A New X-ray Medical-Image-Enhancement Method Based on Multiscale Shannon–Cosine Wavelet

Li, Meng; Mei, Shuli; Liu, Pengfei; Gasimov, Yusif S.; Cattani, Carlo

doi:10.3390/e24121754

Cited by 10 publications

(7 citation statements)

References 21 publications

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“…The proposed method effectively enhances the texture, increasing the brightness and contrast, suppress the noise and obtained MSE= 4.85E+3, PSNR= 25.96, Entropy= 7.88. Liu et al [11] proposed an algorithm which is based on Shannon-cosine wavelets; a multi-scale interpolation wavelet operator is constructed to divide the image into sub-images from high frequency to low frequency. And perform different multi-scale wavelet transform on the detailed image of each channel.…”

Section: Cmentioning

confidence: 99%

Investigation of Different Pre-processing Quality Enhancement Techniques on X-ray Images

Sharda Rani

2023

IJRITCC

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To maximize the accuracy of classification for medical images especially in chest- X ray, we need to improve quality of CXR images or high resolute images will be needed. Pneumonia is a lung infection caused by organism like bacteria or virus. Mostly Chest X-Ray (CXR) is used to detect the infection, but due to limitation of existing equipment, bandwidth, storage space we obtain low quality images. Spatial resolution of medical images is reduced due to image acquisition time, low radiation dose. Quality in medical images plays a major role for clinical diagnosis of disease in deep learning. There is no doubt that noise, low resolution and annotations in chest images are major constraint to the performance of deep learning. Researchers used famous image enhancement algorithms: Histogram equalization (HE), Contrast-limited Adaptive Histogram Equalization (CLAHE), De-noising, Discrete Wavelet Transform (DWT), Gamma Correction (GC), but it is still a challenging task to improve features in images. Computer vision and Super resolution are growing fields of deep learning. Super resolution is also feasible for mono chromatic medical images, which improve the region of interest. Multiple low-resolution images mix with high resolution and then reconstruct a target input image to high quality image by using Super Convolution Neural Network (SRCNN). The objective evaluation based on pixel difference-based PSNR and Human visual system SSIM metric are used for quality measurement. In this study we achieve effective value of PSNR (40 to 43 dB) by considering 30 images of different category (normal, viral or bacterial pneumonia) and SSIM value varies from 97% to 98%. The experiment shows that image quality of CXR is increased by SRCNN, and then high qualitative images will be used for further classification, so that significant parameter of accuracy will be finding in diagnosis of disease in deep learning.

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Section: Cmentioning

confidence: 99%

Investigation of Different Pre-processing Quality Enhancement Techniques on X-ray Images

Sharda Rani

2023

IJRITCC

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“…Traditional image enhancement algorithms have certain effects in both natural image and medical DR image enhancement (especially in natural image enhancement), such as histogram equalization (HE) [6], adaptive histogram equalization (AHE) [7], contrast-limited adaptive histgram equalization (CLAHE) [8]- [10], nonlinear enhancement based on wavelet decomposition [11]- [14], enhancement based on Gauss-Laplacian pyramid [15]- [17] and other algorithms. These traditional enhancement algorithms have some inherent problems.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Feature Attention Module Based Pyramid Network for Medical Digital Radiography Image Enhancement

Xue,

Wang,

Qin

2024

IEEE Access

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Medical digital radiography (DR) is widely used in the clinical application. To deal with the problems of noise, edge blur, low contrast in DR images, we propose a multiscale feature attention module based pyramid enhancement network by training image blocks. The network is in the framework of a simplified U-Net, which reduces the computational load by reducing the convolution layer, and adopts Laplacian pyramid connection instead of concatenation operation to preserve the image boundary information. In addition, we embed a simple multiscale feature attention (SMFA) module between the encoder and decoder, which integrates the feature information of different scales precisely and makes the network have a stronger ability to perceive the local feature information. Our proposed algorithm is a network realization of Gauss-Laplacian pyramid decomposition with an attention module. Furthermore, we design a side feature loss function combined with mean square loss and absolute loss. We adopt batch normalization between convolution and activation operations to ensure information of all gray scale regions to be considered, which enhances the robustness of the network. We use LeakyReLu activation function and Sigmoid function in the previous layers and in the output layers respectively to preserve the negative information of multiscale details and to keep the gray scale region of the output images. Experiments with real data of different parts of human body validate the effectiveness of our algorithm, which shows that our proposed algorithm performs well on contrast enhancement, structure details preservation, and noise suppression. It has certain value of clinical application.

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“…The final results showed that the recommended technique was effective in increasing the contrast of X‐ray images. Liu et al 23 proposed a Shannon‐Cosine wavelet‐based method. Following the division of the image into several sub‐images from high frequency to low frequency using a multiscale interpolation wavelet operator, the detailed image of each channel is subjected to various multiscale wavelet transforms.…”

Section: Introductionmentioning

confidence: 99%

Lung computed tomography image enhancement using U‐Net segmentation

Sheer,

Kareem,

Daway

2024

Int J Imaging Syst Tech

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The goal of image enhancement methods is to improve image's quality. The efficacy of U‐net is evident through its extensive utilization across various significant image modalities, involving computed tomography (CT) scans, magnetic resonance imaging, X‐rays, and microscopy. In this study, we provided a novel and efficient strategy to improve lung CT images based on segmentation using U‐Net architecture. Subsequently, contrast enhancement was performed using adaptive histogram equalization and dark channel prior methods. Finally, the lightness of the lung CT image was enhanced using nonlinear mapping. The contrast enhancement performance of the suggested method is quantified by various measures like the average gradient, mean of the local standard deviation, contrast enhancement measure, and structural similarity index. The performance of the suggested method is compared against other methods, and the results indicate that the suggested method achieves better quality measures of 23.4907, 55.20341, 0.961674, and 0.4143 for the four performance metrics.

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A New X-ray Medical-Image-Enhancement Method Based on Multiscale Shannon–Cosine Wavelet

Cited by 10 publications

References 21 publications

Investigation of Different Pre-processing Quality Enhancement Techniques on X-ray Images

Investigation of Different Pre-processing Quality Enhancement Techniques on X-ray Images

Multiscale Feature Attention Module Based Pyramid Network for Medical Digital Radiography Image Enhancement

Lung computed tomography image enhancement using U‐Net segmentation

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