X-ray image global enhancement algorithm in medical image classification

Zhu, Wenzhong; Jiang, Huanlong; Wang, Erli; Hou, Yani; Xian, Lidong; Debnath, Joyati

doi:10.3934/dcdss.2019089

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…9a and 9b. Based on the behavior of SSIM(t) at several iterations, 1 SSIM(t) can be modeled by αrs t [15]. The final value of SSIM (after several iterations) can be expressed as:…”

Section: B) Gradient Based Structural Similarity Measurementioning

confidence: 99%

Two-Stage Contrast Enhancement Method for Liver Tumor Segmentation

Journal¹

2022

IJSREM

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Tumor segmentation in Computed Tomography (CT) images is a crucial step in image-guided surgery. However, low-contrast CT images impede the performance of subsequent segmentation tasks. Contrast enhancement is then used as a preprocessing step to highlight the relevant structures, thus facilitating not only medical diagnosis but also image segmentation with higher accuracy. The proposed method is based on two concepts, namely adaptive gamma correction using DWT-SVD and OPTimized Guided Contrast Enhancement (OPTGCE). In the proposed DWT-SVD scheme, the technique decomposes the input medical image into four frequency sub-bands by using DWT and then estimates the singular value matrix of the LL sub-band image. An enhanced LL component is generated using an adequate correction factor and inverse SVD. The proposed OPTimized Guided Contrast Enhancement (OPTGCE) scheme exploits both contextual information from the guidance image and structural information from the input image. Tumor segmentation algorithm is applied on the enhanced images to analyze the performance of the proposed method in facilitating tumor segmentation. The qualitative and quantitative analysis using metrics including entropy, MCCEE, and MIGLCM shows the superiority of the proposed method in comparison with the existing methods that do not include guidance mechanism Keywords—: 2D-DWT, Gamma Correction, SSIM, Gradient.

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“…9a and 9b. Based on the behavior of SSIM(t) at several iterations, 1 SSIM(t) can be modeled by αrs t [15]. The final value of SSIM (after several iterations) can be expressed as:…”

Section: B) Gradient Based Structural Similarity Measurementioning

confidence: 99%

Two-Stage Contrast Enhancement Method for Liver Tumor Segmentation

Journal¹

2022

IJSREM

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“…As a result, it is difficult to deal with grid images in the raw scanning data and at the same time to keep image details. In addition, due to noise, improper X-ray exposure and unequal thickness of human tissue [1], DR images have blurred edges and low contrast [2], [3], which will affect disease diagnosis of the doctors. To meet the needs of high-end clinical applications, DR image enhancement is of research significance [4], [5] in the field of medical radiology imaging.…”

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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“…Introduction. Image enhancement is a meaningful and important task in digital image processing, which can be widely used in many fields, such as medical imaging processing, pattern recognition, traffic safety, machine vision, robotics, and remote sensing [16,15,14,37,41]. Basic algorithms of image enhancement are divided into two categories: the spatial domain method and the frequency domain method.…”

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confidence: 99%

Image enhancement algorithm using adaptive fractional differential mask technique

Zhang¹,

Hui²

2019

Mathematical Foundations of Computing

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This paper addresses a novel adaptive fractional order image enhancement method. Firstly, an image segmentation algorithm is proposed, it combines Otsu algorithm and rough entropy to segment image accurately into the objet and the background. On the basis of image segmentation and the knowledge of fractional order differential, an image enhancement model is established. The rough characteristics of each average gray value are obtained by image segmentation method, through these features, we can determine the optimal fractional order of image enhancement. Then image will be enhanced using fractional order differential mask, from which fractional order is obtained adaptively. Several images are used for experiments, the proposed model is compared with other models, and the results of comparison exhibit the superiority of our algorithm in terms of image quality measures.

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X-ray image global enhancement algorithm in medical image classification

Cited by 6 publications

References 13 publications

Two-Stage Contrast Enhancement Method for Liver Tumor Segmentation

Two-Stage Contrast Enhancement Method for Liver Tumor Segmentation

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

Image enhancement algorithm using adaptive fractional differential mask technique

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