
  • Yusong Lin
  • Meiyun Wang

Abstract

Image enhancement technology is often used to improve the quality of medical images and helps doctors or expert systems identify and diagnose diseases. This paper aimed at the characteristics of magnetic resonance imaging (MRI) with complex and difficult-to-enhance details and to propose a nonsubsampled contourlet transform- (NSCT-) based enhancement algorithm called MIE-NSCT. NSCT was used for MRI sub-band decomposition. For high-pass sub-bands, four fuzzy rules were proposed to enhance multiscale and multidirectional edge contour details from adjacent eight directions, whilst for low-pass sub-bands, a new adaptive histogram enhancement algorithm was proposed. The problem of noise amplification and loss of details during the enhancement process was solved. The algorithm was verified on the public dataset BraTS2017 and compared with other advanced methods. Experimental results showed that MIE-NSCT had obvious advantages in improving the quality of medical images, and high-quality medical images showed enhanced performance in grading tumour. MIE-NSCT is suitable for integration into an interactive expert system to provide support for the visualization of disease diagnosis.

Suggested Citation

  • Caiwei Liu & Guohua Zhao & Jiale Dong & Yusong Lin & Meiyun Wang, 2021. "MIE-NSCT: Adaptive MRI Enhancement Based on Nonsubsampled Contourlet Transform," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-12, January.
    • Handle: RePEc:hin:jnlmpe:6681202
    DOI: 10.1155/2021/6681202
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