共查询到20条相似文献,搜索用时 140 毫秒
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基于最大熵原则和灰度变换的图像增强 总被引:3,自引:0,他引:3
提出了一种利用最大熵原则和灰度变换进行图像对比度增强的方法.在最大熵原则基础上利用条件迭代算法对图像灰度级进行最佳分类,对各分类区域进行相应的灰度变换,根据不同需要选取变换参数,在图像对比度增强同时各区域均衡性也得到很大改善.将利用条件迭代算法计算最大熵多阈值的方法与最小均方误差(LMSE)计算多阈值的方法进行比较,实验结果表明,文中所用方法在迭代次数上大大低于基于最小均方误差算法所需迭代次数,节省了图像处理时间,图像均衡化效果也相对提高. 相似文献
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红外图像实时增强的新算法 总被引:10,自引:0,他引:10
针对红外图像的特点,提出了一种红外图像实时增强的新算法。该算法通过分析图像的直方图,得到图像中目标像素数峰值的估计值,并作为平台直方图均衡化的阈值。用该阈值对直方图进行修正,然后通过修正后的直方图进行直方图均衡化。在FPGA内通过采用并行处理结构及流水线技术实现了该算法,并且每秒可处理25帧128×128×8bits的红外图像。理论分析和实验结果均表明,本算法克服了采用一般直方图均衡化增强红外图像的缺点?对背景和噪声增强过度,抑制了目标的增强。该算法对红外图像增强后,图像对比度是直方图均衡化增强后图像对比度的1.8倍。 相似文献
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超声弹性成像提供组织的硬度信息以区分正常状态和疾病状态.应变图像的质量受到随机噪声的影响,从而降低了病变的可检测性并增加了误诊率.如何有效地抑制噪声,提高应变图像的质量对于诊断是至关重要的.本文提出一种将信号预处理与应变估计及其后处理相结合的超声弹性成像增强方法.将采集的射频(RF)信号经过巴特沃斯滤波来滤除信号中的噪声;通过二维自相关进行组织应变估计;利用统计阈值和中值平滑处理应变估计结果并进一步获得超声弹性图像.为了证明算法的有效性,将本文结果与传统二维自相关方法做对比.实验结果证明,本文方法得到的应变图像信噪比(SNRe)较传统方法提高了0.55 dB,对比度噪声比(CNRe)提高了18.09 dB.因此,该方法可以有效地提高超声弹性成像的质量,有望提高病变组织诊断的正确率. 相似文献
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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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Tamalika Chaira 《International journal of imaging systems and technology》2020,30(4):1162-1172
Mammogram image enhancement is very much necessary in diagnosing breast cancer or tumor at an early stage. Nonuniform illumination and low contrast images are commonly encountered in mammogram images. Conventional enhancement algorithms produce either some artifacts or cannot highlight minute details present in the images, particularly when dealing with mammogram images. In this article, we propose a new mammogram image enhancement scheme using Atanassov's intuitionistic fuzzy set (IFS) theory. IFS considers two uncertainties—membership and nonmembership degree apart from membership degree as in fuzzy set theory. As mammogram images are low contrast images and many of the image definitions are vague/unclear, so IFS theory may be suitable for better image enhancement. Initially, the image is transformed to an intuitionistic fuzzy image using a novel intuitionistic fuzzy generator. Hesitation degree is computed and using the hesitation degree, two membership levels are computed to form an interval type 2 fuzzy set. These two membership functions are then combined using Zadeh's fuzzy t-conorm to form a new membership function. Threshold of interval type 2 fuzzy image is obtained using restricted equivalence function. Using the threshold, modified fuzzy hyperbolization is carried out. Real data experiments demonstrate that the proposed algorithm has better performance on contrast and visual quality of the images both quantitatively and qualitatively when compared with different existing methods. 相似文献
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Magudeeswaran Veluchamy Krishnamurthy Mayathevar Bharath Subramani 《International journal of imaging systems and technology》2019,29(3):339-352
Medical image segmentation is crucial for neuroscience research and computer-aided diagnosis. However, intensity inhomogeneity and existence of noise in magnetic resonance images lead to incorrect segmentation. In this article, an effective method called enhanced fuzzy level set algorithm is presented to segment the white matter, gray matter, and cerebrospinal fluid automatically in contrast-enhanced brain images. In this method, first, exposure threshold is computed to divide the input histogram into two sub-histograms of different gray levels. The input histogram is clipped using a mean gray level to control the excessive enhancement rate. Then, these two sub-histograms are modified and equalized independently to get a better contrast enhanced image. Finally, an enhanced fuzzy level set algorithm is employed to facilitate image segmentation. The extensive experimental results proved the outstanding performance of the proposed algorithm compared with other existing methods. The results conform its effectiveness for MR brain image segmentation. 相似文献
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Hanuman Verma Ramesh K. Agrawal Naveen Kumar 《International journal of imaging systems and technology》2014,24(4):277-283
Magnetic resonance imaging (MRI) brain image segmentation is essential at preliminary stage in the neuroscience research and computer‐aided diagnosis. However, presence of noise and intensity inhomogeneity in MRI brain images leads to improper segmentation. The fuzzy entropy clustering (FEC) is often used to deal with noisy data. One major disadvantage of the FEC algorithm is that it does not consider the local spatial information. In this article, we have proposed an improved fuzzy entropy clustering (IFEC) algorithm by introducing a new fuzzy factor, which incorporates both local spatial and gray‐level information. The IFEC algorithm is insensitive to noise, preserves the image detail during clustering, and is free of parameter selection. The efficacy of IFEC algorithm is demonstrated by comparing it quantitatively with the state‐of‐the‐art segmentation approaches in terms of similarity index on publically available real and simulated MRI brain images. 相似文献
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MRI brain image enhancement using brightness preserving adaptive fuzzy histogram equalization 下载免费PDF全文
Bharath Subramani Magudeeswaran Veluchamy 《International journal of imaging systems and technology》2018,28(3):217-222
In this article, fuzzy logic based adaptive histogram equalization (AHE) is proposed to enhance the contrast of MRI brain image. Medical image plays an important role in monitoring patient's health condition and giving an effective diagnostic. Mostly, medical images suffer from different problems such as poor contrast and noise. So it is necessary to enhance the contrast and to remove the noise in order to improve the quality of a various medical images such as CT, X‐ray, MRI, and MAMOGRAM images. Fuzzy logic is a useful tool for handling the ambiguity or uncertainty. Brightness Preserving Adaptive Fuzzy Histogram Equalization technique is proposed to improve the contrast of MRI brain images by preserving brightness. Proposed method comprises of two stages. First, fuzzy logic is applied to an input image and then it's output is given to AHE technique. This process not only preserves the mean brightness and but also improves the contrast of an image. A huge number of highly MRI brain images are taken in the proposed method. Performance of the proposed method is compared with existing methods using the parameters namely entropy, feature similarity index, and contrast improvement index and the experimental results show that the proposed method overwhelms the previous existing methods. 相似文献
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提出了一种基于局部均值分解多尺度模糊熵和灰色相似关联度相结合的滚动轴承故障诊断方法。该方法将故障信号自适应地分解为若干乘积函数,并从中选取包含主要故障信息的PF分量计算多尺度模糊熵作为特征向量,通过计算待识别样本与标准故障模式的灰色相似关联度,对滚动轴承故障类型和损伤程度进行判断。将该方法与LMD模糊熵和灰色相似关联度相结合的方法进行了对比,实验表明,基于LMD多尺度模糊熵和灰色相似关联度的滚动轴承故障诊断方法,能够有效地识别滚动轴承运行状态,实现对滚动轴承的故障诊断。 相似文献
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C. Prabu S.V.M.G. Bavithiraja S. Narayanamoorthy 《International journal of imaging systems and technology》2016,26(1):24-28
A process of splitting the image into pixel bands is the image segmentation. As medical imaging contain uncertainties, there are difficulties in classification of images into homogeneous regions. There is a need for segmentation algorithm for removing the noise from the medical image segmentation. The very popular algorithm is Fuzzy C‐Means (FCM) algorithm used for image segmentation. Fuzzy sets, rough sets, and the combination of fuzzy and rough sets play a prominent role in formalizing uncertainty, vagueness, and incompleteness in diagnosis. But it will use intensity values only which will be highly sensitive to noise. In this article, an Intuitionistic FCM (IFCM) algorithm is presented for clustering. Intuitionistic fuzzy (IF) sets are generalized sets and their elements are characterized by a membership value as well as nonmembership value. This IFCM has an uncertainty parameter which is called hesitation degree and a new objective function is integrated in the standard FCM based on IF entropy. The IFCM will provide better performance than FCM for image segmentation. 相似文献
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一类灰色模糊决策问题的熵权分析方法 总被引:1,自引:0,他引:1
以灰色系统理论和模糊数学为基础,探讨了不确定型决策问题的特性,分析了一些相关成果中所给方法在直接处理灰色模糊数方面的优势与不足。运用优化理论和熵极大化准则,建立了基于灰色模糊关系的多属性群体决策方法,分别对属性权重向量已知和未知两种情况给出了简便实用的算法,通过算例说明了算法的合理性。 相似文献
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Monika Agarwal Geeta Rani Vijaypal Singh Dhaka 《International journal of imaging systems and technology》2020,30(3):687-703
Magnetic resonance imaging (MRI) is a real assistant for doctors. It provides rich information about anatomy of human body for precise diagnosis of a diseases or disorder. But it is quite challenging to extract relevant information from low contrast and poor quality MRI images. Poor visual interpretation is a hindrance in correct diagnosis of a disease. This creates a strong need for contrast enhancement of MRI images. Study of existing literature shows that conventional techniques focus on intensity histogram equalization. These techniques face the problems of over enhancement, noise and unwanted artifacts. Moreover, these are incapable to yield the maximum entropy and brightness preservation. Thus ineffective in diagnosis of a defect/disease such as tumor. This motivates the authors to propose the contrast enhancement model namely optimized double threshold weighted constrained histogram equalization. The model is a pipelined approach that incorporates Otsu's double threshold method, particle swarm optimized weighted constrained model, histogram equalization, adaptive gamma correction, and Wiener filtering. This algorithm preserves all essential information recorded in an image by automatically selecting an appropriate value of threshold for image segmentation. The proposed model is effective in detecting tumor from enhanced MRI images. 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