MRI brain image enhancement using brightness preserving adaptive fuzzy histogram equalization

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.     

Contrast limited fuzzy adaptive histogram equalization for enhancement of brain images

Contrast limited fuzzy adaptive histogram equalization (CLFAHE) is proposed to improve the contrast of MRI Brain images. The proposed method consists of three stages. First, the gray level intensities are transformed into membership plane and membership plane is modified with Contrast intensification operator. In the second stage, the contrast limited adaptive histogram equalization is applied to the modified membership plane to prevent excessive enhancement in contrast by preserving the original brightness. Finally, membership plane is mapped back to the gray level intensities. The performance of proposed method is evaluated and compared with the existing methods in terms of qualitative measures such as entropy, PSNR, AMBE, and FSIM. The proposed method provides enhanced results by giving better contrast enhancement and preserving the local information of the original image. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 98–103, 2017     

Contrast enhancement dynamic histogram equalization for medical image processing application

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;     

Image contrast enhancement using histogram equalization with maximum intensity coverage

The histogram equalization process is a simple yet efficient image contrast enhancement technique that generally produces satisfactory results. However, due to its design limitations, output images often experience a loss of fine details or contain unwanted viewing artefacts. One reason for such imperfection is a failure of some techniques to fully utilize the allowable intensity range in conveying the information captured from a scene. The proposed colour image enhancement technique introduced in this work aims at maximizing the information content within an image, whilst minimizing the presence of viewing artefacts and loss of details. This is achieved by weighting the input image and the interim equalized image recursively until the allowed intensity range is maximally covered. The proper weighting factor is optimally determined using the efficient golden section search algorithm. Experiments had been conducted on a large number of images captured under natural indoor and outdoor environment. Results showed that the proposed method is able to recover the largest amount of information as compared to other current approaches. The developed method also provides satisfactory performances in terms of image contrast, and sharpness.     

Palmprint enhancement using recursive histogram equalisation

Abstract

Palmprint identification system is one of the most powerful personal identification systems in recent years. In order to achieve high identification accuracy, all parts of the palmprint are needed to be enhanced. Histogram equalisation is a very popular image enhancing technique. A novel histogram equalisation technique, called recursive\ histogram equalisation, for brightness preservation and image contrast enhancement, is put forward in this paper. The essence of proposed algorithm is to decompose an input histogram into two or more sub-histograms recursively based on its mean, change the sub-histograms through a weighting process based on a normalised power law function and then equalise the weighted sub-histograms independently. Experiments show that our method preserves the mean brightness of a given image, enhances the contrast and produces more natural looking images than the other histogram equalisation methods.     

Contrast enhancement using real coded genetic algorithm based modified histogram equalization for gray scale images

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     

Adaptive contrast enhancement based on histogram modification framework

ABSTRACT

An 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.     

A machine learning approach for brain image enhancement and segmentation

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.     

Residual spatial entropy-based image contrast enhancement and gradient-based relative contrast measurement

This paper proposes a novel algorithm to achieve both global and local contrast enhancement using a new definition of residual spatial entropy of gray levels of an image. Residual spatial entropy is utilized to assign a weight to each gray level which is further used in mapping of input gray levels to output gray levels to achieve global contrast enhancement. A non-linear mapping is applied on transform-domain coefficients of image enhanced globally to perform local contrast enhancement. The algorithm allows to control levels of perceived global and local contrast. New definitions of full-reference relative contrast measures are also introduced. Experimental results show that proposed algorithm produces better or comparable contrast-enhanced images than several state-of-the-art algorithms.     

Contrast enhancement of computed tomography images by adaptive histogram equalization‐application for improved ischemic stroke detection

The visualization of computed tomography brain images is basically done by performing the window setting, which stretches an image from the Digital Imaging and Communications in Medicine format into the standard grayscale format. However, the standard window setting does not provide a good contrast to highlight the hypodense area for the detection of ischemic stroke. While the conventional histogram equalization and other proposed enhanced schemes insufficiently enhance the image contrast, they also may introduce unwanted artifacts on the so‐called “enhanced image.” In this article, a new adaptive method is proposed to excellently improve the image contrast without causing any unwanted defects. The method first decomposed an image into equal‐sized nonoverlapped sub‐blocks. After that, the distribution of the extreme levels in the histogram for a sub‐block is eliminated. The eliminated distribution pixels are then equally redistributed to the other grey levels with threshold limitation. Finally, the grey level reallocation function is defined. The bilinear interpolation is used to estimate the best value for each pixel in the images to remove the potential blocking effect. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 153–160, 2012     

Brightness preserving optimized weighted bi-histogram equalization algorithm and its application to MR brain image segmentation

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.     

Medical image contrast enhancement based on improved sparrow search algorithm

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.     

Intuitionistic fuzzy approach for enhancement of low contrast mammogram images

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.     

Image contrast enhancement with brightness preservation using an optimal gamma correction and weighted sum approach

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.     

Infrared small target enhancement: grey level mapping based on improved sigmoid transformation and saliency histogram

Infrared (IR) small target enhancement plays a significant role in modern infrared search and track (IRST) systems and is the basic technique of target detection and tracking. In this paper, a coarse-to-fine grey level mapping method using improved sigmoid transformation and saliency histogram is designed to enhance IR small targets under different backgrounds. For the stage of rough enhancement, the intensity histogram is modified via an improved sigmoid function so as to narrow the regular intensity range of background as much as possible. For the part of further enhancement, a linear transformation is accomplished based on a saliency histogram constructed by averaging the cumulative saliency values provided by a saliency map. Compared with other typical methods, the presented method can achieve both better visual performances and quantitative evaluations.     

图像对比度增强的非线性变换法

对原始图像用所提出的判据判断图像的对比度类型,针对不同类型直接确定灰度变换参数,实现图像全局对比度增强。再对图像进行离散平稳小波变换,利用所提出的非线性增强方法分别对各个分解层的高频子带进行细节增强。实验表明,提出的方法在有效提高图像整体对比度的同时,又能突出图像中目标的细节部分信息。     

Medical image enhancement algorithm based on NSCT and the improved fuzzy contrast

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     

Gaussian probability bi-histogram equalization for enhancement of the pathological features in medical images

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.     

基于自适应传播机制的红外图像快速增强算法

针对传统的红外图像对比度增强方法存在的运算速度慢、增强效果不佳的问题,提出了一种基于自适应传播机制的红外图像对比度快速增强算法。首先使用自适应传播机制求出红外图像的各种局部空间信息,然后对各种信息进行非线性拉伸调整,得到增强后的图像。最后,比较和分析了几种增强算法实验结果,证明了本方法快速高效的优异性质。     

Optimized contrast enhancement for tumor detection

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.