Contrast Enhancement Of Medical Images Research Articles

A New Approach based on Fuzzy Clustering and Enhancement Operator for Medical Image Contrast Enhancement.

Image enhancement is a very significant topic in image processing that improves the quality of the images. The methods of image enhancement are classified into 3 categories. They include histogram method, fuzzy logic method and optimal method. Studies on image enhancement are often based on rules: if it's bright then it's brighter, if it's dark then it's darker and using the global approach. So, it's hard to enhance objects in all dark and light areas, as in the medical images. Input data is downloaded from the link: http://www.med.harvard.edu/AANLIB. This paper introduces a new algorithm for enhancing medical images that is called the medical image enhancement based on cluster enhancement (MIECE). Firstly, the input image is clustered by the algorithm of fuzzy clustering. Then, the upper bound, and lower bound are calculated according to cluster. Next, the sub-algorithm is implemented for clustering enhancement using an enhancement operator. For each pixel, the gray levels for each channel (R, G, B) are transformed with this sub-algorithm to generate new corresponding gray levels. Because after clustering, each pixel belongs to each cluster with the corresponding membership value. Therefore, the output gray level value will be aggregated from the enhanced gray levels by the sub-algorithm with the weight of the corresponding cluster membership value. This paper experiences the method MIECE with input data downloaded from the link: http://www.med.harvard.edu/AANLIB. The experimental results are compared with some recent methods that include: SGHIE (2017), Ying (2017) and KinD++ (2021). This paper introduces the new algorithm which is based on cluster enhancement (MIECE) to enhance the medical image contrast. The experimental results show that the output images of the proposed algorithm are better than some other recent methods for enhancing dark objects.

Cetacean Optimization Based Medical Image Contrast-Enhancement Technique for Improving Disease Diagnosis in Cardiac MRI

Conventional preprocessing methods in medical imaging can effectively reduce the combination of Gaussian and Impulsive noise, while still maintaining the crucial features such as edges within the images. However, these techniques are found to lag in the optimizing the certain image parameters which play a crucial rule in enhancing the input images and thus may lead to failing in preserving the edges that may adversely affect the early diagnosis of diseases. An attempt has been made to enhance the contrast of cardiac MR (Medical Resonance) images using Cetacean Optimization Algorithm (COA) for improving disease diagnosis. Input cardiac images have been acquired from the free online available databases viz., SCMR consensus and AMRG Atlas. The methodology includes integration of the cost function with contrast measure based on certain metrics to create a newer transformation function. It results in enhanced search pattern using a transformation function in spatial domain and improves the pixel intensity to increase the resolution of an image. The developed algorithm’s performance was evaluated by computing various parameters including peak signal-to-noise ratio (PSNR), structural similarity index measurement (SSIM), mean square error (MSE), and normalized absolute error (NAE). Analysis of the results reveals that the proposed method achieves the highest PSNR (72 dB) and SSIM (0.99) values, along with the lowest NAE (0.16%) and MSE (0.22%) compared to both the particle swarm optimized texture-based histogram equalization (PSOTHE) method and the modified sunflower optimization (MSFO) method. The results indicate that the suggested strategy might improve the intricate details of the input cardiac MRI images. This enhancement can contribute to the efficient identification of cardiac disorder-specific biomarkers.

A new approach of contrast enhancement for Medical Images based on entropy curve

X-ray, MRI, and CT scan images etc, are widely used for the detection of various prevalent diseases. The efficient identification of the disease depends very much on the quality of the image. Sometimes due to environmental conditions such as insufficient and nonuniform illumination, shacking and movement of objects or the imaging device, and some other fault in the device, the resultant image may have low contrast and the presence of noise or artifacts. In such situation, a contrast enhancement algorithm that can efficiently improve the contrast without increasing noise and artifacts can play an important role. In this article, we propose a simple yet efficient method for these types of images by using the image’s entropy curve and homomorphic filtering. It enhances the contrast of medical images without increasing the content of noise and artifacts. Also, the performance of the proposed method is evaluated by a complete qualitative and quantitative analysis.

Optical remote sensing, brightness preserving and contrast enhancement of medical images using histogram equalization with minimum cross-entropy-Otsu algorithm

Optical remote sensing, brightness preserving and contrast enhancement of medical images using histogram equalization with minimum cross-entropy-Otsu algorithm

Medical image contrast enhancement based on improved sparrow search algorithm

AbstractThe 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.

Reversible data hiding with automatic contrast enhancement for medical images

Recent developments in data hiding technology have made reversible data hiding (RDH) a popular research topic. This paper is focused on medical images and the up-to-date RDH methods, and proposes an automatic contrast enhancement algorithm called the RDHACEM algorithm considering two metrics: larger embedding capacity and better image visual quality. The proposed algorithm separates medical images into regions of interest (ROI) and regions of non-interest (NROI). It automatically stretches the ROI's grayscale histogram to simultaneously enlarge the embedding capacity of the ROI and enhance the image contrast. Experimental results show that the marked medical images generated by the proposed algorithm have better visual quality and larger embedding capacity in the ROI than those generated by the up-to-date RDH methods.

Reversible contrast enhancement for medical images with background segmentation

Contrast enhancement (CE) of medical images is helpful to bring out the unclear content in the interested regions. Recently, reversible CE has been proposed so that the original version of a contrast-changed image can be exactly recovered. This property can be used to save storage space or facilitate the archiving system. To enhance the regions of interest (ROI) without introducing visual distortions, the technique of image segmentation (e.g. using Otsu's method) has been used to obtain the background before conducting the CE process. To segment the ROI more accurately, an interactive algorithm called GrabCut is employed in the proposed scheme. In addition, a new preprocessing strategy is adopted to preserve the image quality through the CE process. Consequently, the content in the selected regions can be better brought out while the reversibility of the CE process is achieved. The experimental results on 30 chest radiograph images and 20 magnetic resonance images have demonstrated the efficacy of the proposed scheme for reversible CE. The evaluation results are provided to show the better performances of the proposed method in achieving CE effects and preserving image quality.

A novel reformed histogram equalization based medical image contrast enhancement using krill herd optimization

In this paper, a novel krill herd (KH) based optimized contrast and sharp edge enhancement framework is introduced for medical images. Plateau limit and fitness function are proposed in this paper to achieve the best-enhanced image. A new plateau limit is applied to clip the histogram using minimum, maximum, mean, and median of the histogram with a tunable parameter. The residue pixels are reallocated to the relative vacancy available on histogram bins. This method explores KH meta-heuristic algorithm to automatically adjust the tunable parameter based on a novel fitness function. Fitness function contains two different objective functions, which use edge, entropy, gray level co-occurrence matrix (GLCM) contrast, and GLCM energy of image for best visual, contrast enhancement and improved different characteristic information of the anatomical images. This method is compared with a different state of the art methods to check the viability and vigorous of the scheme and salp swarm algorithm (SSA) optimization is also used for the fair comparison of the proposed approach. The results show that the proposed framework is having superior performance compared to all the existing methods, both qualitatively and quantitatively, in terms of contrast, information content, edge details, and structure similarity.

Contrast enhancement of medical images using a new version of the World Cup Optimization algorithm.

In this paper, a new method for optimal enhancement of the contrast of a medical image is proposed. The main idea is to improve the Gamma correction method to enhance and highlight the image information and the details based on a new design of the World Cup Optimization (WCO) algorithm. Gamma correction is a suitable method for contrast enhancement with an efficiency that directly depends on the correct selection of the Gamma coefficient. In this study, a newly presented algorithm was employed for optimal selection of the Gamma value by considering the entropy, edge content, and multi-objective optimization. The simulation results were compared with 5 state of the art methods for presenting method efficiency. To do this, contrast, homogeneity, weighted average peak signal-to-noise ratio (WPSNR), measure of enhancement (EME), and contrast-to-noise ratio (CNR) were employed. Final results denote that the presented multi-objective optimization algorithm improves the quality of the image contrast and can provide more details and information than the other comparable methods.

Contrast enhancement of medical images using fuzzy set theory and nonsubsampled shearlet transform

AbstractNoises 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.

A Novel Reversible Data Hiding Scheme Based on Lesion Extraction and with Contrast Enhancement for Medical Images

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Contrast Enhancement of Medical Images through Adaptive Genetic Algorithm (AGA) over Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)

Assessment of images after processing is a significant step for determining how good the images are being analyzed. Quality of image is usually estimated with the help of image quality metrics. Unfortunately, most of the commonly used metrics cannot sufficiently portray the visual aspect of the enhanced image. In this proposed system, an approach for medical image enhancement is presented. Here adaptive genetic algorithm is proposed for medical image contrast enhancement. Initially, the chromosomes having gene value of the image gray levels have been generated. After that the fitness function will be calculated for each generated chromosome based on the image edge and their overall intensity values. The selected best chromosomes which have the high fitness value will be given to crossover and mutation operation. In GA the adaptive property is introduced by including adaptive crossover and mutation operations. The proposed method is compared with two different types of optimization algorithms such as Genetic algorithm (GA) and Particle swarm optimization (PSO) that ensure accuracy and quality of medical images in proposed adaptive genetic algorithm (AGA). The experimental solutions are got with the help of metrics like PSNR, SDME, MSE, SSIM, MSSIM, AD, MD, NAE, PSO and SC which proves the proposed algorithm, produces better results as compared to the existing algorithms.

A ROI-based high capacity reversible data hiding scheme with contrast enhancement for medical images

In this paper, we attempt to investigate the secure archiving of medical images which are stored on semi-trusted cloud servers, and focus on addressing the complicated and challenging integrity control and privacy preservation issues. With the intention of protecting the medical images stored on a semi-trusted server, a novel ROI-based high capacity reversible data hiding (RDH) scheme with contrast enhancement is proposed in this paper. The proposed method aims at improving the quality of the medical images effectively and embedding high capacity data reversibly meanwhile. Therefore, the proposed method adopts “adaptive threshold detector” (ATD) segmentation algorithm to automatically separate the “region of interest” (ROI) and “region of non-interest” (NROI) at first, then enhances the contrast of the ROI region by stretching the grayscale and embeds the data into peak bins of the stretched histogram without extending the histogram bins. Lastly, the rest of the required large of data are embedded into NROI region regardless its quality. In addition, the proposed method records the edge location of the segmentation instead of recording the location of the overflow and underflow. The experiment shows that the proposed method can improve the quality of medical images obviously whatever in low embedding rate or high embedding rate when compared with other contrast-based RDH methods.

Optimum wavelet based masking for the contrast enhancement of medical images using enhanced cuckoo search algorithm

Unsharp masking techniques are a prominent approach in contrast enhancement. Generalized masking formulation has static scale value selection, which limits the gain of contrast. In this paper, we propose an Optimum Wavelet Based Masking (OWBM) using Enhanced Cuckoo Search Algorithm (ECSA) for the contrast improvement of medical images. The ECSA can automatically adjust the ratio of nest rebuilding, using genetic operators such as adaptive crossover and mutation. First, the proposed contrast enhancement approach is validated quantitatively using Brain Web and MIAS database images. Later, the conventional nest rebuilding of cuckoo search optimization is modified using Adaptive Rebuilding of Worst Nests (ARWN). Experimental results are analyzed using various performance matrices, and our OWBM shows improved results as compared with other reported literature.

A reversible data hiding method with contrast enhancement for medical images

In this paper, a reversible data hiding method with contrast enhancement is presented for medical images. Firstly, image background segmentation is performed and the principal gray-scale values in the segmented background are identified. By excluding the corresponding histogram bins from being expanded for data hiding, the contrast of region of interest (ROI) in medical images can be selectively enhanced. Considering the characteristics of pixel distribution, we develop a new pre-processing strategy to reduce the visual distortions that may be caused. With the proposed method, an original image can be exactly recovered from the corresponding enhanced image by hiding the side information within it. The experimental results on a set of medical images show that the visibility of ROI can be improved. Compared with the previous method, the proposed method can achieve more contrast enhancement effects and better visual quality for medical images.

Contrast enhancement of digital mammograms using a novel walking ant histogram equalisation

Contrast enhancement of medical images could improve the quality of its visual contents for better diagnosis. In this paper, a novel walking ant histogram equalisation WAHE is proposed to improve the contrast of digital mammograms. The proposed method is hybridisation between the histogram equalisation with a novel ant colony optimisation ACO algorithm. Histogram equalisation is the concept of transferring input intensities to desired output intensities while improving the visual content of the images. Here we have used ACO for constructing the transfer functions. A fitness function is employed to control the transfer function construction in a balanced way. The experiments are carried out with the digital mammograms received from MIAS database, and results indicate the latent performance of the proposed contrast enhancement approach over the existing with finest results on all the contrast enhancement measures and the classification accuracy.

An opposition-based firefly algorithm for medical image contrast enhancement

Image enhancement is a crucial pre-processing step in almost every medical imaging system. Different types of degradation can occur in medical images such as noise, blur and contrast imperfection. Filtering techniques have been successfully applied for denoising and deblurring images, while contrast enhancement has been achieved using histogram equalisation. This technique has the main drawbacks of losing details included in minor grey levels or over-enhancement. As a solution, the grey-level mapping technique has been adopted. Defining the new set of grey levels, to substitute those of the input image, using an exhaustive search is computationally complex; so metaheuristics are generally used. In this topic, this paper presents a new opposition-based firefly algorithm to search the best target set of grey levels for medical image contrast enhancement. The obtained results are compared against those obtained by histogram equalisation and classical variants of the firefly algorithm.

An improved medical image enhancement scheme using Type II fuzzy set

A contrast enhancement of medical images using Type II fuzzy set theory is suggested. Fuzzy set theory considers uncertainty in the form of membership function but to have better information on uncertainty on the membership function, Type II fuzzy set is considered. Type II fuzzy set considers fuzziness in the membership function. Hamacher T co norm is used as an aggregation operator to form a new membership function using the upper and lower membership function of Type II fuzzy set. The image with the new membership function is an enhanced image. As medical images contain lot of uncertainties, Type II fuzzy set may be a good tool for medical image analysis. To show the effectiveness of the proposed method, the results are compared with fuzzy, intuitionistic fuzzy, and existing Type II fuzzy methods. Experiments on several images show that the proposed Type II fuzzy method performs better than the existing methods. To show the advantage of the proposed enhancement method, detection or extraction of abnormal lesions or blood vessels has been carried out on enhanced images of all the methods. It is observed that the segmented results on the proposed enhanced images are better.

Contrast Enhancement of Medical Images Using Sigmoid-type Transfer Curves for Wavelet Coefficient Weighting Adjustment

Contrast Enhancement of Medical Images Using Sigmoid-type Transfer Curves for Wavelet Coefficient Weighting Adjustment

Real-time edge-aware image processing with the bilateral grid

We present a new data structure---the bilateral grid , that enables fast edge-aware image processing. By working in the bilateral grid, algorithms such as bilateral filtering, edge-aware painting, and local histogram equalization become simple manipulations that are both local and independent. We parallelize our algorithms on modern GPUs to achieve real-time frame rates on high-definition video. We demonstrate our method on a variety of applications such as image editing, transfer of photographic look, and contrast enhancement of medical images.