A novel brain image enhancement method based on nonsubsampled contourlet transform

Li, Liangliang; Si, Yujuan; Jia, Zhenhong

doi:10.1002/ima.22264

Cited by 10 publications

(2 citation statements)

References 29 publications

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“…In this approach, low‐frequency coefficients are analyzed by a linear method. Next, a modified adaptive threshold function 7 is used to handle the high‐frequency coefficients. Finally, a global contrast of the image is associated with the improved fuzzy contrast.…”

Section: Related Workmentioning

confidence: 99%

Modified transform‐based gamma correction for MRI tumor image denoising and segmentation by optimized histon‐based elephant herding algorithm

Kollem

Reddy²,

Rao

2020

Int J Imaging Syst Tech

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Medical image processing is typically performed to diagnose a patient's brain tumor prior to surgery. In this study, a technique in denoising and segmentation was developed to improve medical image processing. The proposed approach employs multiple modules. In the first module, the noisy brain tumor image is transformed into multiple low‐ and high‐pass tetrolet coefficients. In the second module, multiple low‐pass tetrolet coefficients are applied through a modified transform‐based gamma correction method. Generalized cross‐validation is used on multiple high‐pass tetrolet coefficients to obtain the best threshold value. In the third module, all enhanced coefficients are applied to the partial differential equation method. In the final module, the denoised image is applied to Atanassov's intuitionistic fuzzy set histon‐based fuzzy clustering method with centroid optimization using an elephant herding method. Accordingly, the tumor part is segmented from the nontumor part in the magnetic resonance imaging brain images. The method was assessed in terms of peak signal‐to‐noise ratio, mean square error, specificity, sensitivity, and accuracy. The experimental results showed that the suggested method is superior to traditional methods.

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Section: Related Workmentioning

confidence: 99%

Modified transform‐based gamma correction for MRI tumor image denoising and segmentation by optimized histon‐based elephant herding algorithm

Kollem

Reddy²,

Rao

2020

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…These algorithms enhance the brightness of the image; however, the image experiences the phenomenon of overall whitening, which leads to increase noises and reduce contrast. On the other hand, enhancement algorithms based on transform domain include wavelet transform, curvelet transform, contourlet transform, nonsubsampled contourlet transform, and shearlet transform . Wavelet transform has excellent time‐frequency characteristics and multiresolution features, and consequently has attracted significant research attention.…”

Section: Introductionmentioning

confidence: 99%

Contrast enhancement of medical images using fuzzy set theory and nonsubsampled shearlet transform

Guo

Jia

Yang

et al. 2019

Int J Imaging Syst Tech

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Noises and artifacts are introduced in medical images during the process of imaging and transmission, resulting in reduced definition and lack of detail. Therefore, a contrast enhancement method, based on fuzzy set theory and nonsubsampled shearlet transform (NSST), is proposed. First, the original image is decomposed into several high‐frequency components and a low‐frequency component by NSST. Then, the threshold method is used to remove noises in the high‐frequency components. In addition, a linear stretch is used to improve the overall contrast in the low‐frequency component. Then, the reconstruct image is reconstructed by applying the inverse NSST to the processed high‐frequency and low‐frequency components. Finally, the fuzzy contrast is used to improve the detail information and global contrast in the reconstruct image. Experimental results indicate that, relative to contrast algorithms, the peak signal‐to‐noise ratio of the proposed method is improved by approximately 18%, and the root mean square error (RMSE) is optimized to approximately 48%. The proposed method also improves the image definition and texture information. Moreover, when compared with the Improved Fuzzy Contrast Combined Adaptive Threshold in NSCT for Medical Image Enhancement, the processing time (time) of this proposed method optimizes about 86%, which can obviously improve the computational efficiency of this method.

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Image fuzzy enhancement algorithm based on contourlet transform domain

Huang

Chen

2019

Multimed Tools Appl

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A novel brain image enhancement method based on nonsubsampled contourlet transform

Cited by 10 publications

References 29 publications

Modified transform‐based gamma correction for MRI tumor image denoising and segmentation by optimized histon‐based elephant herding algorithm

Modified transform‐based gamma correction for MRI tumor image denoising and segmentation by optimized histon‐based elephant herding algorithm

Contrast enhancement of medical images using fuzzy set theory and nonsubsampled shearlet transform

Image fuzzy enhancement algorithm based on contourlet transform domain

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