Contrast-limited Adaptive Histogram Equalization Method Research Articles

Heuristic strategy using hybrid deep learning with transfer learning for oral cancer detection

Oral cancer becomes the most disastrous ailment that affects the oral cavity parts of the mouth. Oral cancer diagnosis is the main challenge in the medical field. It becomes expensive and less capable of classifying oral cancer. In some cases, it may cause unnecessary morbidity and mortality. Recently, the detection of malignant and premalignant oral lesions has been a critical process owing to their low image resolution and lower acquisition time. Thus, a novel hybrid deep learning with meta-heuristic-based optimization is proposed. The pre-processing occurs by median filtering and Contrast Limited Adaptive Histogram Equalization (CLAHE). The CLAHE method is utilized to reduce unwanted noise. Finally, the classification is done by a proposed hybrid-based deep learning model termed as Recurrent Deep Belief Network (RDBN), in which the Deep Belief Network (DBN) is incorporated with the Recurrent Neural Network (RNN). Here, the RDBN helps to increase the performance classification. Furthermore, the hyperparameters of the RDBN model, such as learning rate, epochs and hidden neurons, are tuned using the proposed Hybrid Beetle-Barnacle Swarm Optimization (HBBSO) algorithm, where the Barnacles Mating Optimizer (BMO) is superimposed with Beetle Swarm Optimization (BSO) algorithms. In the given proposed model, the selected features are extracted by the optimization algorithms. Here, the parameters are fine-tuned to get the better optimal solution. From the experimental outcome, the developed model has acquired 5.2% better than PSO-RDBN, 5.6% improved than GWO-RDBN, 3.2% enhanced than BSO-RDBN and 3.5% superior to BMO-RDBN regarding accuracy. Thus, the proposed model achieves higher results for detecting oral cancer with enhanced classification performance than the existing approaches.

Performance Comparison of GLCM Features and Preprocessing Effect on Batik Image Retrieval

The use of the Grey-Level Co-occurrence Matrix (GLCM) for feature extraction in image retrieval with complex motifs, such as batik images, has been widely used. Some features often extracted include energy, entropy, correlation, and contrast. Other than these four features, the addition of dissimilarity and homogeneity features to the GLCM method is proposed in this study. Preprocessing methods such as Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) are also used to see whether the two methods can increase the precision value of the retrieval results. This study used the Batik 300 dataset, which consists of 50 classes. Batik was chosen because this type of image has complex patterns and motifs so that it will maximize the role of the GLCM method itself. In addition, Batik is also a world heritage art, so its sustainability needs to be maintained. The test results show that adding dissimilarity and homogeneity features and using the CLAHE method in the preprocessing step can improve model performance. Combining these two methods has produced higher precision values than not using either. Batik, a globally recognized art form, holds the status of a world heritage, necessitating the preservation of its sustainability. Test results have demonstrated that incorporating dissimilarity and homogeneity features, alongside using the CLAHE method during the preprocessing stage, leads to enhanced model performance. The amalgamation of these two methods has yielded precision values that surpass those achieved when either method is used in isolation.

Identifying License Plates in Distorted Vehicle Images: Detecting Distorted Vehicle Licence Plates Using a Novel Preprocessing Methods With Hybrid Feature Descriptors

Intelligent transportation systems (ITSs) play a key role in many people’s lives with different aspects. Most of the ITS applications include a system for detecting license plates (LPs) in transportation vehicles. A new framework for such detection systems is introduced in this article. It includes a novel technique for preprocessing, extraction, and detection stages to identify LPs from distorted vehicle images. An efficient preprocessing method is developed in this study. An enhanced, contrast-limited adaptive histogram equalization method for filtering the unwanted LP features is proposed. At the features-extraction stage, strong hybrid features from extended local binary patterns with a median robust pattern descriptor and speeded-up robust feature descriptor are applied to extract complicated features from LP areas. Those hybrid features can enhance the useful information, and the detection system performed well under difficult scenarios. In the detection stage, the trained model by an extreme learning machine (ELM) classifier, with mean-shift algorithm, is used as a detector to decide output results. Performances of the proposed framework were compared with other classifiers using true- and false-positive rates. The system’s performance improvements by the proposed method were also compared with our previous methods and the existing detection methods in the literature. The experiments on an English car LP’s database and other language vehicle images showed that the proposed method made significant improvements to the accuracy and runtime speed for the detection system under difficult image conditions.

Real-Time CLAHE Algorithm Implementation in SoC FPGA Device for 4K UHD Video Stream

One of the problems encountered in the field of computer vision and video data analysis is the extraction of information from low-contrast images. This problem can be addressed in several ways, including the use of histogram equalisation algorithms. In this work, a method designed for this purpose—the Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithm—is implemented in hardware. An FPGA platform is used for this purpose due to the ability to run parallel computations and very low power consumption. To enable the processing of a 4K resolution (UHD, 3840 × 2160 pixels) video stream at 60 fps (frames per second) by using the CLAHE method, it is necessary to use a vector data format and process multiple pixels simultaneously. The algorithm realised in this work can be a component of a larger vision system, such as in autonomous vehicles or drones, but it can also support the analysis of underwater, thermal, or medical images both by humans and in an automated system.

Towards Better Segmentation of Abnormal Part in Multimodal Images Using Kernel Possibilistic C Means Particle Swarm Optimization With Morphological Reconstruction Filters

The authors designed an automated framework to segment tumors with various image sequences like T1, T2, and post-processed MRI multimodal images. Contrast-limited adaptive histogram equalization method is used for preprocessing images to enhance the intensity level and view the tumor part clearly. With the combination of kernel possibilistic c means clustering with particle swarm optimization technique, a tumor part is segmented, and morphological filters are applied to remove the unrelated outlier pixels in the segmented image to detect the accurate tumor part. The authors collected various image sequences from online resources like Harvard brain dataset, BRATS, and RIDER, and a few from clinical datasets. Efficiency is ensured by computing various performance metrics like Jaccard Index MSE, PSNR, sensitivity, specificity, accuracy, and computational time. The proposed approach yields 97.06% segmentation accuracy and 98.08% classification accuracy for multimodal images with an average of 5s for all multimodal images.

Assessment of Some Enhancement Methods of Renal X-ray Image

The study focuses on assessment of the quality of some image enhancement methods which were implemented on renal X-ray images. The enhancement methods included Imadjust, Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The images qualities were calculated to compare input images with output images from these three enhancement techniques. An eight renal x-ray images are collected to perform these methods. Generally, the x-ray images are lack of contrast and low in radiation dosage. This lack of image quality can be amended by enhancement process. Three quality image factors were done to assess the resulted images involved (Naturalness Image Quality Evaluator (NIQE), Perception based Image Quality Evaluator (PIQE) and Blind References Image Spatial Quality Evaluator (BRISQE)). The quality of images had been heightened by these methods to support the goals of diagnosis. The results of the chosen enhancement methods of collecting images reflected more qualified images than the original images. According to the results of the quality factors and the assessment of radiology experts, the CLAHE method was the best enhancement method.

Contrast Enhancement for Improved Detection of Ananas Comosus from Aerial Imagery

This paper describes contrast enhancement method used in detecting Ananas Comosus from aerial images. The main challenge faced by many researchers is to deal with image under low contrast conditions. Under low contrast, target images of Ananas Comosus often blends with the background features such as leaves and ground, making detection difficult. This study is set out to investigate the usefulness of implementing contrast enhancement technique on existing image to improve detection performance. Aerial images under study were obtained at Simpang Renggam pineapple plantation using DJI Phantom 3 and subjected to two image pre-processing methods-intensity adjustment method and contrast-limited adaptive histogram equalization methods. As a control, these images were also evaluated against pre-processing method without any contrast enhancement. The performance of the techniques for detecting Ananas Comosus is then assessed using relative error, RE and mean absolute percentage, MAPE. The best results were obtained by using contrast-limited adaptive histogram equalization method; yielding RE of 0.125 and MAPE of 0.008% compared to intensity adjustment technique yielding maximum RE of 0.286 and MAPE of 0.027%. Meanwhile detection without image enhancement had resulted maximum RE of 0.250 and MAPE is 0.024%.

Counting of Bacterial Colonies of the Low Quality Image Using Perona-Malik Diffusion Filters and Image Morphology Operators

Microbiology has an important role in solving human problems that are complex in many fields, such as environment, medical, and biotechnology. One of the important researches in microbiology fields is the counting of the number of bacterial colonies. This research is very important to estimate the number of bacterial cells in each millilitre or gram sample. However, the manual counting of bacterial colonies is a time-consuming task and is also very tedious. To help microbiologists, an automatic counting method of bacterial colonies based on bacterial colonies images is needed to be developed. However, the bacterial colonies images taking by the phones camera has low-quality. They are noise presence and lack of contrast, so the pre-processing method is necessary to be developed. This paper proposes the pre-processing method by using modified Perona-Malik Diffusion (PMD) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The modified PMD filter is used to reduce noise, while the CLAHE method is used to enhance the contrast. Moreover, the morphology operators are used for the petri dish extraction and bacterial colonies counting process. The experimental results show that the proposed method has better performance than the original PMD based on the average values of precision, recall, and F-measure.

Improving Image Segmentation by DFS Algorithm

Medical imagining has proven to be a significant field for examining human tissues non-intrusively. One of the subset of Imaging is the Image segmentation where in an image is split into significant regions which being later used for classification and performing analysis. This process is quiet complex as it involves accurately detecting and removing the affected part of the image containing abnormal tissues which are later being used for analysis. Image segmentation employs numerous techniques and approaches. Though there exist several methods and techniques for image segmentation but all of them can’t be implemented on medical images. The existing paper put forwards a complete survey and review concerning the medical image segmentation models, techniques, algorithms along with the challenges faced with the involvement of contrast filtering and large scale image processing perspectives. The technique of Discrete Feature Segmentation (DFS) is adopted for extracting the attributes related to a medical image. For improvising the contrast of an image, the popular method of Histogram equalization is utilized that basically enlarges the dynamic range of intensity. A method is recommended for defining the parameters of the Contrast-Limited Adaptive Histogram Equalization (CLAHE) by utilizing entropy of image. The CLAHE method that projects intensity levels concerning the medical images is backed up by evidence from detection trials and anecdotal evidence. For classifying the diseases in medical image, the prime emphasis is on the FCM (Fuzzy C-Means (FCM) algorithm. Present research paper compares various techniques of image enhancement considering their quality parameters (PSNR, Mean, MSE, Entropy, SN, Variance and RMS)

Marine Radar Oil-Spill Monitoring through Local Adaptive Thresholding

Marine oil spills affect the environment, economy, and quality of life for coastal inhabitants. This article presents a method of X-band marine radar oil-spill identification by considering the marine radar images of the 2010 Dalian 7-16 accident. The Prewitt operator was improved and a linear interpolation was proposed to suppress co-channel interferences. In addition, a model of a gray-intensity-correcting matrix is proposed to smooth a whole image, thus displaying the oil film more intuitively. Furthermore, a contrast-limited adaptive histogram equalization method was used to increase the contrast inside and outside the oil film. Moreover, the local adaptive thresholding method was improved to segment the oil spills. The results show that the proposed method is an improvement on similar previous approaches for this task when employing X-band marine radar images. The proposed method can provide technical and theoretical bases for emergency response, damage assessment, and liability identification of oil spills.

Dental radiography image enhancement for treatment evaluation through digital image processing.

BackgroundEvaluation of dental treatment is performed by observing dental periapical radiography to obtain information of filling’s condition, pulp tissue, remain dentin thickness, periodontal ligament, and lamina dura. Nevertheless, the radiographic image used often has low quality due to the level of x-ray radiation made low purposely in order to prevent health problem and limited tools capability. This low quality of the radiographic image, for examples, low image contrast, less brightness, and noise existence cause periapical radiography evaluation hard to be performed. This study aims to improve dental radiographic image quality for assisting pulp capping treatment evaluation.Material and MethodsThe research methodology consists of three main stages, i.e. data collection, image enhancement method production, and result validation. Radiographic image data collection in The Dental Hospital UMY. Image enhancement method has been conducted by comparing several methods: contourlet transform (CT), wavelet transform, contrast stretching (CS), and contrast limited adaptive histogram equalization (CLAHE) to reduce noise, to optimize image contrast, and to enhance image brightness.ResultsThe result of this study is according to mean square error (MSE) and peak signal to noise ratio (PSNR) statistics evaluation, it obtains that the highest scores of MSE and PSNR in row gained from CT method totaled 5.441453 and 40.53652, followed by CLAHE method with the scores are 10.66326 and 38.00736, CS method whose scores are 12.39881 and 39.18518, and the last is wavelet method with the scores are 15.41569 and 36.25343.ConclusionsNonetheless, MSE and PSNR scores are not enough merely to give a recommendation of any suitable methods for improving contrast, therefore, it needs another success parameter coming from the dentist. Key words:Dental radiography, image enhancement, digital image processing.

Evaluation of Image Enhancement Techniques of Dental X-Ray Images

Objectives: This study focuses on evaluation of the performance of various image enhancement techniques which were applied on dental X-ray images. Methods/Analysis: The images were calculated for comparing between input images and out images from three enhancement methods: imadjust, histogram equalization (HE) and contrast limited adaptive histogram equalization (CLAHE). A collection of eight dental x-ray images are chosen to apply these techniques on them. The x-ray images are -in general- low in contrast and dosage of radiation. This loss of image quality can be treated with enhancement process. Four quality image factors were used to evaluate the resulted images involved Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Maximum Error (MAXERR) and (L2rat). Findings: The quality of images had been raised and improved by these techniques for diagnosing goal. The results show that the enhancement methods chose to enhance the collected images led to more qualified images than original ones. Application/Improvements: The CLAHE method is better than other enhancement methods according to the values of quality image factors and the evaluation of expert dentists. Keywords: Contrast, Dental X-ray Images, Image Enhancement, Image Histogram, Image Quality

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.

Finger Vein Recognition based on Matching Score-Level Fusion of Gabor Features

Most methods for fusion-based finger vein recognition were to fuse different features or matching scores from more than one trait to improve performance. To overcome the shortcomings of "the curse of dimensionality" and additional running time in feature extraction, in this paper, we propose a finger vein recognition technology based on matching score-level fusion of a single trait. To enhance the quality of finger vein image, the contrast-limited adaptive histogram equalization (CLAHE) method is utilized and it improves the local contrast of normalized image after ROI detection. Gabor features are then extracted from eight channels based on a bank of Gabor filters. Instead of using the features for the recognition directly, we analyze the contributions of Gabor feature from each channel and apply a weighted matching score-level fusion rule to get the final matching score, which will be used for the last recognition. Experimental results demonstrate the CLAHE method is effective to enhance the finger vein image quality and the proposed matching score-level fusion shows better recognition performance.

Automatic Robust Neurite Detection and Morphological Analysis of Neuronal Cell Cultures in High-content Screening

Cell-based high content screening (HCS) is becoming an important and increasingly favored approach in therapeutic drug discovery and functional genomics. In HCS, changes in cellular morphology and biomarker distributions provide an information-rich profile of cellular responses to experimental treatments such as small molecules or gene knockdown probes. One obstacle that currently exists with such cell-based assays is the availability of image processing algorithms that are capable of reliably and automatically analyzing large HCS image sets. HCS images of primary neuronal cell cultures are particularly challenging to analyze due to complex cellular morphology. Here we present a robust method for quantifying and statistically analyzing the morphology of neuronal cells in HCS images. The major advantages of our method over existing software lie in its capability to correct non-uniform illumination using the contrast-limited adaptive histogram equalization method; segment neuromeres using Gabor-wavelet texture analysis; and detect faint neurites by a novel phase-based neurite extraction algorithm that is invariant to changes in illumination and contrast and can accurately localize neurites. Our method was successfully applied to analyze a large HCS image set generated in a morphology screen for polyglutamine-mediated neuronal toxicity using primary neuronal cell cultures derived from embryos of a Drosophila Huntington's Disease (HD) model.