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1.
Brightness preserving bi‐level fuzzy histogram equalization for MRI brain image contrast enhancement 下载免费PDF全文
V. Magudeeswaran C. G. Ravichandran P. Thirumurugan 《International journal of imaging systems and technology》2017,27(2):153-161
In this article, brightness preserving bi‐level fuzzy histogram equalization (BPFHE) is proposed for the contrast enhancement of MRI brain images. Histogram equalization (HE) is widely used for improving the contrast in digital images. As a result, such image creates side‐effects such as washed‐out appearance and false contouring due to the significant change in brightness. In order to overcome these problems, mean brightness preserving HE based techniques have been proposed. Generally, these methods partition the histogram of the original image into sub histograms and then independently equalize each sub‐histogram. The BPFHE consists of two stages. First, fuzzy histogram is computed based on fuzzy set theory to handle the inexactness of gray level values in a better way compared to classical crisp histograms. In the second stage, the fuzzy histogram is divided into two sub‐histograms based on the mean intensities of the multi‐peaks in the original image and then equalizes them independently to preserve image brightness. The quantitative and subjective enhancement of proposed BPBFHE algorithm is evaluated using two well known parameters like entropy or average information contents (AIC) and Feature Similarity Index Matrix (FSIM) for different gray scale images. The proposed method have been tested using several images and gives better visual quality as compared to the conventional methods. The simulation results show that the proposed method has better performance than the existing methods, and preserve the original brightness quite well, so that it is possible to be utilized in medical image diagnosis. 相似文献
2.
Chengwei Liu Yuan Liu Xiaodong Kuang Guohua Gu Qian Chen 《Journal of Modern Optics》2013,60(15):1590-1601
ABSTRACTAn adaptive contrast enhancement method is proposed in this paper. The proposed method is based on the contrast limited adaptive histogram equalization (CLAHE) and the histogram modification framework. The predefined clip point for the clipped histogram of each block in original CLAHE may still result in excessive contrast enhancement in homogeneous regions, which gives the enhanced image an unnatural look and creates visual artifacts. By replacing the clipped histogram with a modified histogram, the proposed method achieves success in adaptively enhancing contrast in each block based on its content. In addition to this, a novel mapping function is introduced to further improve the enhanced result of histogram equalization (HE). Experiments are conducted with both visible images and infrared images to evaluate the performance of the proposed method. The results show that the proposed method gets better performance on contrast enhancement and visual quality of the enhanced results. 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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J. Fenshia Singh V. Magudeeswaran 《International journal of imaging systems and technology》2017,27(4):311-316
In brain MRI analysis, image segmentation is commonly used for measuring and visualizing the brain's anatomical structures, for surgical planning, etc. However, due to presence of noise and uncertainty between different tissues in the brain image, the segmentation of brain is a challenging task. This problem is rectified in this article using two stages. In the first stage an enhancement technique called contrast limited fuzzy adaptive histogram equalization (CLFAHE) which is a combination of CLAHE and fuzzy enhancement is used to improve the contrast of MRI Brain images. Contrast of the image is controlled using contrast intensification operator (Clip limit). The second stage deals with the segmentation of enhanced image. The enhanced brain images are segmented using new level‐set method which has the property of both local and global segmentation. Signed pressure force (SPF) function is also used here which stops the contours at weak and blurred edged efficiently. 相似文献
6.
Sonali Dash Manas Ranjan Senapati Pradip Kumar Sahu P. S. R. Chowdary 《International journal of imaging systems and technology》2021,31(1):351-363
The Retinal image carries important information about the health of the sensory part of the visual system. In this paper, a new approach is suggested by utilizing the homomorphic filter integrated with Contrast limited adaptive histogram equalization (CLAHE) method for the illumination normalization and contrast enhancement of the retinal images. Then segmentation is done through several steps by using the existing methods such as morphological filtering, a second derivative operator that is followed by a final morphological filtering stage and hysteresis thresholding. The suggested method is verified on DRIVE and CHASE‐DB1 databases and has average accuracy of 72.03% and 64.54%, accordingly. The obtained results demonstrate that the proposed approaches achieve higher accuracy than the traditional method. The suggested approach not only contributes to the successful result, but also minimizes the computing time. 相似文献
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Contrast enhancement using real coded genetic algorithm based modified histogram equalization for gray scale images 下载免费PDF全文
Histogram equalization is a well‐known technique used for contrast enhancement. The global HE usually results in excessive contrast enhancement because of lack of control on the level of contrast enhancement. A new technique named modified histogram equalization using real coded genetic algorithm (MHERCGA) is aimed to sweep over this drawback. The primary aim of this paper is to obtain an enhanced method which keeps the original brightness. This method incorporates a provision to have a control over the level of contrast enhancement and applicable for all types of image including low contrast MRI brain images. The basic idea of this technique is to partition the input image histogram into two subhistograms based on a threshold which is obtained using Otsu's optimality principle. Then, bicriteria optimization problem is formulated to satisfy the aforementioned requirements. The subhistograms are modified by selecting optimal contrast enhancement parameters. Finally, the union of the modified subhistograms produce a contrast enhanced and details preserved output image. While developing an optimization problem, real coded genetic algorithm is applied to determine the optimal value of contrast enhancement parameters. This mechanism enhances the contrast of the input image better than the existing contemporary HE methods. The quality of the enhanced brain image indicates that the image obtained after this method can be useful for efficient detection of brain cancer in further process like segmentation, classification, etc. The performance of the proposed method is well supported by the contrast enhancement quantitative metrics such as discrete entropy and natural image quality evaluator. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 24–32, 2015 相似文献
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目的为了解决图像因亮度较大造成的成像效果不佳、局部细节不清楚等问题。方法将直方图均衡化技术(Histogram Equalization, HE)引入图像信息熵域,提出对比度弱化的图像信息熵统计直方图自适应均衡化算法(Contrast-reduced Adaptive Entropy Histogram Equalization, CRAEHE)。以各个灰度级信息熵统计值为基础,先将原图像分割成若干个子区域,对每个子区域的灰度信息熵统计值进行阈值截取,补充到子区域内各个灰度级上,再对子区域进行信息熵直方图均衡化处理。采用USC-SIPI和CBSD432数据集图像,用图像灰度均值、标准差、平均梯度、信息熵等参数对实验样本进行质量评价。结果文中算法处理结果较原图灰度均值下降了7.94%,标准差平均提高了52.22%,信息熵平均提高了19.86%,平均梯度提高了57.19%。结论文中算法增强了选自数据集里的过亮图像的细节,并使图像整体细节与质量都得到了改善,该算法的处理结果较其他处理实验样本的主观质量提升明显,对光照强度适应范围广。 相似文献
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Shaowei Weng Yiyun Liu Yunqing Shi Bo Ou Chunyu Zhang Cuiping Wang 《计算机、材料和连续体(英文)》2020,62(1):157-177
This paper proposes a two-step general framework for reversible data hiding
(RDH) schemes with controllable contrast enhancement. The first step aims at preserving
visual perception as much as possible on the basis of achieving high embedding capacity
(EC), while the second step is used for increasing image contrast. In the second step, some
peak-pairs are utilized so that the histogram of pixel values is modified to perform
histogram equalization (HE), which would lead to the image contrast enhancement.
However, for HE, the utilization of some peak-pairs easily leads to over-enhanced image
contrast when a large number of bits are embedded. Therefore, in our proposed framework,
contrast over-enhancement is avoided by controlling the degree of contrast enhancement.
Since the second step can only provide a small amount of data due to controlled contrast
enhancement, the first one helps to achieve a large amount of data without degrading visual
quality. Any RDH method which can achieve high EC while preserve good visual quality,
can be selected for the first step. In fact, Gao et al.’s method is a special case of our
proposed framework. In addition, two simple and commonly-used RDH methods are also
introduced to further demonstrate the generalization of our framework. 相似文献
10.
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
11.
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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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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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
15.
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
16.
一种基于视觉感兴趣区域的彩色图像增强方法 总被引:8,自引:8,他引:0
目的把视觉感兴趣区域概念引入直方图的建造中,提出了一种新的基于视觉感兴趣区域的图像增强方法,使增强效果更符合人眼视觉感知。方法首先在标准观测环境下利用先进的眼动仪设备获得人眼感兴趣区域,然后计算各子区域的平均显著值,以确定各个子区域的权重系数,最后采取类似直方图均衡化的思想,优化配比灰度级的动态范围。结果通过主客观实验结果表明,增强后的图像更符合人眼视觉感知。结论结合了视觉感知特性的直方图增强方法,弥补了传统直方图与人眼视觉感知不一致的弊端,其增强效果更佳。 相似文献
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The enhancement of image contrast and preservation of image brightness are two important but conflicting objectives in image restoration. Previous attempts based on linear histogram equalization had achieved contrast enhancement, but exact preservation of brightness was not accomplished. A new perspective is taken here to provide balanced performance of contrast enhancement and brightness preservation simultaneously by casting the quest of such solution to an optimization problem. Specifically, the non-linear gamma correction method is adopted to enhance the contrast, while a weighted sum approach is employed for brightness preservation. In addition, the efficient golden search algorithm is exploited to determine the required optimal parameters to produce the enhanced images. Experiments are conducted on natural colour images captured under various indoor, outdoor and illumination conditions. Results have shown that the proposed method outperforms currently available methods in contrast to enhancement and brightness preservation. 相似文献
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基于多尺度Retinex算法的彩色雾霾图像增强研究 总被引:1,自引:0,他引:1
介绍了基于颜色恒常性理论的Retinex模型,并重点分析了色彩恢复多尺度Retinex(MSRCR)算法的原理和实现方法。为验证基于Retinex理论的算法对图像增强具有良好的效果,以雾霾天气采集到的3幅彩色道路监控图像为实验对象,在MATLAB7.0软件中,利用MSRCR算法、直方图均衡化2种图像增强方法,对实验图像进行去雾霾处理,并通过主观评价、图像信息熵、亮度通道直方图来比较和分析2种算法的图像增强效果。研究结果表明:采用MSRCR算法可以还原出细节更丰富、辨析度更高的画面,且处理后的图像具有更大的信息熵,图像色彩也更接近原始图像。 相似文献
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Kuldeep Singh Dinesh K. Vishwakarma Gurjit Singh Walia Rajiv Kapoor 《Journal of Modern Optics》2013,60(15):1444-1450
AbstractThis paper presents two novel contrast enhancement approaches using texture regions-based histogram equalization (HE). In HE-based contrast enhancement methods, the enhanced image often contains undesirable artefacts because an excessive number of pixels in the non-textured areas heavily bias the histogram. The novel idea presented in this paper is to suppress the impact of pixels in non-textured areas and to exploit texture features for the computation of histogram in the process of HE. The first algorithm named as Dominant Orientation-based Texture Histogram Equalization (DOTHE), constructs the histogram of the image using only those image patches having dominant orientation. DOTHE categories image patches into smooth, dominant or non-dominant orientation patches by using the image variance and singular value decomposition algorithm and utilizes only dominant orientation patches in the process of HE. The second method termed as Edge-based Texture Histogram Equalization, calculates significant edges in the image and constructs the histogram using the grey levels present in the neighbourhood of edges. The cumulative density function of the histogram formed from texture features is mapped on the entire dynamic range of the input image to produce the contrast-enhanced image. Subjective as well as objective performance assessment of proposed methods is conducted and compared with other existing HE methods. The performance assessment in terms of visual quality, contrast improvement index, entropy and measure of enhancement reveals that the proposed methods outperform the existing HE methods. 相似文献