Adaptive Histogram Equalization Method Research Articles

A Real Time Vein Detection System

<p>Blood veins detection process can be cumbersome for nurses and medical practioners when it comes to special overweight type of patients.This simple routine procedure can lead the process into an extreme calamity for these patients. In this paper, we emphasized on a process for the detection of the vein in real time using the consecrations of Matlab to prevent or at least reduce the number of inescapable calamity for patients during the infusion of a needle by phlebotomy or doctor in everyday lives. Hemoglobin of the blood tissues engrossed the Near Infrared (NIR) illuminated light and Night vision camera is used to capture the scene and enhance the vein pattern clearly using Contrast Limited Adaptive Histogram Equalization (CLAHE) method. This simple approach can successfully also lead to localizing bleeding spots, clots from stroke …etc among other things.</p>

Contrast enhancement for cDNA microarray image based on fourth-order moment

Microarray technology, which can monitor the expression levels of thousands of genes simultaneously, has been widely used in biological experiment. Image processing, as one key step in microarray technology, plays an essential role in microarray analysis. Meanwhile, biological applications require a higher accuracy in each image processing step. However, the low contrast levels of image make it difficult to obtain better processing precision. This paper proposes a fully automatic contrast enhancement (CE) method based on fourth-order moment. Also, a background estimation method is presented to obtain a better microarray image processing result. Comparative results on contrast enhance and gridding revealed that the proposed CE algorithm performs better compared to the adaptive histogram equalization method. Numerous experiments on the Swiss Institute of Bioinformatics (SIB), Joe DeRisi individual (DeRisi), Gene Expression Omnibus (GEO), and Stanford Microarray Database (SMD) data sets also indicate that the proposed CE exerts a tremendous effect on gridding, but has nothing to do with segmentation.

Illumination invariant facial expression recognition using selected merged binary patterns for real world images

Automatic facial expression recognition has always been a challenging task to understand human behavior from real world images. Certain type of issues are associated with such images that include poor illumination, different orientations and varying pose. The proposed technique first applies Fast Fourier Transform and Contrast Limited Adaptive Histogram Equalization (FFT + CLAHE) method to compensate the poor illumination. Then merged binary pattern code (MBPC) is generated for every pixel. Two bits per neighbourhood are produced to form a 16-bit code per pixel. This code merges local features to improve the effectiveness of facial expression recognition system. MBPC descriptor captures changes along fine edges and prominent pattern around eyes, eye brows, mouth, bulges and wrinkles of the face. The results of proposed technique are compared with different variants of LBP and LGC based techniques for both holistic and zoned images. Static Facial Expression in Wild (SFEW) dataset is selected for experimentation. Results clearly indicate that the suggested MBPC based technique surpasses other techniques with 96.5% and 67.2% accuracy for holistic and division based approach respectively. Moreover, results indicate that the performance of holistic approach is much higher than division based approach.

Supervised retinal vessel segmentation from color fundus images based on matched filtering and AdaBoost classifier.

The structure and appearance of the blood vessel network in retinal fundus images is an essential part of diagnosing various problems associated with the eyes, such as diabetes and hypertension. In this paper, an automatic retinal vessel segmentation method utilizing matched filter techniques coupled with an AdaBoost classifier is proposed. The fundus image is enhanced using morphological operations, the contrast is increased using contrast limited adaptive histogram equalization (CLAHE) method and the inhomogeneity is corrected using Retinex approach. Then, the blood vessels are enhanced using a combination of B-COSFIRE and Frangi matched filters. From this preprocessed image, different statistical features are computed on a pixel-wise basis and used in an AdaBoost classifier to extract the blood vessel network inside the image. Finally, the segmented images are postprocessed to remove the misclassified pixels and regions. The proposed method was validated using publicly accessible Digital Retinal Images for Vessel Extraction (DRIVE), Structured Analysis of the Retina (STARE) and Child Heart and Health Study in England (CHASE_DB1) datasets commonly used for determining the accuracy of retinal vessel segmentation methods. The accuracy of the proposed segmentation method was comparable to other state of the art methods while being very close to the manual segmentation provided by the second human observer with an average accuracy of 0.972, 0.951 and 0.948 in DRIVE, STARE and CHASE_DB1 datasets, respectively.

Deep architecture neural network-based real-time image processing for image-guided radiotherapy

IntroductionTo develop real-time image processing for image-guided radiotherapy, we evaluated several neural network models for use with different imaging modalities, including X-ray fluoroscopic image denoising. Methods & materialsSetup images of prostate cancer patients were acquired with two oblique X-ray fluoroscopic units. Two types of residual network were designed: a convolutional autoencoder (rCAE) and a convolutional neural network (rCNN). We changed the convolutional kernel size and number of convolutional layers for both networks, and the number of pooling and upsampling layers for rCAE. The ground-truth image was applied to the contrast-limited adaptive histogram equalization (CLAHE) method of image processing. Network models were trained to keep the quality of the output image close to that of the ground-truth image from the input image without image processing. For image denoising evaluation, noisy input images were used for the training. ResultsMore than 6 convolutional layers with convolutional kernels >5×5 improved image quality. However, this did not allow real-time imaging. After applying a pair of pooling and upsampling layers to both networks, rCAEs with >3 convolutions each and rCNNs with >12 convolutions with a pair of pooling and upsampling layers achieved real-time processing at 30 frames per second (fps) with acceptable image quality. ConclusionsUse of our suggested network achieved real-time image processing for contrast enhancement and image denoising by the use of a conventional modern personal computer.

Evaluation of an automated thresholding algorithm for the quantification of paraspinal muscle composition from MRI images

BackgroundThe imaging assessment of paraspinal muscle morphology and fatty infiltration has gained considerable attention in the past decades, with reports suggesting an association between muscle degenerative changes and low back pain (LBP). To date, qualitative and quantitative approaches have been used to assess paraspinal muscle composition. Though highly reliable, manual thresholding techniques are time consuming and not always feasible in a clinical setting. The tedious and rater-dependent nature of such manual thresholding techniques provides the impetus for the development of automated or semi-automated segmentation methods. The purpose of the present study was to develop and evaluate an automated thresholding algorithm for the assessment of paraspinal muscle composition. The reliability and validity of the muscle measurements using the new automated thresholding algorithm were investigated through repeated measurements and comparison with measurements from an established, highly reliable manual thresholding technique.MethodsMagnetic resonance images of 30 patients with LBP were randomly selected cohort of patients participating in a project on commonly diagnosed lumbar pathologies in patients attending spine surgeon clinics. A series of T2-weighted MR images were used to train the algorithm; preprocessing techniques including adaptive histogram equalization method image adjustment scheme were used to enhance the quality and contrast of the images. All muscle measurements were repeated twice using a manual thresholding technique and the novel automated thresholding algorithm, from axial T2-weigthed images, at least 5 days apart. The rater was blinded to all earlier measurements. Inter-method agreement and intra-rater reliability for each measurement method were assessed. The study did not received external funding and the authors have no disclosures.ResultsThere was excellent agreement between the two methods with inter-method reliability coefficients (intraclass correlation coefficients) varying from 0.79 to 0.99. Bland and Altman plots further confirmed the agreement between the two methods. Intra-rater reliability and standard error of measurements were comparable between methods, with reliability coefficient varying between 0.95 and 0.99 for the manual thresholding and 0.97–0.99 for the automated algorithm.ConclusionThe proposed automated thresholding algorithm to assess paraspinal muscle size and composition measurements was highly reliable, with excellent agreement with the reference manual thresholding method.

The Application of the Montage Image Mosaic Engine to the Visualization of Astronomical Images

The Montage Image Mosaic Engine was designed as a scalable toolkit, written in C for performance and portability across *nix platforms, that assembles FITS images into mosaics. This code is freely available and has been widely used in the astronomy and IT communities for research, product generation, and for developing next-generation cyber-infrastructure. Recently, it has begun finding applicability in the field of visualization. This development has come about because the toolkit design allows easy integration into scalable systems that process data for subsequent visualization in a browser or client. The toolkit it includes a visualization tool suitable for automation and for integration into Python: mViewer creates, with a single command, complex multi-color images overlaid with coordinate displays, labels, and observation footprints, and includes an adaptive image histogram equalization method that preserves the structure of a stretched image over its dynamic range. The Montage toolkit contains functionality originally developed to support the creation and management of mosaics, but which also offers value to visualization: a background rectification algorithm that reveals the faint structure in an image; and tools for creating cutout and downsampled versions of large images. Version 5 of Montage offers support for visualizing data written in HEALPix sky-tessellation scheme, and functionality for processing and organizing images to comply with the TOAST sky-tessellation scheme required for consumption by the World Wide Telescope (WWT). Four online tutorials allow readers to reproduce and extend all the visualizations presented in this paper.

Smoothing and enhancement algorithms for underwater images based on partial differential equations

The formulation and application of an algorithm based on partial differential equations for processing underwater images are presented. The proposed algorithm performs simultaneous smoothing and enhancement operations on the image and yields better contrast enhancement, color correction, and rendition compared to conventional algorithms. Further modification of the proposed algorithm and its combination with the powerful contrast-limited adaptive histogram equalization (CLAHE) method using an adaptive computation of the clip limit enhances the local enhancement results while mitigating the color distortion and intrinsic noise enhancement observed in the CLAHE algorithm. Ultimately, an optimized version of the algorithm based on image information metric is developed for best possible results for all images. The method is compared with existing algorithms from the literature using subjective and objective measures, and results indicate considerable improvement over several well-known algorithms.

An automatic active contour method for sea cucumber segmentation in natural underwater environments

Sea cucumbers have become an important sector of the marine industry in northern China, with a culture area exceeding one million acres and a production value over one hundred and twenty million dollars. However, sea cucumber culture and fishing are mainly dependent on manual work. To promote the development of sea cucumber culture automation, it is necessary to research sea cucumber automatic segmentation based on machine vision in natural underwater environments. Sea cucumbers usually live in an environment where lighting, visibility and stability are generally not controllable, which cause underwater images of sea cucumbers to be distorted, blurred, and severely attenuated. Moreover, sea cucumbers are flexible and colored much like sandy sediments. Therefore, it is difficult to fully separate a cucumber from the background in an underwater image. For fast and accurate automatic segmenting of sea cucumbers, an improved method based on active contour is presented in this paper. Image fusion based on the RGB color space and the contrast limited adaptive histogram equalization (CLAHE) method are used to increase the contrast of the sea cucumber thorns and body, respectively. Then, an edge detection algorithm is proposed to extract the edge of the sea cucumber thorns as an initial contour for the thorn segmentation, and a rectangular contour based on the edge information is built as the initial contour for the body segmentation. Finally, the results of the thorn and body are fused. All the procedures are automatically completed without human intervention. Qualitative and quantitative analysis indicates that the proposed method outperformed the other two compared methods in sea cucumber segmentation. A test with 120 samples showed that for the proposed method, the mean values of Euclidean distance, sensitivity, specificity, and accuracy were 12.7, 84.51, 96.97, and 96.54, respectively. The average time to run the algorithm for all images is 4.27s. Thus, the proposed method could work for sea cucumber monitoring and fishing in real time.

Finger vein recognition based on finger crease location

Finger vein recognition technology has significant advantages over other methods in terms of accuracy, uniqueness, and stability, and it has wide promising applications in the field of biometric recognition. We propose using finger creases to locate and extract an object region. Then we use linear fitting to overcome the problem of finger rotation in the plane. The method of modular adaptive histogram equalization (MAHE) is presented to enhance image contrast and reduce computational cost. To extract the finger vein features, we use a fusion method, which can obtain clear and distinguishable vein patterns under different conditions. We used the Hausdorff average distance algorithm to examine the recognition performance of the system. The experimental results demonstrate that MAHE can better balance the recognition accuracy and the expenditure of time compared with three other methods. Our resulting equal error rate throughout the total procedure was 3.268% in a database of 153 finger vein images.

Image Enhancement using Contrast Limited Adaptive Histogram Equalization and Wiener filter

This paper presents the Image Enhancement using Contrast Limited Adaptive Histogram Equalization method and Wiener filter to remove the noise that might be presented in image. Gamma correction technique is used to transfer the image in to a suitable dynamic range. To avoid amplifying any noise that might be present in an image we use contrast limited adaptive histogram equalization parameter to limit the contrast especially in homogeneous area.

Noise-Free Rule-Based Fuzzy Image Enhancement

Different kinds of noises have considerable effects on most of image sensing systems. Suitable image contrast enhancement algorithms can improve contrast or retain detail information while reducing noises as well. Fuzzy representation of an image provides a reliable analysis when inexactness occurred at the gray level values. This paper presents a fuzzy-based novel image contrast enhancement method. Several image quality indices, such as similarity, naturalness, and mean brightness preserving examined and experimentally show the effectiveness of the proposed technique in comparison with well-known image enhancement methods such as histogram equalization and contrast limited adaptive histogram equalization methods.

A joint implementation of adaptive histogram equalization and interpolation

This paper proposed a new method to improve the visibility and contrast of color images. The proposed method is a joint technique scheme of Color Adaptive Histogram Equalization (CAHE) and interpolation method. CAHE is an effective algorithm for image contrast enhancement, and interpolation method enhances the image by increasing the number of pixels without missing any intermediate component of image. Hence, the combination of both may give the good agreement of image visuality and contrast.

Color medical image enhancement based on adaptive equalization of intensity numbers matrix histogram

The enhancement technique for color medical images is conductive to improve the resolution and accuracy of the original image. A new enhancement method combining the Young-Helmholtz (Y-H) transformation with the adaptive equalization of intensity numbers matrix histogram is proposed in this paper. The adaptive histogram equalization method is applied to strengthen the details, enhance the contrast, and suppress the noise of the original image effectively. The enhanced image can be displayed in the red-greenblue (RGB) color space through inverse Y-H transformation with the same hue and saturation. The experiment results demonstrate that the method has the enhancement effect with low computational complexity, which provides the foundation for the medical diagnosis and further processing of medical images.

An approach to analyze the breast tissues in infrared images using nonlinear adaptive level sets and Riesz transform features.

Breast thermography is a potential imaging method for the early detection of breast cancer. The pathological conditions can be determined by measuring temperature variations in the abnormal breast regions. Accurate delineation of breast tissues is reported as a challenging task due to inherent limitations of infrared images such as low contrast, low signal to noise ratio and absence of clear edges. Segmentation technique is attempted to delineate the breast tissues by detecting proper lower breast boundaries and inframammary folds. Characteristic features are extracted to analyze the asymmetrical thermal variations in normal and abnormal breast tissues. An automated analysis of thermal variations of breast tissues is attempted using nonlinear adaptive level sets and Riesz transform. Breast thermal images are initially subjected to Stein's unbiased risk estimate based orthonormal wavelet denoising. These denoised images are enhanced using contrast-limited adaptive histogram equalization method. The breast tissues are then segmented using non-linear adaptive level set method. The phase map of enhanced image is integrated into the level set framework for final boundary estimation. The segmented results are validated against the corresponding ground truth images using overlap and regional similarity metrics. The segmented images are further processed with Riesz transform and structural texture features are derived from the transformed coefficients to analyze pathological conditions of breast tissues. Results show that the estimated average signal to noise ratio of denoised images and average sharpness of enhanced images are improved by 38% and 6% respectively. The interscale consideration adopted in the denoising algorithm is able to improve signal to noise ratio by preserving edges. The proposed segmentation framework could delineate the breast tissues with high degree of correlation (97%) between the segmented and ground truth areas. Also, the average segmentation accuracy and sensitivity are found to be 98%. Similarly, the maximum regional overlap between segmented and ground truth images obtained using volume similarity measure is observed to be 99%. Directionality as a feature, showed a considerable difference between normal and abnormal tissues which is found to be 11%. The proposed framework for breast thermal image analysis that is aided with necessary preprocessing is found to be useful in assisting the early diagnosis of breast abnormalities.

Colour Contrast Enhancement Method by Scaling the DC Coefficients in CIE-LAB Colour Space

Colour and brightness of the captured images from low cost digital cameras are usually different from the colour and brightness of the original scene. Many factors are responsible for the low contrast images such as non uniform illumination, large or small exposure of some specific regions, and the unwanted movements of camera users. Due to these reasons the captured image to looks annoying to human visual system. Thus, contrast enhancement methods are required for improving the visual appearance of the colour images. In this paper the performance of contrast limited adaptive histogram equalization (CLAHE) method is compared with method of DC coefficient scaling in the compressed DCT domain for enhancing the contrast of colour images. In addition, as a modification DC coefficient scaling is also implemented in the CIE-Lab colour space. The contrast is only enhanced in L component and keeping the colour information preserved. This improves the entropy of standard method. For comparing the performance along with SNR and entropy the absolute standard deviation difference is also used as parameters. The methods are tested on various true colour images from different environment. General Terms Image processing, Image enhancement, Colour spaces

Performance Evaluation of Spatial Domain Contrast Enhancement Techniques for Underwater Images

Image enhancement methods are widely used for improving the feature and quality of the images. Researchers have designed various contrast enhancement methods in the past. But since underwater image contain low contrast and resolution due to poor visibility conditions and refractions of light rays. Thus analysis and identification of the underwater object is a challenging and difficult task. In this paper presents a comparative analysis of various contrast enhancement techniques for such underwater images. Performance of contrast limited adaptive histogram equalization (CLAHE) method is compared with contrast stretching, global histogram equalization (HE) Brightness preserving Bi-histogram equalization (BBHE) and finally Dual Sub-Image histogram equalization (DSIHE) methods. Different colour underwater images are enhanced in the RGB colour space. The performance is evaluated based on discrete entropy, signal to noise ratio (SNR) and Standard deviation difference and the mean brightness. Performance of methods is evaluated under various type of underwater image environment. General Terms Image processing, Image enhancement, Underwater Images

Spatially Adaptive Histogram Equalization for Single Image-Based Ghost-free High Dynamic Range Imaging

In this paper, we present a spatially adaptive histogram equalization method for generating a ghost-free high dynamic range (HDR) image using a single input image. Existing multiple input-based HDR methods fuse multiple low dynamic range (LDR) images, acquired using different exposures. However, these methods work only under the assumption that neither global nor local motions exists in between the LDR images. To overcome such an unrealistic constraint, we generate two LDR images from a single input image. To generate LDR images with appropriate exposures, we divide the entire intensity range into multiple sub-ranges using histogram quantization and separately perform histogram equalization in each sub-range. Thus, we can acquire a set of differently exposed LDR images of the same scene, which are then fused to generate a ghost-free HDR image. The major contribution of this work is twofold: (i) a novel estimation method for providing optimal sub-ranges of intensity using histogram quantization, which (ii) requires no additional hardware for generating multiple LDR images. Because the proposed method uses a set of optimally self-generated LDR images, it is inherently free of ghost artifacts and can provide a ghost-free HDR function for low-cost, lightweight imaging devices, such as mobile phones and compact cameras.

A combined approach for the enhancement and segmentation of mammograms using modified fuzzy C-means method in wavelet domain.

In this paper, a combined approach for enhancement and segmentation of mammograms is proposed. In preprocessing stage, a contrast limited adaptive histogram equalization (CLAHE) method is applied to obtain the better contrast mammograms. After this, the proposed combined methods are applied. In the first step of the proposed approach, a two dimensional (2D) discrete wavelet transform (DWT) is applied to all the input images. In the second step, a proposed nonlinear complex diffusion based unsharp masking and crispening method is applied on the approximation coefficients of the wavelet transformed images to further highlight the abnormalities such as micro-calcifications, tumours, etc., to reduce the false positives (FPs). Thirdly, a modified fuzzy c-means (FCM) segmentation method is applied on the output of the second step. In the modified FCM method, the mutual information is proposed as a similarity measure in place of conventional Euclidian distance based dissimilarity measure for FCM segmentation. Finally, the inverse 2D-DWT is applied. The efficacy of the proposed unsharp masking and crispening method for image enhancement is evaluated in terms of signal-to-noise ratio (SNR) and that of the proposed segmentation method is evaluated in terms of random index (RI), global consistency error (GCE), and variation of information (VoI). The performance of the proposed segmentation approach is compared with the other commonly used segmentation approaches such as Otsu's thresholding, texture based, k-means, and FCM clustering as well as thresholding. From the obtained results, it is observed that the proposed segmentation approach performs better and takes lesser processing time in comparison to the standard FCM and other segmentation methods in consideration.

Contrast Enhancement and Brightness Preservation Using Multi-Decomposition Histogram Equalization

Histogram Equalization (HE) has been an essential addition to the Image Enhancement world. Enhancement techniques like Classical Histogram Equalization (CHE), Adaptive Histogram Equalization (ADHE), Bi-Histogram Equalization (BHE) and Recursive Mean Separate Histogram Equalization (RMSHE) methods enhance contrast, however, brightness is not well preserved with these methods, which gives an unpleasant look to the final image obtained. Thus, we introduce a novel technique Multi-Decomposition Histogram Equalization (MDHE) to eliminate the drawbacks of the earlier methods. In MDHE, we have decomposed the input sixty-four parts, applied CHE in each of the sub-images and then finally interpolated them in correct order. The final image after MDHE results in contrast enhanced and brightness preserved image compared to all other techniques mentioned above. We have calculated the various parameters like PSNR, SNR, RMSE, MSE, etc. for every technique. Our results are well supported by bar graphs, histograms and the parameter calculations at the end.