Transform-based image enhancement algorithms with performance measure

Enhancing Diagnostic Images to Improve the Performance of the Segment Anything Model in Medical Image Segmentation

Medical imaging serves as a crucial tool in current cancer diagnosis. However, the quality of medical images is often compromised to minimize the potential risks associated with patient image acquisition. Computer-aided diagnosis systems have made significant advancements in recent years. These systems utilize computer algorithms to identify abnormal features in medical images, assisting radiologists in improving diagnostic accuracy and achieving consistency in image and disease interpretation. Importantly, the quality of medical images, as the target data, determines the achievable level of performance by artificial intelligence algorithms. However, the pixel value range of medical images differs from that of the digital images typically processed via artificial intelligence algorithms, and blindly incorporating such data for training can result in suboptimal algorithm performance. In this study, we propose a medical image-enhancement scheme that integrates generic digital image processing and medical image processing modules. This scheme aims to enhance medical image data by endowing them with high-contrast and smooth characteristics. We conducted experimental testing to demonstrate the effectiveness of this scheme in improving the performance of a medical image segmentation algorithm.

Underwater dynamic polarization imaging without dependence on the background region

Active-polarization imaging holds significant promise for achieving clear underwater vision. However, only static targets were considered in previous studies, and a background region was required for image restoration. To address these issues, this study proposes an underwater dynamic polarization imaging method based on image pyramid decomposition and reconstruction. During the decomposition process, the polarized image is downsampled to generate an image pyramid. Subsequently, the spatial distribution of the polarization characteristics of the backscattered light is reconstructed by upsampling, which recovered the clear scene. The proposed method avoids dependence on the background region and is suitable for moving targets with varying polarization properties. The experimental results demonstrate effective elimination of backscattered light while sufficiently preserving the target details. In particular, for dynamic targets, processing times that fulfill practical requirements and yield superior recovery effects are simultaneously obtained.

Skin Lesion Synthesis and Classification Using an Improved DCGAN Classifier

The prognosis for patients with skin cancer improves with regular screening and checkups. Unfortunately, many people with skin cancer do not receive a diagnosis until the disease has advanced beyond the point of effective therapy. Early detection is critical, and automated diagnostic technologies like dermoscopy, an imaging device that detects skin lesions early in the disease, are a driving factor. The lack of annotated data and class-imbalance datasets makes using automated diagnostic methods challenging for skin lesion classification. In recent years, deep learning models have performed well in medical diagnosis. Unfortunately, such models require a substantial amount of annotated data for training. Applying a data augmentation method based on generative adversarial networks (GANs) to classify skin lesions is a plausible solution by generating synthetic images to address the problem. This article proposes a skin lesion synthesis and classification model based on an Improved Deep Convolutional Generative Adversarial Network (DCGAN). The proposed system generates realistic images using several convolutional neural networks, making training easier. Scaling, normalization, sharpening, color transformation, and median filters enhance image details during training. The proposed model uses generator and discriminator networks, global average pooling with 2 × 2 fractional-stride, backpropagation with a constant learning rate of 0.01 instead of 0.0002, and the most effective hyperparameters for optimization to efficiently generate high-quality synthetic skin lesion images. As for the classification, the final layer of the Discriminator is labeled as a classifier for predicting the target class. This study deals with a binary classification predicting two classes-benign and malignant-in the ISIC2017 dataset: accuracy, recall, precision, and F1-score model classification performance. BAS measures classifier accuracy on imbalanced datasets. The DCGAN Classifier model demonstrated superior performance with a notable accuracy of 99.38% and 99% for recall, precision, F1 score, and BAS, outperforming the state-of-the-art deep learning models. These results show that the DCGAN Classifier can generate high-quality skin lesion images and accurately classify them, making it a promising tool for deep learning-based medical image analysis.

High-definition image transmission through dynamically perturbed multimode fiber by a self-attention based neural network

We implement faithful multimode fiber (MMF) image transmission by a self-attention-based neural network. Compared with a real-valued artificial neural network (ANN) based on a convolutional neural network (CNN), our method utilizes a self-attention mechanism to achieve a higher image quality. The enhancement measure (EME) and structural similarity (SSIM) of the dataset collected in the experiment improved by 0.79 and 0.04; the total number of parameters can be reduced by up to 25%. To enhance the robustness of the neural network to MMF bending in image transmission, we use a simulation dataset to prove that the hybrid training method is helpful in MMF transmission of a high-definition image. Our findings may pave the way for simpler and more robust single-MMF image transmission schemes with hybrid training; SSIM on datasets under different disturbances improve by 0.18. This system has the potential to be applied to various high-demand image transmission tasks, such as endoscopy.

A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography

Chest X-ray radiography (CXR) is among the most frequently used medical imaging modalities. It has a preeminent value in the detection of multiple life-threatening diseases. Radiologists can visually inspect CXR images for the presence of diseases. Most thoracic diseases have very similar patterns, which makes diagnosis prone to human error and leads to misdiagnosis. Computer-aided detection (CAD) of lung diseases in CXR images is among the popular topics in medical imaging research. Machine learning (ML) and deep learning (DL) provided techniques to make this task more efficient and faster. Numerous experiments in the diagnosis of various diseases proved the potential of these techniques. In comparison to previous reviews our study describes in detail several publicly available CXR datasets for different diseases. It presents an overview of recent deep learning models using CXR images to detect chest diseases such as VGG, ResNet, DenseNet, Inception, EfficientNet, RetinaNet, and ensemble learning methods that combine multiple models. It summarizes the techniques used for CXR image preprocessing (enhancement, segmentation, bone suppression, and data-augmentation) to improve image quality and address data imbalance issues, as well as the use of DL models to speed-up the diagnosis process. This review also discusses the challenges present in the published literature and highlights the importance of interpretability and explainability to better understand the DL models' detections. In addition, it outlines a direction for researchers to help develop more effective models for early and automatic detection of chest diseases.

A comparative study of medical image enhancement algorithms and quality assessment metrics on COVID-19 CT images

Medical imaging can help doctors in better diagnosis of several conditions. During the present COVID-19 pandemic, timely detection of novel coronavirus is crucial, which can help in curing the disease at an early stage. Image enhancement techniques can improve the visual appearance of COVID-19 CT scans and speed-up the process of diagnosis. In this study, we analyze some state-of-the-art image enhancement techniques for their suitability in enhancing the CT scans of COVID-19 patients. Six quantitative metrics, Entropy, SSIM, AMBE, PSNR, EME, and EMEE, are used to evaluate the enhanced images. Two experienced radiologists were involved in the study to evaluate the performance of the enhancement techniques and the quantitative metrics used to assess them.

Image Enhancement Based on Rough Set and Fractional Order Differentiator

In the paper, an image enhancement algorithm based on a rough set and fractional order differentiator is proposed. By combining the rough set theory with a Gaussian mixture model, a new image segmentation algorithm with higher immunity is obtained. This image segmentation algorithm can obtain more image layers with concentrating information and preserve more image details than traditional algorithms. After preprocessing, the segmentation layers will be enhanced by a new adaptive fractional order differential mask in the Fourier domain. Experimental results and numerical analysis have verified the effectiveness of the proposed algorithm.

Unsupervised learning polarimetric underwater image recovery under nonuniform optical fields

Turbid media will lead to a sharp decline in image quality. Polarization imaging is an effective method to obtain clear images in turbid media. In this paper, we propose an improved method that combines unsupervised learning and polarization imaging theory, which can be applied in a variety of nonuniform optical fields. We treat the background light as a spatially variable parameter, so we designed an end-to-end unsupervised generative network to inpaint the background light, which produces an adversarial loss with the discriminative network to improve the performance. And we use the angle of polarization to estimate the polarization parameters. The experimental results have demonstrated the effectiveness and generalization ability of our method. Compared with other works, our method shows a better real-time performance and has a lower cost in preparing the training dataset.

Deep Learning for Automated Detection and Identification of Migrating American Eel Anguilla rostrata from Imaging Sonar Data

Adult American eels (Anguilla rostrata) are vulnerable to hydropower turbine mortality during outmigration from growth habitat in inland waters to the ocean where they spawn. Imaging sonar is a reliable and proven technology for monitoring of fish passage and migration; however, there is no efficient automated method for eel detection. We designed a deep learning model for automated detection of adult American eels from sonar data. The method employs convolution neural network (CNN) to distinguish between 14 images of eels and non-eel objects. Prior to image classification with CNN, background subtraction and wavelet denoising were applied to enhance sonar images. The CNN model was first trained and tested on data obtained from a laboratory experiment, which yielded overall accuracies of >98% for image-based classification. Then, the model was trained and tested on field data that were obtained near the Iroquois Dam located on the St. Lawrence River; the accuracy achieved was commensurate with that of human experts.

Pipeline for Advanced Contrast Enhancement (PACE) of Chest X-ray in Evaluating COVID-19 Patients by Combining Bidimensional Empirical Mode Decomposition and Contrast Limited Adaptive Histogram Equalization (CLAHE)

COVID-19 is a new pulmonary disease which is driving stress to the hospitals due to the large number of cases worldwide. Imaging of lungs can play a key role in the monitoring of health status. Non-contrast chest computed tomography (CT) has been used for this purpose, mainly in China, with significant success. However, this approach cannot be massively used, mainly for both high risk and cost, also in some countries, this tool is not extensively available. Alternatively, chest X-ray, although less sensitive than CT-scan, can provide important information about the evolution of pulmonary involvement during the disease; this aspect is very important to verify the response of a patient to treatments. Here, we show how to improve the sensitivity of chest X-ray via a nonlinear post-processing tool, named PACE (Pipeline for Advanced Contrast Enhancement), combining properly Fast and Adaptive Bidimensional Empirical Mode Decomposition (FABEMD) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The results show an enhancement of the image contrast as confirmed by three widely used metrics: (i) contrast improvement index, (ii) entropy, and (iii) measure of enhancement. This improvement gives rise to a detectability of more lung lesions as identified by two radiologists, who evaluated the images separately, and confirmed by CT-scans. The results show this method is a flexible and an effective approach for medical image enhancement and can be used as a post-processing tool for medical image understanding and analysis.

Low-contrast X-ray enhancement using a fuzzy gamma reasoning model

X-ray images play an important role in providing physicians with satisfactory information correlated to fractures and diseases; unfortunately, most of these images suffer from low contrast and poor quality. Thus, enhancement of the image will increase the accuracy of correct information on pathologies for an autonomous diagnosis system. In this paper, a new approach for low-contrast X-ray image enhancement based on brightness adjustment using a fuzzy gamma reasoning model (FGRM) is proposed. To achieve this, three phases are considered: pre-processing, Fuzzy model for adaptive gamma correction (GC), and quality assessment based on blind reference. The proposed approach's accuracy is examined through two different blind reference approaches based on statistical measures (BR-SM) and dispersion-location (BR-DL) descriptors, supported by resulting images. Experimental results of the proposed FGRM approach on three databases (cervical, lumbar, and hand radiographs) yield favorable results in terms of contrast adjustment and providing satisfactory quality images. Graphical Abstract Graphical abstract of the proposed enhancement method.

Classification of Microcalcification Clusters in Digital Mammograms Using a Stack Generalization Based Classifier

This paper presents a machine learning based approach for the discrimination of malignant and benign microcalcification (MC) clusters in digital mammograms. A series of morphological operations was carried out to facilitate the feature extraction from segmented microcalcification. A combination of morphological, texture, and distribution features from individual MC components and MC clusters were extracted and a correlation-based feature selection technique was used. The clinical relevance of the selected features is discussed. The proposed method was evaluated using three different databases: Optimam Mammography Image Database (OMI-DB), Digital Database for Screening Mammography (DDSM), and Mammographic Image Analysis Society (MIAS) database. The best classification accuracy (95.00±0.57%) was achieved for OPTIMAM using a stack generalization classifier with 10-fold cross validation obtaining an Az value equal to 0.97±0.01.

A Fusion of Cuckoo Search and Multiscale Adaptive Smoothing Based Unsharp Masking for Image Enhancement

Image enhancement means to improve the visual appearance of an image by increasing its contrast and sharpening the features. This article presents a fusion of cuckoo search optimization-based image enhancement (CS-IE) and multiscale adaptive smoothing based unsharping method (MAS-UM) for image enhancement. The fusion strategy is introduced to improve the deficiency of enhanced image that suppresses the saturation and over-sharpness artefacts in order to obtain a visually pleasing result. The ideology behind the selection of fusion images (candidate) is that one image should have high sharpness or contrast with maximum entropy and other should be high Peak Signal-to-Noise Ratio (PSNR) sharp image, to provide a better trade-off between sharpness and noise. In this article, the CS-IE and MAS-UM results are fused to combine their complementary advantages. The proposed algorithms are applied to lathe tool images and some natural standard images to verify their effectiveness. The results are compared with conventional enhancement techniques such as Histogram equalization (HE), Linear contrast stretching (LCS), Contrast-limited adaptive histogram equalization (CLAHE), standard PSO image enhancement (PSO-IE), Differential evolution image enhancement (DE-IE) and Firefly algorithm-based image enhancement (FA-IE) techniques.

Spatiotemporal Image Fusion in Remote Sensing

In this paper, we discuss spatiotemporal data fusion methods in remote sensing. These methods fuse temporally sparse fine-resolution images with temporally dense coarse-resolution images. This review reveals that existing spatiotemporal data fusion methods are mainly dedicated to blending optical images. There is a limited number of studies focusing on fusing microwave data, or on fusing microwave and optical images in order to address the problem of gaps in the optical data caused by the presence of clouds. Therefore, future efforts are required to develop spatiotemporal data fusion methods flexible enough to accomplish different data fusion tasks under different environmental conditions and using different sensors data as input. The review shows that additional investigations are required to account for temporal changes occurring during the observation period when predicting spectral reflectance values at a fine scale in space and time. More sophisticated machine learning methods such as convolutional neural network (CNN) represent a promising solution for spatiotemporal fusion, especially due to their capability to fuse images with different spectral values.

Spatio-Temporal Super-Resolution Reconstruction of Remote-Sensing Images Based on Adaptive Multi-Scale Detail Enhancement

There are many problems in existing reconstruction-based super-resolution algorithms, such as the lack of texture-feature representation and of high-frequency details. Multi-scale detail enhancement can produce more texture information and high-frequency information. Therefore, super-resolution reconstruction of remote-sensing images based on adaptive multi-scale detail enhancement (AMDE-SR) is proposed in this paper. First, the information entropy of each remote-sensing image is calculated, and the image with the maximum entropy value is regarded as the reference image. Subsequently, spatio-temporal remote-sensing images are processed using phase normalization, which is to reduce the time phase difference of image data and enhance the complementarity of information. The multi-scale image information is then decomposed using the L₀ gradient minimization model, and the non-redundant information is processed by difference calculation and expanding non-redundant layers and the redundant layer by the iterative back-projection (IBP) technique. The different-scale non-redundant information is adaptive-weighted and fused using cross-entropy. Finally, a nonlinear texture-detail-enhancement function is built to improve the scope of small details, and the peak signal-to-noise ratio (PSNR) is used as an iterative constraint. Ultimately, high-resolution remote-sensing images with abundant texture information are obtained by iterative optimization. Real results show an average gain in entropy of up to 0.42 dB for an up-scaling of 2 and a significant promotion gain in enhancement measure evaluation for an up-scaling of 2. The experimental results show that the performance of the AMED-SR method is better than existing super-resolution reconstruction methods in terms of visual and accuracy improvements.

Infrared image enhancement algorithm based on adaptive histogram segmentation

Contrast enhancement plays a crucial role in infrared image pre-processing. Compared with the increasingly popular local-mapping enhancement methods, the global-mapping enhancement methods have a unique feature that reserves the thermal distribution information, which is vital in some temperature-sensitive applications. However, the main challenge of the global-mapping methods is how to enhance the contrast effectively without suffering from over-enhancement of the background and noise. To this end, we propose a novel global-mapping enhancement algorithm in this paper. First, the histogram is divided into several sub-histograms adaptively based on the heat conduction theory. By designing a metric called AHV, the background and non-background sub-histograms are distinguished, and then enhanced separately where more grayscales are allocated to non-background sub-histograms than background sub-histograms. Meanwhile, the property of the human visual system described by Weber's law is also taken into consideration during the grayscale redistribution. The qualitative and quantitative comparisons with state-of-the-art methods on several databases demonstrate the advantages of our proposed method.

HDR Pathological Image Enhancement Based on Improved Bias Field Correction and Guided Image Filter

Pathological image enhancement is a significant topic in the field of pathological image processing. This paper proposes a high dynamic range (HDR) pathological image enhancement method based on improved bias field correction and guided image filter (GIF). Firstly, a preprocessing including stain normalization and wavelet denoising is performed for Haematoxylin and Eosin (H and E) stained pathological image. Then, an improved bias field correction model is developed to enhance the influence of light for high-frequency part in image and correct the intensity inhomogeneity and detail discontinuity of image. Next, HDR pathological image is generated based on least square method using low dynamic range (LDR) image, H and E channel images. Finally, the fine enhanced image is acquired after the detail enhancement process. Experiments with 140 pathological images demonstrate the performance advantages of our proposed method as compared with related work.

Medical Image Enhancement Using Edge Information-Based Methods

Medical image enhancement improves the quality and facilitates diagnosis. This chapter investigates three methods of medical image enhancement by exploiting useful edge information. Since edges have higher perceptual importance, the edge information based enhancement process is always of interest. But determination of edge information is not an easy job. The edge information is obtained from various approaches such as differential hyperbolic function, Haar filters and morphological functions. The effectively determined edge information is used for enhancement process. The retinal image enhancement method given in this chapter improves the visual quality of the vessels in the optic region. X-ray image enhancement method presented here is to increase the visibility of the bones. These algorithms are used to enhance the computer tomography, chest x-ray, retinal, and mammogram images. These images are obtained from standard datasets and experimented. The performance of these enhancement methods are quantitatively evaluated.

Underwater image recovery considering polarization effects of objects

In underwater imaging scenarios, the scattering media could cause severe image degradation due to the backscatter veiling as well as signal attenuation. In this paper, we consider the polarization effect of the object, and propose a method of retrieving the objects radiance based on estimating the polarized-difference image of the target signal. We show with a real-world experiment that by taking into account the polarized-difference image of the target signal additionally, the quality of the underwater image can be effectively enhanced, which is particularly effective in the cases where both the object radiance and the backscatter contribute to the polarization, such as underwater detection of the artifact objects.

Micro-object motion tracking based on the probability hypothesis density particle tracker

Tracking micro-objects in the noisy microscopy image sequences is important for the analysis of dynamic processes in biological objects. In this paper, an automated tracking framework is proposed to extract the trajectories of micro-objects. This framework uses a probability hypothesis density particle filtering (PF-PHD) tracker to implement a recursive state estimation and trajectories association. In order to increase the efficiency of this approach, an elliptical target model is presented to describe the micro-objects using shape parameters instead of point-like targets which may cause inaccurate tracking. A novel likelihood function, not only covering the spatiotemporal distance but also dealing with geometric shape function based on the Mahalanobis norm, is proposed to improve the accuracy of particle weight in the update process of the PF-PHD tracker. Using this framework, a larger number of tracks are obtained. The experiments are performed on simulated data of microtubule movements and real mouse stem cells. We compare the PF-PHD tracker with the nearest neighbor method and the multiple hypothesis tracking method. Our PF-PHD tracker can simultaneously track hundreds of micro-objects in the microscopy image sequence.

Choosing the optimal spatial domain measure of enhancement for mammogram images

Medical imaging systems often require image enhancement, such as improving the image contrast, to provide medical professionals with the best visual image quality. This helps in anomaly detection and diagnosis. Most enhancement algorithms are iterative processes that require many parameters be selected. Poor or nonoptimal parameter selection can have a negative effect on the enhancement process. In this paper, a quantitative metric for measuring the image quality is used to select the optimal operating parameters for the enhancement algorithms. A variety of measures evaluating the quality of an image enhancement will be presented along with each measure's basis for analysis, namely, on image content and image attributes. We also provide guidelines for systematically choosing the proper measure of image quality for medical images.

Computer-Aided Prostate Cancer Diagnosis From Digitized Histopathology: A Review on Texture-Based Systems

Prostate cancer (PCa) is currently diagnosed by microscopic evaluation of biopsy samples. Since tissue assessment heavily relies on the pathologists level of expertise and interpretation criteria, it is still a subjective process with high intra- and interobserver variabilities. Computer-aided diagnosis (CAD) may have a major impact on detection and grading of PCa by reducing the pathologists reading time, and increasing the accuracy and reproducibility of diagnosis outcomes. However, the complexity of the prostatic tissue and the large volumes of data generated by biopsy procedures make the development of CAD systems for PCa a challenging task. The problem of automated diagnosis of prostatic carcinoma from histopathology has received a lot of attention. As a result, a number of CAD systems, have been proposed for quantitative image analysis and classification. This review aims at providing a detailed description of selected literature in the field of CAD of PCa, emphasizing the role of texture analysis methods in tissue description. It includes a review of image analysis tools for image preprocessing, feature extraction, classification, and validation techniques used in PCa detection and grading, as well as future directions in pursuit of better texture-based CAD systems.

Medical image visual appearance improvement using bihistogram Bezier curve contrast enhancement: data from the Osteoarthritis Initiative

Well-defined image can assist user to identify region of interest during segmentation. However, complex medical image is usually characterized by poor tissue contrast and low background luminance. The contrast improvement can lift image visual quality, but the fundamental contrast enhancement methods often overlook the sudden jump problem. In this work, the proposed bihistogram Bezier curve contrast enhancement introduces the concept of "adequate contrast enhancement" to overcome sudden jump problem in knee magnetic resonance image. Since every image produces its own intensity distribution, the adequate contrast enhancement checks on the image's maximum intensity distortion and uses intensity discrepancy reduction to generate Bezier transform curve. The proposed method improves tissue contrast and preserves pertinent knee features without compromising natural image appearance. Besides, statistical results from Fisher's Least Significant Difference test and the Duncan test have consistently indicated that the proposed method outperforms fundamental contrast enhancement methods to exalt image visual quality. As the study is limited to relatively small image database, future works will include a larger dataset with osteoarthritic images to assess the clinical effectiveness of the proposed method to facilitate the image inspection.

Non-linear direct multi-scale image enhancement based on the luminance and contrast masking characteristics of the human visual system

Image enhancement is a crucial pre-processing step for various image processing applications and vision systems. Many enhancement algorithms have been proposed based on different sets of criteria. However, a direct multi-scale image enhancement algorithm capable of independently and/or simultaneously providing adequate contrast enhancement, tonal rendition, dynamic range compression, and accurate edge preservation in a controlled manner has yet to be produced. In this paper, a multi-scale image enhancement algorithm based on a new parametric contrast measure is presented. The parametric contrast measure incorporates not only the luminance masking characteristic, but also the contrast masking characteristic of the human visual system. The formulation of the contrast measure can be adapted for any multi-resolution decomposition scheme in order to yield new human visual system-inspired multi-scale transforms. In this article, it is exemplified using the Laplacian pyramid, discrete wavelet transform, stationary wavelet transform, and dual-tree complex wavelet transform. Consequently, the proposed enhancement procedure is developed. The advantages of the proposed method include: 1) the integration of both the luminance and contrast masking phenomena; 2) the extension of non-linear mapping schemes to human visual system inspired multi-scale contrast coefficients; 3) the extension of human visual system-based image enhancement approaches to the stationary and dual-tree complex wavelet transforms, and a direct means of; 4) adjusting overall brightness; and 5) achieving dynamic range compression for image enhancement within a direct multi-scale enhancement framework. Experimental results demonstrate the ability of the proposed algorithm to achieve simultaneous local and global enhancements.

Naturalness preserved enhancement algorithm for non-uniform illumination images

Image enhancement plays an important role in image processing and analysis. Among various enhancement algorithms, Retinex-based algorithms can efficiently enhance details and have been widely adopted. Since Retinex-based algorithms regard illumination removal as a default preference and fail to limit the range of reflectance, the naturalness of non-uniform illumination images cannot be effectively preserved. However, naturalness is essential for image enhancement to achieve pleasing perceptual quality. In order to preserve naturalness while enhancing details, we propose an enhancement algorithm for non-uniform illumination images. In general, this paper makes the following three major contributions. First, a lightness-order-error measure is proposed to access naturalness preservation objectively. Second, a bright-pass filter is proposed to decompose an image into reflectance and illumination, which, respectively, determine the details and the naturalness of the image. Third, we propose a bi-log transformation, which is utilized to map the illumination to make a balance between details and naturalness. Experimental results demonstrate that the proposed algorithm can not only enhance the details but also preserve the naturalness for non-uniform illumination images.

Content-aware dark image enhancement through channel division

The current contrast enhancement algorithms occasionally result in artifacts, overenhancement, and unnatural effects in the processed images. These drawbacks increase for images taken under poor illumination conditions. In this paper, we propose a content-aware algorithm that enhances dark images, sharpens edges, reveals details in textured regions, and preserves the smoothness of flat regions. The algorithm produces an ad hoc transformation for each image, adapting the mapping functions to each image's characteristics to produce the maximum enhancement. We analyze the contrast of the image in the boundary and textured regions, and group the information with common characteristics. These groups model the relations within the image, from which we extract the transformation functions. The results are then adaptively mixed, by considering the human vision system characteristics, to boost the details in the image. Results show that the algorithm can automatically process a wide range of images-e.g., mixed shadow and bright areas, outdoor and indoor lighting, and face images-without introducing artifacts, which is an improvement over many existing methods.

Mineral image enhancement based on sequential combination of toggle and top-hat based contrast operator

Enhancing mineral image especially making mineral image details clear is very useful for mineral analysis. To effectively enhance mineral image, an algorithm based on the toggle contrast operator and top-hat based contrast operator is proposed in this paper. Sequentially combining the toggle contrast operator and top-hat based contrast operator could be used to identify image features especially the image details. So, appropriately exacting the identified image features by the sequentially combined toggle and top-hat based contrast operator is important for mineral image enhancement, which is analyzed firstly in this paper. After that, the multi-scale extension of feature extraction is given and used to construct the final features for mineral image enhancement. By importing the final extracted image features into the original mineral image through contrast enlargement, the original mineral image is well enhanced and the mineral image details are very clear. Experimental results on different types of mineral images verified the effective performance of the proposed algorithm.

Noise-suppressed image enhancement using multiscale top-hat selection transform through region extraction

Enhancing an image through increasing the contrast of the image is one effective way of image enhancement. To well enhance an image and suppress the produced noises in the resulting image, a multiscale top-hat selection transform-based algorithm through extracting bright and dark image regions and increasing the contrast between them is proposed. First, the multiscale top-hat selection transform is discussed and then is used to extract the bright and dark image regions of each scale. Second, the final extracted bright and dark image regions are obtained through a maximum operation on all the extracted multiscale bright and dark image regions at all scales. Finally, by using a weight strategy, the image is enhanced through increasing the contrast of the image by adding the final bright regions on and subtracting the final dark regions from the original image. The weight parameters are used to adjust the effect of image enhancement. Because the multiscale top-hat selection transform is used to effectively extract the final image regions and discriminate the possible noise regions, the image is well enhanced and some noises are suppressed. Experimental results on different types of images show that our algorithm performs well for noise-suppressed image enhancement and is useful for different applications.

Mathematical morphology-based approach to the enhancement of morphological features in medical images

Background

Medical image processing is essential in many fields of medical research and clinical practice because it greatly facilitates early and accurate detection and diagnosis of diseases. In particular, contrast enhancement is important for optimal image quality and visibility. This paper proposes a new image processing method for enhancing morphological features of masses and other abnormalities in medical images.

Method

The proposed method involves two steps: (1) selective extraction of target features by mathematical morphology and (2) enhancement of the extracted features by two contrast modification techniques.

Results

The goal of the proposed method is to enable enhancement of fine morphological features of a lesion region with high suppression of surrounding tissues. The effectiveness of the method was evaluated in quantitative terms of the contrast improvement ratio. The results clearly show that the method outperforms five conventional contrast enhancement methods. The effectiveness and usefulness of the proposed method were further demonstrated by application to three types of medical images: a mammographic image, a chest radiographic image, and a retinal image.

Conclusion

The proposed method enables specific extraction and enhancement of mass lesions, which is essential for clinical diagnosis based on medical image analysis. Thus, the method can be expected to achieve automatic recognition of lesion location and quantitative analysis of legion morphology.

Nonlinear unsharp masking for mammogram enhancement

This paper introduces a new unsharp masking (UM) scheme, called nonlinear UM (NLUM), for mammogram enhancement. The NLUM offers users the flexibility 1) to embed different types of filters into the nonlinear filtering operator; 2) to choose different linear or nonlinear operations for the fusion processes that combines the enhanced filtered portion of the mammogram with the original mammogram; and 3) to allow the NLUM parameter selection to be performed manually or by using a quantitative enhancement measure to obtain the optimal enhancement parameters. We also introduce a new enhancement measure approach, called the second-derivative-like measure of enhancement, which is shown to have better performance than other measures in evaluating the visual quality of image enhancement. The comparison and evaluation of enhancement performance demonstrate that the NLUM can improve the disease diagnosis by enhancing the fine details in mammograms with no a priori knowledge of the image contents. The human-visual-system-based image decomposition is used for analysis and visualization of mammogram enhancement.

A histogram modification framework and its application for image contrast enhancement

A general framework based on histogram equalization for image contrast enhancement is presented. In this framework, contrast enhancement is posed as an optimization problem that minimizes a cost function. Histogram equalization is an effective technique for contrast enhancement. However, a conventional histogram equalization (HE) usually results in excessive contrast enhancement, which in turn gives the processed image an unnatural look and creates visual artifacts. By introducing specifically designed penalty terms, the level of contrast enhancement can be adjusted; noise robustness, white/black stretching and mean-brightness preservation may easily be incorporated into the optimization. Analytic solutions for some of the important criteria are presented. Finally, a low-complexity algorithm for contrast enhancement is presented, and its performance is demonstrated against a recently proposed method.

Human visual system-based image enhancement and logarithmic contrast measure

Varying scene illumination poses many challenging problems for machine vision systems. One such issue is developing global enhancement methods that work effectively across the varying illumination. In this paper, we introduce two novel image enhancement algorithms: edge-preserving contrast enhancement, which is able to better preserve edge details while enhancing contrast in images with varying illumination, and a novel multihistogram equalization method which utilizes the human visual system (HVS) to segment the image, allowing a fast and efficient correction of nonuniform illumination. We then extend this HVS-based multihistogram equalization approach to create a general enhancement method that can utilize any combination of enhancement algorithms for an improved performance. Additionally, we propose new quantitative measures of image enhancement, called the logarithmic Michelson contrast measure (AME) and the logarithmic AME by entropy. Many image enhancement methods require selection of operating parameters, which are typically chosen using subjective methods, but these new measures allow for automated selection. We present experimental results for these methods and make a comparison against other leading algorithms.

Transform coefficient histogram-based image enhancement algorithms using contrast entropy

Many applications of histograms for the purposes of image processing are well known. However, applying this process to the transform domain by way of a transform coefficient histogram has not yet been fully explored. This paper proposes three methods of image enhancement: a) logarithmic transform histogram matching, b) logarithmic transform histogram shifting, and c) logarithmic transform histogram shaping using Gaussian distributions. They are based on the properties of the logarithmic transform domain histogram and histogram equalization. The presented algorithms use the fact that the relationship between stimulus and perception is logarithmic and afford a marriage between enhancement qualities and computational efficiency. A human visual system-based quantitative measurement of image contrast improvement is also defined. This helps choose the best parameters and transform for each enhancement. A number of experimental results are presented to illustrate the performance of the proposed algorithms.