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Xiaoyan Fan Zhanquan Sun Engang Tian Zhong Yin Gaoyu Cao 《International journal of imaging systems and technology》2023,33(1):389-402
The collection or transmission of medical images is often disturbed by various factors, such as insufficient brightness and noise pollution, which will result in the deterioration of image quality and significantly affect the clinical diagnosis. To improve the quality of medical images, a contrast enhancement method based on improved sparrow search algorithm is proposed in this paper. The method is divided into two steps to enhance the medical images. First, a new transform function is introduced to improve the brightness or contrast of medical images, and two parameters in the transform function are optimized by the improved sparrow search algorithm. Second, adaptive histogram equalization method with contrast limited is used to equalize the result image of the previous step to make the pixel distribution of the image more uniform. Finally, a large number of experiments and qualitative and quantitative analyses were conducted on the common data sets. The analysis results demonstrate that the presented approach outperforms some existing medical image processing approaches. 相似文献
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Chung-Feng Jeffrey Kuo Han-Cheng Wu 《International journal of imaging systems and technology》2019,29(2):132-145
In order to enhance the pathological features of medical images and aid the medical diagnosis, the image enhancement is a necessary process. This study presented the Gaussian probability model combining with bi-histogram equalization to enhance the contrast of pathological features in medical images. There are five different bi-histogram equalizations, namely, bi-histogram equalization (BBHE), dualistic sub-image histogram equalization (DSIHE), bi-histogram equalization with a plateau limit (BHEPL), bi-histogram equalization median plateau limit (BHEPL-D), and bi-histogram equalization with modified histogram bins (BHEMHB). The entropy, contrast, absolute mean brightness error (AMBE), and skewness difference are used to quantize the enhancement results. From the experimental result, it is observed that the entropy and contrast of the images can be effectively enhanced by using Gaussian probability bi-histogram equalizations, and the Gaussian probability bi-histogram equalization median plateau limit (GPBHEPL-D) has the best enhanced result. The proposed GPBHEPL-D method is effective in strengthening the pathological features in medical images, so as to increase the efficiency of doctors' diagnoses and computer-aided detection. 相似文献
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Wan Zakiah Wan Ismail Kok Swee Sim 《International journal of imaging systems and technology》2011,21(3):280-289
Image processing requires an excellent image contrast‐enhancement technique to extract useful information invisible to the human or machine vision. Because of the histogram flattening, the widely used conventional histogram equalization image‐enhancing technique suffers from severe brightness changes, rendering it undesirable. Hence, we introduce a contrast‐enhancement dynamic histogram‐equalization algorithm method that generates better output image by preserving the input mean brightness without introducing the unfavorable side effects of checkerboard effect, artefacts, and washed‐out appearance. The first procedure of this technique is; normalizing input histogram and followed by smoothing process. Then, the break point detection process is done to divide the histogram into subhistograms before we can remap the gray level allocation. Lastly, the transformation function of each subhistogram is constructed independently. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 280‐289, 2011; 相似文献
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Sharma Dileepkumar Ramlal Jainy Sachdeva Chirag Kamal Ahuja Niranjan Khandelwal 《International journal of imaging systems and technology》2019,29(2):146-160
This research proposes an improved hybrid fusion scheme for non-subsampled contourlet transform (NSCT) and stationary wavelet transform (SWT). Initially, the source images are decomposed into different sub-bands using NSCT. The locally weighted sum of square of the coefficients based fusion rule with consistency verification is used to fuse the detailed coefficients of NSCT. The SWT is employed to decompose approximation coefficients of NSCT into different sub-bands. The entropy of square of the coefficients and weighted sum-modified Laplacian is employed as the fusion rules with SWT. The final output is obtained using inverse NSCT. The proposed research is compared with existing fusion schemes visually and quantitatively. From the visual analysis, it is observed that the proposed scheme retained important complementary information of source images in a better way. From the quantitative comparison, it is seen that this scheme gave improved edge information, clarity, contrast, texture, and brightness in the fused image. 相似文献
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Mehdi Hassan Iqbal Murtza Muhammad Aqdus Zafar Khan Syed Fahad Tahir Labiba Gillani Fahad 《International journal of imaging systems and technology》2019,29(4):633-644
Imaging based sensitive clinical diagnosis is critically depending on image quality. In this article, the problem of enhancing fundus images is addressed by a novel fusion technique. The proposed approach utilizes the representation capability of wavelet transform and the learning ability of artificial neural networks. In this approach, input images are decomposed into wavelet transform followed by appropriate feature extraction for training of neural networks to obtain fused image. To ensure homogeneity, it employs consistency verification for minimizing the fusion artifacts. The visual and quantitative performance of the proposed approach is assessed using a number of experiments performed on the standard datasets of DRIVE and DRION-DB. The experimental results demonstrate that the proposed fusion technique offers high average structural similarity of “0.99.” The proposed approach outperforms state-of-the-art techniques which validates its effectiveness. The developed approach might be applied by the clinical diagnosis system for fundus related diseases. 相似文献
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针对低照度图像质量较差、噪声多、纹理模糊等问题,提出一种基于双频域特征聚合的低照度增强网络(dual frequency-domain feature aggregation network, DF-DFANet)。首先,构建频谱光照估计模块(frequency domain illumination estimation module, FDIEM)实现跨域特征提取,通过共轭对称约束调整频域特征图抑制噪声信号,并采用逐层融合方式提高多尺度融合效率以扩大特征图感受野范围。其次,设计多谱双注意力模块(multiple spectral attention module, MSAM)聚焦图像局部频率特征,通过小波域空间、通道注意力机制关注图像细节信息。最后,提出双域特征聚合模块(dual domain feature aggregation module, DDFAM)融合傅里叶域和小波域特征信息,利用激活函数计算自适应调整权重实现像素级图像增强,并结合傅里叶域全局信息提高融合效果。实验结果表明,在LOL数据集上所提网络的PSNR达到24.3714,SSIM达到0.8937。与对比网络相比,所提网络增强效果更具自然性。
相似文献8.
Jing-jing Wang Zhen‐hong Jia Xi‐zhong Qin Jie Yang Nikola Kasabov 《International journal of imaging systems and technology》2015,25(1):7-14
In order to solve the problem of noise amplification, low contrast and image distortion in the process of medical image enhancement, a new algorithm is proposed which combines NSCT (nonsubsampled contourlet transform) and improved fuzzy contrast. The image is decomposed by NSCT. Firstly, linear enhancement method is used in low frequency coefficients; secondly the improved adaptive threshold function is used to deal with the high frequency coefficients. Finally, the improved fuzzy contrast is used to enhance the global contrast and the Laplace operator is used to enhance the details of the medical images. Experimental results show that the proposed algorithm can improve the image visual effects, remove the noise and enhance the details of medical images. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 7–14, 2015 相似文献
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Guo Qingrong Jia Zhenhong Yang Jie Nikola Kasabov 《International journal of imaging systems and technology》2019,29(4):483-490
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. 相似文献
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Wei Li Qinyong Lin Keqiang Wang Ken Cai 《International journal of imaging systems and technology》2021,31(1):204-214
Many types of medical images must be fused, as single‐modality medical images can only provide limited information due to the imaging principles and the complexity of human organ structures. In this paper, a multimodal medical image fusion method that combines the advantages of nonsubsampling contourlet transform (NSCT) and fuzzy entropy is proposed to provide a basis for clinical diagnosis and improve the accuracy of target recognition and the quality of fused images. An image is initially decomposed into low‐ and high‐frequency subbands through NSCT. The corresponding fusion rules are adopted in accordance with the different characteristics of the low‐ and high‐frequency components. The membership degree of low‐frequency coefficients is calculated. The fuzzy entropy is also computed and subsequently used to guide the fusion of coefficients to preserve image details. High‐frequency components are fused by maximizing the regional energy. The final fused image is obtained by inverse transformation. Experimental results show that the proposed method achieves good fusion effect based on the subjective visual effect and objective evaluation criteria. This method can also obtain high average gradient, SD, and edge preservation and effectively retain the details of the fused image. The results of the proposed algorithm can provide effective reference for doctors to assess patient condition. 相似文献
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《成像科学杂志》2013,61(7):529-540
AbstractMedical image fusion plays an important role in clinical applications, such as image-guided surgery, image-guided radiotherapy, non-invasive diagnosis and treatment planning. Shearlet is a novel multi-scale geometric analysis (MGA) tool proposed recently. In order to overcome the drawback of the shearlet-based fusion methods that the pseudo-Gibbs phenomenon is easily caused around the singularities of the fused image, a new multi-modal medical image fusion method is proposed in shift-invariant shearlet transform domain. First, the original images are decomposed into lowpass sub-bands and highpass sub-bands; then, the lowpass sub-bands and high sub-bands are combined according to the fusion rules, respectively. All the operations are performed in shift-invariant shearlet domain. The final fused image is obtained by directly applying inverse shift-invariant shearlet transform to the fused lowpass sub-bands and highpass sub-bands. Experimental results demonstrate that the proposed method can not only suppress the pseudo-Gibbs phenomenon efficiently, but perform better than the popular wavelet transform-based method, contourlet transform-based method and non-subsampled contourlet transform-based method. 相似文献
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《成像科学杂志》2013,61(4):208-218
AbstractImage enhancement and de-noising is an essential pre-processing step in many image processing algorithms. In any image de-noising algorithm, the main concern is to keep the interesting structures of the image. Such interesting structures often correspond to the discontinuities (edges). In this paper, we present a new algorithm for image noise reduction based on the combination of complex diffusion process and wavelet thresholding. In the existing wavelet thresholding methods, the noise reduction is limited, because the approximate coefficients containing the main information of the image are kept unchanged. Since noise affects both the approximate and detail coefficients, the proposed algorithm for noise reduction applies the complex diffusion process on the approximation band in order to alleviate the deficiency of the existing wavelet thresholding methods. The algorithm has been examined using a variety of standard images and its performance has been compared against several de-noising algorithms known from the prior art. Experimental results show that the proposed algorithm preserves the edges better and in most cases, improves the measured visual quality of the de-noised images in comparison to the existing methods known from the literature. The improvement is obtained without excessive computational cost, and the algorithm works well on a wide range of different types of noise. 相似文献
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Peng Gui Wing-Kuen Ling Dengyi Zhang Yan Xiang Dangguo Shao Lei Ma 《International journal of imaging systems and technology》2019,29(4):701-710
Image registration is the process of overlaying images of the same scene taken at different times by different sensors from different viewpoints. The cross-cumulative residual entropy (CCRE)-based medical image registration could achieve a high precision and a strong robustness performance. However, the optimization problem formulated by CCRE consists of some local extrema, especially for noise images. In order to address these difficulties, this article proposes a new optimization algorithm named hybrid differential search algorithm (HDSA) to optimize CCRE. As HDSA consists of simple control parameters, it is independent of the initial searching point. In addition, HDSA ameliorated the search method and the iterative conditions. As a result, the optimization process is more stable and efficient. Image registration experiments of HDSA are performed and compared with the conventional differential search algorithm (DSA) and adaptive differential evolution with optional external archive (JADE). The results show that HDSA does not only overcome the difficulties of sticking in the local extrema but also enhances the precision of registration. It is effective, robust, and fast for both the single-mode rigid medical image registration and the multispectral-mode rigid medical image registration. 相似文献
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A medical image enhancement method using adaptive thresholding in NSCT domain combined unsharp masking 下载免费PDF全文
Lu Liu Zhenhong Jia Jie Yang Nikola Kasabov Fellow IEEE 《International journal of imaging systems and technology》2015,25(3):199-205
In the process of medical image formation, the medical image is often interfered by various factors, and it is deteriorated by some new noise that may reduce the quality of the obtained image, which affect the clinical diagnosis seriously. A new medical image enhancement method is proposed in this article. Firstly, the initial medical image is decomposed into the NSCT domain with a low‐frequency sub‐band, and several high‐frequency sub‐bands. Secondly, linear transformation is adopted for the coefficients of the low‐frequency sub‐band. An adaptive thresholding method is used for denoising the coefficients of the high‐frequency sub‐bands. Then, all sub‐bands were reconstructed into spatial domains using the inverse transformation of NSCT. Finally, unsharp masking was used to enhance the details of the reconstructed image. The results of experiment show that the proposed method is superior to other methods in image entropy, EME, and PSNR. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 199–205, 2015 相似文献
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Durga Prasad Bavirisetti Vijayakumar Kollu Xiao Gang Ravindra Dhuli 《International journal of imaging systems and technology》2017,27(3):227-237
In medical imaging using different modalities such as MRI and CT, complementary information of a targeted organ will be captured. All the necessary information from these two modalities has to be integrated into a single image for better diagnosis and treatment of a patient. Image fusion is a process of combining useful or complementary information from multiple images into a single image. In this article, we present a new weighted average fusion algorithm to fuse MRI and CT images of a brain based on guided image filter and the image statistics. The proposed algorithm is as follows: detail layers are extracted from each source image by using guided image filter. Weights corresponding to each source image are calculated from the detail layers with help of image statistics. Then a weighted average fusion strategy is implemented to integrate source image information into a single image. Fusion performance is assessed both qualitatively and quantitatively. Proposed method is compared with the traditional and recent image fusion methods. Results showed that our algorithm yields superior performance. 相似文献
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To transfer the medical image from one place to another place or to store a medical image in a particular place with secure manner has become a challenge. In order to solve those problems, the medical image is encrypting and compressing before sending or saving at a place. In this paper, a new block pixel sort algorithm has been proposed for compressing the encrypted medical image. The encrypted medical image acts as an input for this compression process. During the compression, encrypted secret image E12(;) is compressed by the pixel block sort encoding (PBSE). The image is divided into four identical blocks, similar to 2×2 matrix. The minimum occurrence pixel(s) are found out from every block and the positions of the minimum occurrence pixel(s) are found using the verdict occurrence process. The pixel positions are shortened with the help of a shortening process. The features (symbols and shortened pixel positions) are extracted from each block and the extracted features are stored in a particular place, and the values of these features put together as a compressed medical image. The next process of PBSE is pixel block short decoding (PBSD) process. In the decoding process, there are nine steps involved while decompressing the compressed encrypted medical image. The feature extraction value of compressed information is found out from the feature extraction, the symbols are split and the positions are shortened in a separate manner. The position is retrieved from the rescheduled process and the symbols and reconstructed positions of the minimum occurrence pixels are taken block wise. Every symbol is placed based on the position in each block: if the minimum occurrence pixel is ‘0’, then the rest of the places are automatically allocated as ‘1’ or if the minimum occurrence pixel is ‘1’ the remaining place is automatically allocated as ‘0’. Both the blocks are merged as per order 2×2. The final output is the reconstructed encrypted medical image. From this compression method, we can achieve the high compression ratio, minimum time, less compression size and lossless compression, which are the things experimented and proved. 相似文献
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