Image contrast enhancement using fuzzy clustering with adaptive cluster parameter and sub-histogram equalization

Histogram equalization is an effective technique to boost image quality and contrast enhancement. However, in some cases the increase in image contrast by traditional histogram equalization exceeds the desired amount Which damages the image properties and wanes its natural look. Histogram division and performing a separate equalization for each sub-histogram is one of the presented solutions. The dividing method and determining the number of sub-histograms are the main problems directly affecting the output image quality. In this study, a method is introduced for automatic determination of the number of sub-histograms and density based histogram division leading to appropriate output with no need for parameter setting. Each main peak is in a separate section. Image contrast is increased with no loss of image specifications through determining the number of sub-histograms based on the number of main peaks. The introduced histogram equalization approach consists of three stages. The first stage, using histogram analysis, produces an automated estimate of number of clusters for image brightness levels. The second, clusters the image brightness levels, and using the provided transfer function, the final stage includes contrast enhancement for each individual cluster separately. The results of the proposed approach demonstrate not only clearer details along with a boost in contrast, but also noticeably more natural appearance in the images.     

Contrast enhancement for cephalometric images using wavelet-based modified adaptive histogram equalization

Cephalometric images usually have low contrast. The existing techniques for automatic cephalometric analysis usually use histogram equalization for image enhancement. This technique has the advantage of being fully automatic and nonlinear. However, it suffers from spikes, excessive enhancement, and lack of brightness preservation. The proposed technique is an adaptive histogram equalization technique that uses wavelet based gradient histograms. This paper compares its performance with two traditional techniques, three histogram modification based techniques, and two wavelet based techniques. Forty digital and scanned cephalograms are used to conduct tests. In addition to visual histograms and intensity profiles, the proposed method is compared in terms of eight quantitative measures. The various measures are applied to analyze the results in terms of contrast enhancement (EME, CNR), brightness preservation (AMBE), edge conservation and enhancement (H, TEN), preservation of image structures and non-addition distortion (MSSIM, SVD-M). The proposed method gives good contrast enhancement, with better brightness preservation without losing edge information and with the minimum addition of distortions to the enhanced cephalometric images.     

Analog and digital image enhancement using fuzzy expert systems

 Image enhancement is a field that is being used in various areas and disciplines. Advances in computers, microcontrollers and DSP boards have opened new horizons to digital image processing, and have opened many avenues to the design and implementation of new innovative techniques. This paper compares image enhancement via the modification of the probability density function of the gray levels with the new techniques that involves the use of knowledge-base (fuzzy expert) systems that are capable of mimicking the behavior of a human expert. A fuzzy expert system based software for image enhancement, called SmartPhotoLab has been introduced for the above purpose. Present address: A. El-Osery Dept. of Electrical Engineering, New Mexico Tech, Workman Center Rm. 247 801 Leroy place, Socorro, NM 87801 e-mail: elosery@ee.nmt.edu. This work was supported in parts by NASA grants no. NAG2–1196 and 2-1480.     

Bi-histogram equalization using modified histogram bins

The shifting of image mean brightness and the domination of high-frequency bins during histogram equalization (HE) often result in the deteriorating quality of enhanced images and a considerable amount of information loss. This study proposes a novel approach based on bi-histogram equalization to improve its abilities in preserving information entropy and mean brightness. The proposed technique, named Bi-histogram Equalization using Modified Histogram Bins (BHEMHB), segments the input histogram based on the median brightness of an image and alters the histogram bins before HE is applied. Histogram segmentation enables mean brightness preservation, whereas the modification of histogram bins restricts the enhancement rate, thus minimizing the domination effects of high-frequency histogram bins. Simulation results show that BHEMHB significantly outperforms its peers in preserving the details and mean brightness of an image. The output image is visually pleasant with a natural appearance.     

亮度自适应的保熵直方图均衡化方法

直方图均衡化是一种简单有效的图像对比度增强技术,由于它无法保持图像的均值亮度和熵值,因此在实际工程中很少应用。提出了一种直方图规定化的新方法以避免直方图均衡化的缺点,该算法利用直方图均衡化的特点--使直方图分布尽可能均匀,通过变分法求出一个在熵值不变的约束下使得图像均值亮度最大化的直方图,最后将原始直方图转换成直方图规定化后的目标直方图。通过与已有方法HE/DSIHE/MMBEBHE/BPHEME比较,结果表明该方法不仅能够保持熵值,而且可以有效地增强图像对比度,可以用于消费型电子产品中。     

Novel mean-shift based histogram equalization using textured regions

This paper presents a novel mean-shift based histogram equalization method called the MSHE method. The key insight of the proposed MSHE method is that the basis of histogram equalization could be based on textured regions in an image, while impact of smoother regions should be suppressed. Using a mean-shift based approach, the sets of textured regions in an image are determined by finding regions which have a high density of edge concentration. In addition, a new cost function is presented to balance the image quality and contrast enhancement effect for search termination in the proposed algorithm. Based on three typical test images, experimental results show that our proposed MSHE method is quite competitive with the previous eleven methods, such as the HE, BBHE, DSIHE, POHE, RSWHE, DHE, BPDHE, SRHE, GHE, FHE, and THShap.     

Adaptive height-modified histogram equalization and chroma correction in YCbCr color space for fast backlight image compensation

Automatic exposure controls in commercially available cameras often encounter difficulties in capturing scenes with backlight luminance which dominates the entire image. An Adaptive Height-Modified Histogram Equalization (AHMHE) algorithm is proposed as a compensation technique for backlight images. It simultaneously enhances contrast in both the dark and the bright areas without creating regions of degraded local contrast. Moreover AHMHE is an adaptive algorithm: thus it requires minimal user input, and its reduced computational requirement makes it suitable for real-time application. In addition to AHMHE, a chroma correction technique was applied to chroma components in the YCbCr color space to produce more vivid color images. A series of subjective and index evaluations were conducted to measure the resultant image quality improvements by the AHMHE and the chroma correction algorithms.     

基于直方图均衡化的手背静脉图像对比度增强

手背静脉图像对比度往往较低,这将影响整个手背静脉识别系统的识别准确率。首先提取手背静脉图像中的有效区域,然后利用直方图均衡化 (HE) 及其各种改进算法对提取的手背静脉图像进行对比度增强处理。实验结果表明,子块部分重叠局部直方图均衡化算法（POSHE）不但能够增强图像的整体对比度,而且图像中细节与背景之间的对比度也得到了增强,同时该算法效率较高,适合于手背静脉图像的对比度增强处理。     

基于对比度受限自适应直方图均衡的乳腺图像增强

采用对比度受限自适应直方图均衡对乳腺图像进行增强,有效地增强了乳腺图像中的细节,如钙化点、乳导管等组织;并通过对算法中相关参数研究,得到应用于乳腺图像增强的参数优选值,以求获得较好的增强效果,为医师分析影像提供方便。通过与灰度直方图均衡的结果进行比较得出：对比度受限自适应直方图均衡为乳腺数字图像增强的有效方法,在计算机辅助乳腺诊断方面有较高应用价值。     

Segment dependent dynamic multi-histogram equalization for image contrast enhancement

Histogram equalization (HE) method proved to be a simple and most effective technique for contrast enhancement of digital images. However it does not preserve the brightness and natural appearance of the images, which is a major drawback. To overcome this limitation, several Bi- and Multi-HE methods have been proposed. Although the Bi-HE methods significantly enhance the contrast and may preserve the brightness, the natural appearance of the images is not preserved as these methods suffer with the problem of intensity saturation. While Multi-HE methods are proposed to further maintain the brightness and natural appearance of images, but at the cost of contrast enhancement. In this paper, two novel Multi-HE methods for contrast enhancement of natural images, while preserving the brightness and natural appearance of the images, have been proposed. The technique involves decomposing the histogram of an input image into multiple segments based on mean or median values as thresholds. The narrow range segments are identified and are allocated full dynamic range before applying HE to each segment independently. Finally the combined equalized histogram is normalized to avoid the saturation of intensities and un-even distribution of bins. Simulation results show that, for the variety of test images (120 images) the proposed method enhances contrast while preserving brightness and natural appearance and outperforms contemporary methods both qualitatively and quantitatively. The statistical consistency of results has also been verified through ANOVA statistical tool.     

Contrast enhancement of images using Partitioned Iterated Function Systems

A new algorithm for the contrast enhancement of images, based on the theory of Partitioned Iterated Function System (PIFS), is presented. A PIFS consists of contractive transformations, such that the original image is the fixed point of the union of these transformations. Each transformation involves the contractive affine spatial transform of a square block, as well as the linear transform of the gray levels of its pixels. The transformation of the gray levels is determined by two parameters which adjust the brightness and the contrast of the transformed block. The PIFS is used in order to create a lowpass version of the original image. The contrast-enhanced image is obtained by adding the difference of the original image with its lowpass version, to the original image itself. The proposed algorithm uses a predefined constant value for the contrast parameter, whereas, the parameters of the affine spatial transform, as well as the parameter adjusting the brightness, are calculated using k-dimensional trees. The lowpass version of the original image is obtained applying the PIFS on the original image repeatedly while using a value for the contrast parameter that is lower than the predefined one. Quantitative and qualitative results stress the superior performance of the proposed contrast enhancement algorithm against four other widely used contrast enhancement methods; namely, linear and nonlinear unsharp masking, Contrast Limited Adaptive Histogram Equalization and Local Range Modification.     

Gaussian mixture modeling of histograms for contrast enhancement

The current major theme in contrast enhancement is to partition the input histogram into multiple sub-histograms before final equalization of each sub-histogram is performed. This paper presents a novel contrast enhancement method based on Gaussian mixture modeling of image histograms, which provides a sound theoretical underpinning of the partitioning process. Our method comprises five major steps. First, the number of Gaussian functions to be used in the model is determined using a cost function of input histogram partitioning. Then the parameters of a Gaussian mixture model are estimated to find the best fit to the input histogram under a threshold. A binary search strategy is then applied to find the intersection points between the Gaussian functions. The intersection points thus found are used to partition the input histogram into a new set of sub-histograms, on which the classical histogram equalization (HE) is performed. Finally, a brightness preservation operation is performed to adjust the histogram produced in the previous step into a final one. Based on three representative test images, the experimental results demonstrate the contrast enhancement advantage of the proposed method when compared to twelve state-of-the-art methods in the literature.     

数字CR医学图像自适应增强算法研究

数字CR(Computed Radiography)医学放射图像以其高灰阶分辨率、强大的计算机图像后处理功能、小辐射剂量、无胶片诊断、异地会诊等优势,已成为医学成像技术新的热点。然而在成像过程中,由于人体结构和组织的复杂性以及成像系统中的X线散射、电器噪声等各种不利因素的影响导致图像质量的下降,主要表现为细节模糊、对比度差,要对其进行增强处理以改善其视觉质量,便于医生更准确地诊断。而目前通用的CR图像增强方法对比度和噪声增强过度,丢失细节,为此提出一种基于邻域标准差与均值之比自适应增强算法。算法能根据CR图像的邻域标准差与均值之比来调节增强程度的加权因数k,从而自适应的增强CR图像的边缘细节。实验证明,该算法处理后的CR图像细节丰富,信噪比高,具有良好的视觉效果,是一种有效的适合CR医学放射图像的自适应增强算法。     

Extraction and optimization of fuzzy association rules using multi-objective genetic algorithm

Association Rule Mining is one of the important data mining activities and has received substantial attention in the literature. Association rule mining is a computationally and I/O intensive task. In this paper, we propose a solution approach for mining optimized fuzzy association rules of different orders. We also propose an approach to define membership functions for all the continuous attributes in a database by using clustering techniques. Although single objective genetic algorithms are used extensively, they degenerate the solution. In our approach, extraction and optimization of fuzzy association rules are done together using multi-objective genetic algorithm by considering the objectives such as fuzzy support, fuzzy confidence and rule length. The effectiveness of the proposed approach is tested using computer activity dataset to analyze the performance of a multi processor system and network audit data to detect anomaly based intrusions. Experiments show that the proposed method is efficient in many scenarios.

Approaches for automated detection and classification of masses in mammograms

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammography has been one of the most reliable methods for early detection of breast carcinomas. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. The estimated sensitivity of radiologists in breast cancer screening is only about 75%, but the performance would be improved if they were prompted with the possible locations of abnormalities. Breast cancer CAD systems can provide such help and they are important and necessary for breast cancer control. Microcalcifications and masses are the two most important indicators of malignancy, and their automated detection is very valuable for early breast cancer diagnosis. Since masses are often indistinguishable from the surrounding parenchymal, automated mass detection and classification is even more challenging. This paper discusses the methods for mass detection and classification, and compares their advantages and drawbacks.     

低照度图像增强算法的研究与实现

针对低照度图像暗且对比度低的特点,提出了一种将改进的直方图均衡化方法与改进的局部对比度增强方法相结合的低照度图像处理方法,满足了图像增强的两种要求：调节动态范围,增强局部对比度。实验表明该方法在对低照度图像处理时可以达到局部细节对比度增强和全局清晰的效果。     

High dynamic range optimal fuzzy color image enhancement using Artificial Ant Colony System

This paper presents a novel approach for the enhancement of high dynamic range color images using fuzzy logic and modified Artificial Ant Colony System techniques. Two thresholds, the lower and the upper are defined to provide an estimate of the underexposed, mixed-exposed and overexposed regions in the image. The red, green and blue (RGB) color space is converted into Hue Saturation and Value (HSV) color space so as to preserve the chromatic information. Gaussian MFs suitable for the underexposed and overexposed regions of the image are used for the fuzzification. Parametric sigmoid functions are used for enhancing the luminance components of under and over-exposed regions. Mixed-exposed regions are left untouched throughout the process. An objective function comprising of Shannon entropy function as the information factor and visual appeal indicator is optimized using Artificial Ant Colony System to ascertain the parameters needed for the enhancement of a particular image. Visual appeal is preferred over the consideration of entropy so as to make the image human-eye-friendly. Separate power law operators are used for the saturation adjustment so as to restore the lost information. On comparison, this approach is found to be better than the bacterial foraging (BF)-based approach [1].     

Design of fuzzy classifier for diabetes disease using Modified Artificial Bee Colony algorithm

In this study, diagnosis of diabetes disease, which is one of the most important diseases, is conducted with artificial intelligence techniques. We have proposed a novel Artificial Bee Colony (ABC) algorithm in which a mutation operator is added to an Artificial Bee Colony for improving its performance. When the current best solution cannot be updated, a blended crossover operator (BLX-α) of genetic algorithm is applied, in order to enhance the diversity of ABC, without compromising with the solution quality. This modified version of ABC is used as a new tool to create and optimize automatically the membership functions and rules base directly from data. We take the diabetes dataset used in our work from the UCI machine learning repository. The performances of the proposed method are evaluated through classification rate, sensitivity and specificity values using 10-fold cross-validation method. The obtained classification rate of our method is 84.21% and it is very promising when compared with the previous research in the literature for the same problem.     

Microcalcification detection using fuzzy logic and scale space approaches

Breast cancer is one of the leading causes of women mortality in the world. Since the causes are unknown, breast cancer cannot be prevented. It is difficult for radiologists to provide both accurate and uniform evaluation over the enormous number of mammograms generated in widespread screening. Computer-aided mammography diagnosis is an important and challenging task. Microcalcifications and masses are the early signs of breast carcinomas and their detection is one of the key issues for breast cancer control. In this study, a novel approach to microcalcification detection based on fuzzy logic and scale space techniques is presented. First, we employ fuzzy entropy principal and fuzzy set theory to fuzzify the images. Then, we enhance the fuzzified image. Finally, scale-space and Laplacian-of-Gaussian filter techniques are used to detect the sizes and locations of microcalcifications. A free-response operating characteristic curve is used to evaluate the performance. The major advantage of the proposed method is its ability to detect microcalcifications even in the mammograms of very dense breasts. A data set of 40 mammograms (Nijmegen database) containing 105 clusters of microcalcifications is studied. Experimental results demonstrate that the microcalcifications can be accurately and efficiently detected using the proposed approach. It can produce lower false positives and false negatives than the existing methods.     

Modified differential evolution algorithm for contrast and brightness enhancement of satellite images

Satellite images normally possess relatively narrow brightness value ranges necessitating the requirement for contrast stretching, preserving the relevant details before further image analysis. Image enhancement algorithms focus on improving the human image perception. More specifically, contrast and brightness enhancement is considered as a key processing step prior to any further image analysis like segmentation, feature extraction, etc. Metaheuristic optimization algorithms are used effectively for the past few decades, for solving such complex image processing problems. In this paper, a modified differential Modified Differential Evolution (MDE) algorithm for contrast and brightness enhancement of satellite images is proposed. The proposed algorithm is developed with exploration phase by differential evolution algorithm and exploitation phase by cuckoo search algorithm. The proposed algorithm is used to maximize a defined fitness function so as to enhance the entropy, standard deviation and edge details of an image by adjusting a set of parameters to remodel a global transformation function subjective to each of the image being processed. The performance of the proposed algorithm is compared with ten recent state-of-the-art enhancement algorithms. Experimental results demonstrate the efficiency and robustness of the proposed algorithm in enhancing satellite images and natural scenes effectively. Objective evaluation of the compared methods was done using several full-reference and no-reference performance metrics. Qualitative and quantitative evaluation results proves that the proposed MDE algorithm outperforms others to a greater extend.