Local Contrast Research Articles

Amino-modified-silica-coated gadolinium-copper nanoclusters, conjugated to AS1411 aptamer and radiolabeled with technetium-99 m as a novel multimodal imaging agent

Hybridimagingtechnology has the potential to provide reliable imagingand accurate detection of cancer cells by combining the advantages and overcoming the shortages of various clinical imaging tools. Nanomaterials with unique targeting properties and their small size have improved biomedical imaging. Indeed, their small size determines local contrast agent concentrations in tumors by enhanced permeability and retention (EPR) effect. In this work, amino-modified silica-coated Gadolinium-Copper Nanoclusters were fabricated and conjugated to AS1411 aptamer (Apt-ASGCuNCs) and radiolabeled with technetium-99 m (99mTc) for in vivo fluorescence imaging, magnetic resonance imaging (MRI) and single-photon emission computed tomography (SPECT). The synthesized nanoconjugate was fully characterized by transmission electron microscopy (TEM), element mapping, fluorescence spectroscopy, and Fourier-transform infrared spectroscopy. Moreover, XTT assay, and apoptosis and necrosis methods were applied to study toxicity. Radiochemical yield was calculated 93% that revealed a great potential for complex formation between Apt-ASGCuNCs and 99mTcO4−. Also, good stability of 99mTc-Apt-ASGCuNCs was found in the human serum up to 4 h. Both Apt-ASGCuNCs and 99mTc-Apt-ASGCuNCs indicated a considerable tumor-targeting in in vivo fluorescence imaging, MRI and SPECT with 4T1 tumor-bearing BALB/c mice. The biodistribution results showed no undesirable accumulation of 99mTc-Apt-ASGCuNCs in the liver, and spleen as it circulated freely in the blood pool. Meanwhile, 99mTc-Apt-ASGCuNCs were removed from the body through the renal clearance system, making it more convenient for future multimodality imaging applications.

Image Fusion with Sparse Representation: A Novel Local Contrast-Based Preprocessing Strategy

In the field of image fusion, great progress has been achieved in sparse representation (SR)-based methods with the advent of considerable efficacious algorithms being developed. The improvement of SR-based image fusion methods mainly focuses on pursuing better signal representation as well as effective measurement to sparse coefficients. However, the preprocessing strategy, which plays an important role in SR-based fusion methods, is rarely investigated. The traditional preprocessing strategies such as the zero-mean normalized strategy result in the loss of low-frequency information and insufficient detail extraction. To address the above issues, we propose a local contrast (LC)-based preprocessing strategy, since local contrast can be harnessed to capture geometrical visual patterns as well as preserve comprehensive information. Experimental results on multifocus and multimodal image fusion tasks demonstrate that the proposed preprocessing strategy outperforms the traditional ones in terms of both visual quality and objective evaluation.

Fast Superpixel-Based Non-Window CFAR Ship Detector for SAR Imagery

Ship detection in high-resolution synthetic aperture radar (SAR) images has attracted great attention. As a popular method, a constant false alarm rate (CFAR) detection algorithm is widely used. However, the detection performance of CFAR is easily affected by speckle noise. Moreover, the sliding window technique cannot effectively differentiate between clutter and target pixels and easily leads to a high computation load. In this paper, we propose a new superpixel-based non-window CFAR ship detection method for SAR images, which introduces superpixels to CFAR detection to resolve the aforementioned drawbacks. Firstly, our previously proposed fast density-based spatial clustering of applications with noise (DBSCAN) superpixel generation method is utilized to produce the superpixels for SAR images. With the assumption that SAR data obeys gamma distribution, the superpixel dissimilarity is defined. Then, superpixels can be accurately used to estimate the clutter parameters for the tested pixel, even in the multi-target situations, avoiding the drawbacks of the sliding window in the traditional CFAR. Moreover, a local superpixel contrast is proposed to optimize CFAR detection, which can eliminate numerous clutter false alarms, such as man-made urban areas and low bushes. Experimental results with real SAR images indicate that the proposed method can achieve ship detection with a higher speed and accuracy in comparison with other state-of-the-art methods.

Fabrication of a fractal pattern device for focus characterizations of X-ray imaging systems by Si deep reactive ion etching and bottom-up Au electroplating.

Precisely aligned optical components are crucial prerequisites for X-ray tomography at high resolution. We propose a device with a fractal pattern for precise automatic focusing. The device is etched in a Si substrate by deep reactive ion etching and then filled by a self-terminating bottom-up Au electroplating process. The fractal nature of the device produces an X-ray transmission image with globally homogeneous macroscopic visibility and high local contrast for pixel sizes in the range of 0.165 µm to 11 µm, while the high absorption contrast provided between Au and Si enables its use for X-ray energies ranging from 12 keV to 40 keV.

Infrared Small Target Detection Using Regional Feature Difference of Patch Image

Aiming at a thorny issue, that conventional small target detection algorithm using local contrast method is not sensitive for residual background clutter, robustness of algorithms is not strong. A Gaussian fusion algorithm using multi-scale regional patch structure difference and Regional Brightness Level Measurement is proposed. Firstly, Regional Energy Cosine (REC) is constructed to measure the structural discrepancy among a small target with neighboring cells. At the same time, Regional Brightness Level Measurement (RBLM) is constructed utilizing the brightness difference characteristics between small target and background areas. Then, a brand new Gaussian fusion algorithm is proposed for the generated saliency map in multi-scale space to characterize the overall heterogeneity in original infrared small target and local neighborhood. Finally, a self-adapting separation algorithm is adopted with the objective to obtain a small target from background interference. This method is able to utmostly restrain background interference and enhance the target. Extensive qualitative and quantitative testing results display that the desired algorithm has remarkable performance in strengthening target region and restraining background interference compared with current algorithms.

Research on infrared dim and small target detection algorithm based on frequency domain difference

ABSTRACT The high accuracy of infrared dim and small target detection in complex backgrounds is of great relevance for infrared identification and tracking systems. Traditional infrared dim and small target detection methods suit scenes with a single and homogeneous continuous background. However, human vision system methods suffer from an undetectable or high false alarm rate in complex scenes with dim small targets. To address this shortcoming, this paper proposes an infrared dim and small target detection algorithm based on frequency domain differencing (FDD). The proposed algorithm consists of a spectrum residual module and a Gaussian greyscale difference module. Firstly, the target enhancement image is constructed by using the spectrum residual module to highlight small targets and suppress background noise. Secondly, the local contrast of the image is enhanced by the Gaussian grayscale difference module, which accurately depicts the edge information of small targets and locates them. Finally, the target enhancement image and Gaussian grayscale difference image are fused to detect infrared dim and small targets. Experimental results show that the proposed algorithm has higher accuracy under the evaluation metrics of local signal-to-noise ratio gain (LSNRG), signal-to-clutter ratio gain (SCRG) and background suppression factor (BSF). At the same time, compared with other algorithms, the detection rate of the proposed algorithm is higher for infrared dim and small targets in complex scenes. Code is available at https://github.com/m156879/FDD-module.

Detail Enhancement Multi-Exposure Image Fusion Based on Homomorphic Filtering

Due to the large dynamic range of real scenes, it is difficult for images taken by ordinary devices to represent high-quality real scenes. To obtain high-quality images, the exposure fusion of multiple exposure images of the same scene is required. The fusion of multiple images results in the loss of edge detail in areas with large exposure differences. Aiming at this problem, this paper proposes a new method for the fusion of multi-exposure images with detail enhancement based on homomorphic filtering. First, a fusion weight map is constructed using exposure and local contrast. The exposure weight map is calculated by threshold segmentation and an adaptively adjustable Gaussian curve. The algorithm can assign appropriate exposure weights to well-exposed areas so that the fused image retains more details. Then, the weight map is denoised using fast-guided filtering. Finally, a fusion method for the detail enhancement of Laplacian pyramids with homomorphic filtering is proposed to enhance the edge information lost by Laplacian pyramid fusion. The experimental results show that the method can generate high-quality images with clear edges and details as well as similar color appearance to real scenes and can outperform existing algorithms in both subjective and objective evaluations.

Smoke removal and image enhancement of laparoscopic images by an artificial multi-exposure image fusion method

In laparoscopic surgery, image quality is often degraded by surgical smoke or by side effects of the illumination system, such as reflections, specularities, and non-uniform illumination. The degraded images complicate the work of the surgeons and may lead to errors in image-guided surgery. Existing enhancement algorithms mainly focus on enhancing global image contrast, overlooking local contrast. Here, we propose a new Patch Adaptive Structure Decomposition utilizing the Multi-Exposure Fusion technique to enhance the local contrast of laparoscopic images for better visualization. The set of under-exposure level images is obtained from a single input blurred image by using gamma correction. Spatial linear saturation is applied to enhance image contrast and to adjust the image saturation. The Multi-Exposure Fusion (MEF) is used on a series of multi-exposure images to obtain a single clear and smoke-free fused image. MEF is applied by using adaptive structure decomposition on all image patches. Image entropy based on the texture energy is used to calculate image energy strength. The texture entropy energy determined the patch size that is useful in the decomposition of image structure. The proposed method effectively eliminate smoke and enhance the degraded laparoscopic images. The qualitative results showed that the visual quality of the resultant images is improved and smoke-free. Furthermore, the quantitative scores computed of the metrics: FADE, Blur, JNBM, and Edge Intensity are significantly improved as compared to other existing methods.

Reducing the dynamic range of infrared images based on block-priority equalization and compression of histograms

Objectives. The problem of reducing the dynamic range of infrared images for their reproduction on display devices with a narrow dynamic range is considered. The method of local image histogram equalization based on the integral distribution function of brightness is investigated. To transform the brightness of a pixel, this method uses an approximation of the local alignment values of the nearest blocks of pixels of original image. This in-creases the local contrast of the image, but leads to high computational complexity, which is increasing while block size decreases. The aim of the work is to reduce the computational complexity of adaptive equalization and compression of infrared image histograms while reducing their dynamic range.Methods. Image processing methods are used.Results. To reduce the computational complexity of transforming the dynamic range of infrared images, a block-priority modification of the adaptive histogram equalization method is proposed. The modification is based on the division of the set of image blocks into two subsets of high-priority and low-priority blocks depend-ing on their brightness statistical properties. When interpolating pixel values, high-priority blocks use local alignment values, and low-priority blocks use global alignment values. As a result, the total number of alignment vectors is reduced in proportion to the ratio of subsets sizes and the computational complexity of the dynamic range transformation is reduced.Conclusion. When changing the ratio of the number of high-priority blocks of infrared image pixels to the number of all blocks in the range of 0.25–0.75, the proposed algorithm is more efficient than global and adaptive histogram equalization algorithms.

Automatically Extracted Machine Learning Features from Preoperative CT to Early Predict Microvascular Invasion in HCC: The Role of the Zone of Transition (ZOT).

Simple SummaryMicrovascular invasion (MVI) is a universally recognised predictor of hepatocellular carcinoma (HCC) recurrence after curative treatments, whose diagnosis, nowadays, is still postponed to surgery, through histopathological specimen investigation. This retrospective study aims to exploit a radiomic approach to pre-procedural diagnosis MVI in early-stage HCC, with a diameter ≤ 3 cm. The main novelty of the study is the use of the zone of transition (ZOT), crossing tumour and peritumour, detected adaptively through a standardized procedure based on the analysis of image gradients. After generating radiomics features from ZOT and tumour core in arterial and venous computed tomography phases, a classifier is trained and validated using a signature of four features only. The achieved radiomic model enables the early diagnosis of small HCC (≤3 cm) showing MVI with high specificity (82%) and sensitivity (79%), perspectively providing an effective tool to detect the best candidates for surgical treatment and liver transplantation (negative predictive value = 87%).Background: Microvascular invasion (MVI) is a consolidated predictor of hepatocellular carcinoma (HCC) recurrence after treatments. No reliable radiological imaging findings are available for preoperatively diagnosing MVI, despite some progresses of radiomic analysis. Furthermore, current MVI radiomic studies have not been designed for small HCC nodules, for which a plethora of treatments exists. This study aimed to identify radiomic MVI predictors in nodules ≤3.0 cm by analysing the zone of transition (ZOT), crossing tumour and peritumour, automatically detected to face the uncertainties of radiologist’s tumour segmentation. Methods: The study considered 117 patients imaged by contrast-enhanced computed tomography; 78 patients were finally enrolled in the radiomic analysis. Radiomic features were extracted from the tumour and the ZOT, detected using an adaptive procedure based on local image contrast variations. After data oversampling, a support vector machine classifier was developed and validated. Classifier performance was assessed using receiver operating characteristic (ROC) curve analysis and related metrics. Results: The original 89 HCC nodules (32 MVI+ and 57 MVI−) became 169 (62 MVI+ and 107 MVI−) after oversampling. Of the four features within the signature, three are ZOT heterogeneity measures regarding both arterial and venous phases. On the test set (19MVI+ and 33MVI−), the classifier predicts MVI+ with area under the curve of 0.86 (95%CI (0.70–0.93), p∼10), sensitivity = 79% and specificity = 82%. The classifier showed negative and positive predictive values of 87% and 71%, respectively. Conclusions: The classifier showed the highest diagnostic performance in the literature, disclosing the role of ZOT heterogeneity in predicting the MVI+ status.

Joint Segmentation of Multi-Class Hyper-Reflective Foci in Retinal Optical Coherence Tomography Images.

Hyper-reflective foci (HRF) refers to the spot-shaped, block-shaped areas with characteristics of high local contrast and high reflectivity, which is mostly observed in retinal optical coherence tomography (OCT) images of patients with fundus diseases. HRF mainly appears hard exudates (HE) and microglia (MG) clinically. Accurate segmentation of HE and MG is essential to alleviate the harm in retinal diseases. However, it is still a challenge to segment HE and MG simultaneously due to similar pathological features, various shapes and location distribution, blurred boundaries, and small morphology dimensions. To tackle these problems, in this paper, we propose a novel global information fusion and dual decoder collaboration-based network (GD-Net), which can segment HE and MG in OCT images jointly. Specifically, to suppress the interference of similar pathological features, a novel global information fusion (GIF) module is proposed, which can aggregate the global semantic information efficiently. To further improve the segmentation performance, we design a dual decoder collaborative workspace (DDCW) to comprehensively utilize the semantic correlation between HE and MG while enhancing the mutual influence on them by feedback alternately. To further optimize GD-Net, we explore a joint loss function which integrates pixel-level with image-level. The dataset of this study comes from patients diagnosed with diabetic macular edema at the department of ophthalmology, University Medical Center Groningen, The Netherlands. Experimental results show that our proposed method performs better than other state-of-the-art methods, which suggests the effectiveness of the proposed method and provides research ideas for medical applications.

Hardware implementation of Sobel edge detection system for blood cells images-based field programmable gate array

The microscopic-blood image has been used to diagnose various diseases according to the morphological specifications of red and white blood cells. <span lang="EN-US">However, the manual analysis and procedures are not accurate due to the human error. Therefore, several studies conducted to find new techniques to perform this analysis using computer algorithms. The complexity of these algorithms led to thinking in simpler ways or to the hardware solutions. On the other hand, edge detection is a mathematical procedure that play an essential role in the field of medical image processing. It is considered as one of the foundations' processes for other procedures, such as the segmentation and the classification of the image. The Sobel filter is one of the conventional methods that is used to perform the edge detection process. It is based on finding the local contrast for the level of intensity of the image. This paper presents a proposed and a new method for detecting the edges of cells in the microscopic blood images using Sobel filter and its hardware implementation on the field programmable gate array (FPGA) chip. Three different techniques are proposed: MATLAB, OpenCV standard code, and FPGA customize code which give the best visual results, minimum timing results than the others.</span>

A New Gradient-based Feature Extraction Method for Real-time Detection of Moving Objects Using Stereo Cameras

In this study, a gradient-based feature extraction method has been developed that can be used to detect moving objects in real-time applications such as unmanned ground or air vehicles. Feature extraction methods should produce fast results in real-time applications, as results need to be obtained between successive frames of video sequences within a limited time. For this reason, various sized image blocks were used in the developed method. The arithmetic mean (AM), geometric mean (GM), median (MD), and local contrast (LC) methods were used to calculate block intensities. In the stereo video stream, depth maps were also divided into blocks along with successive frames' R, G, and B channels. A novel feature extraction method was developed by calculating gradient-based relationships between adjacent blocks around the centre block. In experimental studies, the features extracted from stereo video frames using the proposed method were compared with Surf, Fast and Brisk methods according to their quantity, accuracy, and processing times, and more successful results were obtained. In addition, the moving object detection performance of the method was tested in real-time using an Unmanned Ground Vehicle.

Underwater image enhancement via integrated RGB and LAB color models

Images taken underwater suffers from color shift and poor visibility because the light is absorbed and scattered when it travels through water. To handle the issues mentioned above, we propose an underwater image enhancement method via integrated RGB and LAB color models (RLCM). In the RGB color model, we first fully consider the leading causes of underwater image color shift, and then the poor color channels are corrected by dedicated fractions, which are designed via calculating the differences between the well and poor color channels. In the LAB color model, wherein the local contrast of the L channel is enhanced by a histogram with local enhancement and exposure cut-off strategy, whereas the difference between the A and B channels is traded-off by a gain equalization strategy. Besides, a normalized guided filtering strategy is incorporated into the histogram enhancement process to mitigate the effects of noise. Ultimately, the image is inverted from the LAB color model to the RGB color model, and a detail sharpening strategy is implemented in each channel to obtain a high-quality underwater image. Experiments on various real-world underwater images demonstrate that our method outputs better results with natural color and high visibility.

Double layer local contrast measure and multi-directional gradient comparison for small infrared target detection

Infrared small target detection is one of the key technologies in the search and track (IRST) based on infrared imaging equipment. At present, the performance of small target detection based on single frame infrared image is directly related to the accuracy of subsequent target tracking, so it has been studied a lot. However, the existing small target detection algorithms have certain limitations in detection accuracy and real-time performance, especially when the contrast between the target and the background area is not high or the background is complex, especially in the complex sea or sky background, due to the influence of a large amount of noise and clutter in the background, the existing infrared small target detection algorithms have a high false alarm rate. To solve the above problems, this paper proposes a small target detection algorithm based on weighted double layer local contrast and multi-directional gradient map, which realizes the accurate detection of small targets from two aspects of targets’ local contrast and gradient. Firstly, we design an improved two layer local contrast measurement architecture, and use the weighted mean method to better represent the gray value of the local window; Secondly, a local contrast comparison method based on target and background is proposed to enhance the intensity of small targets and suppress some background clutter; Then, the multi-directional gradient map is used to further suppress the noise so as to improve the contrast between the target and the background. At the same time, singular value decomposition (SVD) method is used to extract the main features including small targets, which can effectively suppress the small texture interference around the targets in the background without losing the target intensity; Finally, an adaptive threshold method is used to separate small targets from their background. Experimental results show that compared with the existing algorithms, the proposed detection algorithm can effectively reduce the false alarm rate in different complex scenes, and the computational efficiency is improved compared with some multi-scale small target detection methods. At the same time, the signal to clutter ratio (SCR), background suppression factor (BSF) and receiver operating characteristic (ROC) curve are also better than these existing state of the art algorithms, which can display good robustness.

Numerosity tuning in human association cortices and local image contrast representations in early visual cortex

Human early visual cortex response amplitudes monotonically increase with numerosity (object number), regardless of object size and spacing. However, numerosity is typically considered a high-level visual or cognitive feature, while early visual responses follow image contrast in the spatial frequency domain. We find that, at fixed contrast, aggregate Fourier power (at all orientations and spatial frequencies) follows numerosity closely but nonlinearly with little effect of object size, spacing or shape. This would allow straightforward numerosity estimation from spatial frequency domain image representations. Using 7T fMRI, we show monotonic responses originate in primary visual cortex (V1) at the stimulus’s retinotopic location. Responses here and in neural network models follow aggregate Fourier power more closely than numerosity. Truly numerosity tuned responses emerge after lateral occipital cortex and are independent of retinotopic location. We propose numerosity’s straightforward perception and neural responses may result from the pervasive spatial frequency analyses of early visual processing.

Reversible Data Hiding with a New Local Contrast Enhancement Approach

Reversible data hiding schemes hide information into a digital image and simultaneously increase its contrast. The improvements of the different approaches aim to increase the capacity, contrast, and quality of the image. However, recent proposals contrast the image globally and lose local details since they use two common methodologies that may not contribute to obtaining better results. Firstly, to generate vacancies for hiding information, most schemes start with a preprocessing applied to the histogram that may introduce visual distortions and set the maximum hiding rate in advance. Secondly, just a few hiding ranges are selected in the histogram, which means that just limited contrast and capacity may be achieved. To solve these problems, in this paper, a novel approach without preprocessing performs an automatic selection of multiple hiding ranges into the histograms. The selection stage is based on an optimization process, and the iterative-based algorithm increases capacity at embedding execution. Results show that quality and capacity values overcome previous approaches. Additionally, visual results show how greyscale values are better differentiated in the image, revealing details globally and locally.

Infrared small target detection based on multiscale local contrast learning networks

Recently, model-driven deep networks have achieved excellent detection performance on infrared small targets in cluttered environments. However, its detection performance is sensitive to the hyperparameters in the embedded model-driven module. Therefore, we propose a novel multiscale local contrast learning network (MLCL-Net), which is an end-to-end fully convolutional infrared small target detection network. By constructing a local contrast learning (LCL) structure, it can learn to generate local contrast feature maps during training. Considering the difference in target size, we further build a multiscale local contrast learning (MLCL) module based on LCL. By extracting and fusing local contrast information of different scales from feature maps of the same level, the feature information of targets is fully excavated. At the same time, due to the small size of the target, a slight pixel shift will cause a severe loss of accuracy. We propose a bilinear feature pyramid network (BFPN) based on the feature pyramid network (FPN). Compared to state-of-the-art methods, the proposed MLCL-Net achieves superior performance with an intersection-over-union (IoU) of 0.772 and normalized IoU (nIoU) of 0.755 on the public SIRST dataset.

Glossiness Index of Objects in Halftone Color Images Based on Structure and Appearance Distortion.

This paper proposes an objective glossiness index for objects in halftone color images. In the proposed index, we consider the characteristics of the human visual system (HVS) and associate the image’s structure distortion and statistical information. According to the difference in the number of strategies adopted by the HVS in judging the difference between images, it is divided into single and multi-strategy modeling. In this study, we advocate multiple strategies to determine glossy or non-glossy quality. We assumed that HVS used different visual mechanisms to evaluate glossy and non-glossy objects. For non-glossy images, the image structure dominated, so the HVS tried to use structural information to judge distortion (a strategy based on structural distortion detection). For glossy images, the glossy appearance dominated; thus, the HVS tried to search for the glossiness difference (an appearance-based strategy). Herein, we present an index for glossiness assessment that attempts to explicitly model the structural dissimilarity and appearance distortion. We used the contrast sensitivity function to account for the mechanism of halftone images when viewed by the human eye. We estimated the structure distortion for the first strategy by using local luminance and contrast masking; meanwhile, local statistics changing in the spatial frequency components for skewness and standard deviation were used to estimate the appearance distortion for the second strategy. Experimental results showed that these two mixed-distortion measurement strategies performed well in consistency with the subjective ratings of glossiness in halftone color images.

The Role of Specular Reflections and Illumination in the Perception of Thickness in Solid Transparent Objects.

Specular reflections and refractive distortions are complex image properties of solid transparent objects, but despite this complexity, we readily perceive the 3D shapes of these objects (e.g., glass and clear plastic). We have found in past work that relevant sources of scene complexity have differential effects on 3D shape perception, with specular reflections increasing perceived thickness, and refractive distortions decreasing perceived thickness. In an object with both elements, such as glass, the two optical properties may complement each other to support reliable perception of 3D shape. We investigated the relative dominance of specular reflection and refractive distortions in the perception of shape. Surprisingly, the ratio of specular reflection to refractive component was almost equal to that of ordinary glass and ice, which promote correct percepts of 3D shape. The results were also explained by the variance in local RMS contrast in stimulus images but may depend on overall luminance and contrast of the surrounding light field.