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BioMed Research International
Volume 2017, Article ID 3969152, 10 pages
Research Article

Method of Improved Fuzzy Contrast Combined Adaptive Threshold in NSCT for Medical Image Enhancement

1College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
2Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200400, China
3Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1020, New Zealand

Correspondence should be addressed to ZhenHong Jia; moc.uhos@9009hhzj

Received 3 November 2016; Revised 6 April 2017; Accepted 14 May 2017; Published 28 June 2017

Academic Editor: Ayache Bouakaz

Copyright © 2017 Fei Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Noises and artifacts are introduced to medical images due to acquisition techniques and systems. This interference leads to low contrast and distortion in images, which not only impacts the effectiveness of the medical image but also seriously affects the clinical diagnoses. This paper proposes an algorithm for medical image enhancement based on the nonsubsampled contourlet transform (NSCT), which combines adaptive threshold and an improved fuzzy set. First, the original image is decomposed into the NSCT domain with a low-frequency subband and several high-frequency subbands. Then, a linear transformation is adopted for the coefficients of the low-frequency component. An adaptive threshold method is used for the removal of high-frequency image noise. Finally, the improved fuzzy set is used to enhance the global contrast and the Laplace operator is used to enhance the details of the medical images. Experiments and simulation results show that the proposed method is superior to existing methods of image noise removal, improves the contrast of the image significantly, and obtains a better visual effect.