Improve Image Contrast Research Articles

Adaptive Entropy Index Histogram Equalization for Poor Contrast Images

Hidden details and lack of image contrast can be attributed to limited user experience, poor device quality, environment settings during image acquisition, and illumination. To address these problems, techniques based on histogram equalization (HE) have been frequently used to reduce these problems and to improve image contrast. However, the resultant images obtained by techniques often appear unnatural possibly due to washed-out effects and unwanted artifacts. This study proposes a new technique called adaptive entropy index histogram equalization (AEIHE) that belongs to the local sub-class of HE-based contrast enhancement techniques. AEIHE initially divides the image into three sub-images to enhance and highlight its local details. Each of these sub-images uses a different contextual region and clip limit based on the richness of their information and their structure, both of which are adaptively determined by AEIHE. A new parameter called Entropy-Index is then used to ensure the high information richness of the resultant sub-image while preserving its structure. AEIHE guarantees the production of an excellent resultant image by combining enhanced sub-images. Quantitative evaluations of 819 images show that AEIHE has successfully produced excellent resultant images with improved contrast, highlighted local details, and minimized effects of artifacts and unwanted noise. Therefore, AEIHE has a high application potential in the medical imaging, machine vision, and industrial domains.

In Vivo Pretargeting Based on Cysteine-Selective Antibody Modification with IEDDA Bioorthogonal Handles for Click Chemistry.

Pretargeted imaging has emerged as an effective multistep strategy aiming to improve imaging contrast and reduce patient radiation exposure through decoupling of the radioactivity from the targeting vector. The inverse electron-demand Diels-Alder (IEDDA) reaction between a trans-cyclooctene (TCO)-conjugated antibody and a labeled tetrazine holds great promise for pretargeted imaging applications due to its bioorthogonality, rapid kinetics under mild conditions, and formation of stable products. Herein, we describe the use of functionalized carbonylacrylic reagents for site-specific incorporation of TCO onto a human epidermal growth factor receptor 2 (HER2) antibody (THIOMAB) containing an engineered unpaired cysteine residue, generating homogeneous conjugates. Precise labeling of THIOMAB-TCO with a fluorescent or radiolabeled tetrazine revealed the potential of the TCO-functionalized antibody for imaging the HER2 after pretargeting in a cellular context in a HER2 positive breast cancer cell line. Control studies with MDA-MD-231 cells, which do not express HER2, further confirmed the target specificity of the modified antibody. THIOMAB-TCO was also evaluated in vivo after pretargeting and subsequent administration of an 111In-labeled tetrazine. Biodistribution studies in breast cancer tumor-bearing mice showed a significant activity accumulation on HER2+ tumors, which was 2.6-fold higher than in HER2- tumors. Additionally, biodistribution studies with THIOMAB without the TCO handle also resulted in a decreased uptake of 111In-DOTA-Tz on HER2+ tumors. Altogether, these results clearly indicate the occurrence of the click reaction at the tumor site, i.e., pretargeting of SK-BR-3 HER2-expressing cells with THIOMAB-TCO and reaction through the TCO moiety present in the antibody. The combined advantages of site-selectivity and stability of TCO tagged-antibodies could allow application of biorthogonal chemistry strategies for pretargeting imaging with minimal side-reactions and background.

Detector shift iteration method for improving spatial resolution and suppressing pixel value distortion in direct and indirect X-ray detectors

As the pixel pitch of an X-ray detector decreases, the crosstalk and charge-sharing effects become dominant. The decrease in pixel pitch is also directly related to a reduction in input photons per pixel. Thus, the noise of individual pixel increases in both direct and indirect detectors. In photon counting detector, photon energy shift due to charge sharing and blurring in indirect charge integration detector increases greatly. In this study, we propose the detector shift iteration method (DSIM), which improves both image quality and spatial resolution by solving the abovementioned problems. The Modulation Transfer Function (MTF) and Signal-to-Noise Ratio (SNR) are measured to analyze the image quality and spatial resolution, and the improvement in image contrast is analyzed through various object images. The analysis showed sufficient improvement in both the quality and spatial resolution of the DSIM images. In particular, the DSIM images had better quality than those obtained by sensors with the same pixel pitch as virtual pixel pitch.

Reduction-active Fe3O4-loaded micelles with aggregation- enhanced MRI contrast for differential diagnosis of Neroglioma

Differential diagnosis between inflammatory mass and malignant glioma is of great significance to patients, which is the basis for developing accurate individualized treatment. Due to the lack of non-invasive imaging characterization methods in the clinical application, the current diagnosis grading of glioma mainly depended on the pathological biopsy, which is complicated and risky. This study aims to develop a non-invasive imaging differential diagnosis method of glioma based on the reduction activated strategy of intracellular aggregation of sensitive superparamagnetic Fe3O4 nanoparticles (SIONPs). In vitro and in vivo magnetic resonance imaging results indicated that SIONPs could specifically increase the T2 relaxation rate and enhance MR imaging in tumor with redox microenvironment by the response-aggregation in the tumorous site. In vivo experiments also demonstrate that the substantial improvement of T2-weighted imaging contrast could be used to differentiate inflammatory mass and malignant glioma. The reduction-active MR imaging contrast agent offers a new paradigm for designing “smart” MR imaging probes of differential diagnosis of the tumor.

Electron image contrast analysis of mosaicity in rutile nanocrystals using direct electron detection.

Direct electron detection provides high detective quantum efficiency, significantly improved point spread function and fast read-out which have revolutionized the field of cryogenic electron microscopy. However, these benefits for high-resolution electron microscopy (HREM) are much less exploited, especially for in situ study where major impacts on crystallographic structural studies could be made. By using direct detection in electron counting mode, rutile nanocrystals have been imaged at high temperature inside an environmental transmission electron microscope. The improvements in image contrast are quantified by comparison with a charge-coupled device (CCD) camera and by image matching with simulations using an automated approach based on template matching. Together, these approaches enable a direct measurement of 3D shape and mosaicity (∼1°) of a vacuum-reduced TiO2 nanocrystal about 50 nm in size. Thus, this work demonstrates the possibility of quantitative HREM image analysis based on direct electron detection.

The Performance of Three Total Variation Based Algorithms for Enhancing the Contrast of Industrial Radiography Images

ABSTRACT Industrial radiography is considered as one of the most important nondestructive testing methods for different inspections. The radiography images often have a poor signal-to-noise ratio mainly because of the scattered X-rays. Image processing methods may be used to enhance the contrast of radiographs for better defect detection. In this study, outcomes from three total variations (TV) based methods were analyzed and compared. Implemented algorithms were ROF-TV, non-convex p-norm total variation (NCP-TV) and non-convex logarithm-based total variation (NCLog-TV). These TV-based methods have been implemented indirectly as high pass edge-enhancing filters. Based on qualitative operator perception results, the study has shown that the application of all three methods resulted in improved image contrast enabling enhanced image detail visualization. Subtle performance differences between the outputs from different algorithms were noted, however, especially around the edges of image features. Furthermore, it was found that all implemented algorithms have similarities in performance, generate approximately the same results and are suitable for weld inspection.

Contrast enhancement based on discriminative co-occurrence statistics

Despite recent advances in contrast enhancement, it remains difficult for existing methods to simultaneously achieve consistent improvements in image brightness and contrast in both low-light and normal-light images. To address this issue, we revisit 2D histogram equalization methods and extend them to accommodate more diversified and poor lighting conditions. The extension is based on the observation that the contrast needs to be improved by increasing the intensity difference between spatially neighboring pixels, while the degree of increase should be proportional to the difference in their reflectances. On the basis of this observation, we propose to embed the inter-pixel contextual information of image reflectance into the 2D histogram of intensity co-occurrence. An intensity mapping function can thus be derived by solving optimization problems formulated with the 2D histogram, leading to two novel contrast enhancement methods. Qualitative and quantitative evaluations on more than 600 images showed that the proposed methods are superior to state-of-the-art contrast enhancement methods. It was also shown that the reflectance of an image provides important visual information on the significance and objectness of local image areas. Using this reflectance as a clue, the degree of contrast enhancement can be adaptively derived to achieve sufficient brightness improvement in low-light images and to avoid excessive enhancement in normal-light images.

17‐2: A method of improving image contrast based on Dual Cell display

Based on the Dual cell structure display, this paper proposes an effective contrast enhancement scheme. By using the lower cell to control the backlight brightness, local dimming is realized at the pixel level. Combined with spatial filtering, the ghosting problem of the Dual cell structure display is solved.

Selecting optimal spectral bands for improved detection of autofluorescent biomarkers in multiphoton microscopy.

.Significance: In multiphoton microscopy, two-photon excited fluorescence (TPEF) spectra carry valuable information on morphological and functional biological features. For measuring these biomarkers, separation of different parts of the fluorescence spectrum into channels is typically achieved by the use of optical band pass filters. However, spectra from different biomarkers can be unknown or overlapping, creating a crosstalk in between the channels. Previously, establishing these channels relied on prior knowledge or heuristic testing.Aim: The presented method aims to provide spectral bands with optimal separation between groups of specimens expressing different biomarkers.Approach: We have developed a system capable of resolving TPEF with high spectral resolution for the characterization of biomarkers. In addition, an algorithm is created to simulate and optimize optical band pass filters for fluorescence detection channels. To demonstrate the potential improvements in cell and tissue classification using these optimized channels, we recorded spectrally resolved images of cancerous (HT29) and normal epithelial colon cells (FHC), cultivated in 2D layers and in 3D to form spheroids. To provide an example of an application, we relate the results with the widely used redox ratio.Results: We show that in the case of two detection channels, our system and algorithm enable the selection of optimized band pass filters without the need of knowing involved fluorophores. An improvement of 31,5% in separating different 2D cell cultures is achieved, compared to using established spectral bands that assume NAD(P)H and FAD as main contributors of autofluorescence. The compromise is a reduced SNR in the images.Conclusions: We show that the presented method has the ability to improve imaging contrast and can be used to tailor a given label-free optical imaging system using optical band pass filters targeting a specific biomarker or application.

Colonoscopy contrast-enhanced by intuitionistic fuzzy soft sets for polyp cancer localization

Medical images often suffer from low contrast, irregular gray-level spacing and contain a lot of uncertainties due to constraints of imaging devices and environment (various lighting conditions) when capturing images. In order to achieve any clinical-diagnosis method for medical imaging with better comprehensibility, image contrast enhancement algorithms would be appropriate to improve the visual quality of medical images. In this paper, an automated image enhancement method is presented for colonoscopy images based on the intuitionistic fuzzy soft set. The fuzzy soft set is used to model the intuitionistic fuzzy soft image matrix based on a set of soft features of the colonoscopy images. The technique decomposes the fuzzy image into multiple blocks and estimates a soft-score based on an adaptive soft parametric hesitancy map by using the hesitant entropy for each block to quantify the uncertainties. In the processing stage, an adaptive intensity modification process is done for each block according to its soft-score. These scores are accurately addressed the gray-level ambiguities in colonoscopy images that lead to better results. Finally, the enhanced image achieved by performing a defuzzification together with all unprocessed blocks. Qualitative and quantitative assessments demonstrate that the proposed method improves image contrast and region-of-interest of polyps in colonogram. Experimental results on enhancing a large CVC-Clinic-DB and ASU-Mayo clinic colonoscopy benchmark datasets show that the proposed method outperforms the state-of-the-art medical image enhancement methods.

Comparison of the performance of three domain transform filters for radiographic contrast enhancement of welded objects

Weld quality inspection using industrial radiography is considered to be one of the most important processes in critical industries such as aeronautical manufacturing. The quality of radiographic images of welded industrial parts may suffer from poor signal-to-noise ratio (SNR), the main cause of which is the unavoidable detection of scattered X-rays. Image processing methods may be used to enhance image contrast and achieve improved defect detection. In this study, the outcomes from three different image contrast enhancement spatial domain transform algorithms are analysed and compared. The three algorithms used are normalised convolution (NC), interpolated convolution (IC) and recursive filtering (RF). Based on the results of qualitative operator perception, the study shows that the application of all three methods results in improved image contrast, enabling enhanced visualisation of image detail. Subtle differences in performance between the outputs from the different algorithms are noted, especially around the edges of image features. Furthermore, it is found that RF is approximately two orders of magnitude quicker than the other algorithms, making it more suitable for online weld inspection lines.

Combination system in advanced image processing for improving image contrast and a conventional row-ratio grid for an indirect flat-panel detector system: An experimental study

Conventional grids with high grid ratios are not ideal for use in bedside radiography because of the difficulty in maintaining the required alignment. To address this issue, the potential usefulness of a combination system that employs removal processing software for scattered radiation and a conventional grid with a low grid ratio (3:1) for an indirect-conversion-type flat-panel detector system was evaluated by measuring image quality and observer performance. The hypothetical grid ratios for the software were 2:1, 3:1, 6:1, 8:1, and 10:1. The scatter fraction of the combination system was lower than that of the software alone. Significant improvement was observed in the effect of scattered radiation removal up to a hypothetical software grid ratio of 6:1. However, the Wiener spectrum increased (radiographic noise degraded) with an increase in the hypothetical grid ratio. The contrast ratios of the combination system were improved compared to those of the software alone for anthropomorphic chest radiographs. An observer test was also conducted using the contrast-detail phantom. The combination system indicated higher low-contrast detectability compared to the software alone, although there were no statistical differences between the hypothetical grid ratios of 6:1, 8:1, and 10:1 in all combinations of the software alone and the combination system. We concluded that a combination system with software that uses a hypothetical grid ratio of 6:1 or more and a 3:1 conventional grid would be more useful for reducing the scattered radiation component compared to the software alone with a hypothetical higher grid ratio for thicker objects.

Parameter-free fuzzy histogram equalisation with illumination preserving characteristics dedicated for contrast enhancement of magnetic resonance images

Low-field MRI scanners do not offer sufficient image contrast. Hence, offline algorithms for improving image contrast are often needed. Even though modified versions of Histogram Equalisation (HE) are extensively used on panoramic images, they have serious limitations. Most of such modified algorithms have multiple operational parameters which need to be tuned manually. Parameter-free modifications lag in terms of illumination-preserving features. To address these issues, a novel formulation of Parameter-free Fuzzy Histogram Equalisation (PFHE) algorithm with good illumination-preserving characteristics, dedicated for contrast enhancement of MRI is introduced in this paper. In PFHE, a Homogeneity Fuzzy Sub-set (HFS) and its fuzzy complement, termed as Texture Fuzzy Sub-set (TFS) are computed based on the fuzzy similarity of the pixels in the input image with their eight-connected neighbours. Following this, an approximate output is estimated by applying a transformation similar to the histogram equalisation on the Fuzzy Textural Histogram (FTH) derived from TFS. The final output is computed as a nonlinear combination of the approximate output and the input image. The fuzzy weighting vectors used in the nonlinear combination are derived from the HFS. Both Qualitative and quantitative evaluations reveal that the PFHE is superior to Bi-Histogram Equalisation (BHE), Weighted Threshold Histogram Equalisation (WTHE), Contrast Limited Adaptive Histogram Equalisation (CLAHE), Non-parametric Modified Histogram Equalisation (NMHE), Exposure-based Sub-Image Histogram Equalisation (ESIHE), Median–Mean Based Sub-Image-Clipped Histogram Equalisation (MMSICHE) and Dominant Orientation-based Texture Histogram Equalisation (DOTHE), in terms of ability to preserve diagnostically significant features in the MR image.

Effectiveness of MB-102, a novel fluorescent tracer agent, for conducting ocular angiography in dogs.

To evaluate the effectiveness of a novel fluorescence tracer agent, MB-102, for conducting ocular angiography in dogs. 10 ophthalmologically normal dogs (2 to 4 years old) and 10 dogs with retinal degeneration or primary open-angle glaucoma (< 6 years old). While anesthetized, all dogs received sodium fluorescein (20 mg/kg, IV) or MB-102 (20 or 40 mg/kg, IV) first and then the other dye in a second treatment session 2 days later in a randomized crossover design. Anterior fluorescence angiography was performed on one eye and posterior fluorescence angiography on the other. Imaging was performed with a full-spectrum camera and camera adaptor system. Filter sets that were tailored to match the excitation and emission characteristics of each angiographic fluorescent agent were used. All phases and phase intervals during anterior and posterior segment angiography were identified, regardless of the dye used. However, agent fluorescence and visualization of the iridal blood vessels were hindered in some dogs, irrespective of agent, owing to the degree of iridal pigmentation present. No significant difference was noted between the 2 dyes in any phase or phase interval, and slight improvement in image contrast was observed with MB-102 during the venous phases owing to a reduction of vessel wall staining in both normal and diseased eyes. Results suggested that MB-102 would be useful for conducting ocular angiography in dogs.

Image edge detection algorithm based on fuzzy set

Edge detection is image processing, analysis and one of the most important areas of research in the field of computer vision; it is the basic tools of pattern recognition and image information extraction. Actual image processing is generally mixed with noise. How to eliminate the false edge caused by noise interference and ensure the accuracy of edge positioning, it becomes an important problem to be solved in edge detection and is also the purpose of this paper. Firstly, a histogram matching image enhancement algorithm based on maximum fuzzy entropy dynamic improvement is proposed. The algorithm first maps gray scale images from the spatial domain to the fuzzy domain, the target image is divided into several gray layers based on maximum fuzzy entropy. And then, for the characteristics of different gray levels, histogram matching method is used to design corresponding matching function for each gray layer. These matching functions are used to enhance the corresponding gray layer to obtain the enhanced image. Image enhancement method combines fuzzy entropy and histogram matching algorithm, it can effectively suppress noise and improve image contrast ratio. Secondly, an image edge detection algorithm based on improved fuzzy theory is proposed. This algorithm uses the improved fuzzy enhancement algorithm to enhance the original image. The non-maximum suppression algorithm is used to process the enhanced image; the optimal threshold value is obtained by fuzzy extraction and maximum inter-group variance method. This algorithm is used for edge detection of image. Experiments show that the algorithm is feasible and effective, and has some advantages.

Speckle reduction of medical ultrasound images using deep learning with fully convolutional network

Smoothing filters are frequently used for speckle reduction of medical ultrasound images. However, such filters may cause loss of the detailed structures of tissues in terms of image contrast. To improve image contrast in speckle reduction, we investigated a filter for medical ultrasound images using deep learning with a fully convolutional network, which was trained with pairs of input and target data generated by computer simulation. The proposed method achieved higher contrast-to-noise ratio and contrast values than the conventional methods with about 300 times faster processing speed than the NL-means filter.

Concentration-dependent circularly polarized luminescence of chiral cyclometalated platinum(II) complexes for electroluminescence

Circularly polarized organic light-emitting diodes (CP-OLEDs) have been attracting increasing attention due to the direct generation of CP light, and they show the improved image contrast and luminous efficiency in OLEDs displays. In this work, a pair of chiral Pt(II) complexes with methylbenzylisocyanide and 1,3-bis(2-pyridyl)benzene as a tridentate N∧C∧N ligand were synthesized. Both of them exhibited bright green emission in dilute acetonitrile solution, while strong orange emission in high concentration of acetonitrile solution. However, when the complexes were doped into polymethyl methacrylate, the green emission originated from intrinsic single molecule can be changed to the red emission from excimer by adjusting the doping ratios. More importantly, the amplification of CPL signal can be achieved compared with the solution state. Furthermore, the monochrome devices were fabricated by utilizing these two enantiomers as emitters.

Benefit of Later-Time-Point PET Imaging of HER3 Expression Using Optimized Radiocobalt-Labeled Affibody Molecules.

HER3-binding affibody molecules are a promising format for visualization of HER3 expression. Cobalt-55, a positron-emitting isotope, with a half-life of 17.5 h, allows for next-day imaging. We investigated the influence of the charge of the radiocobalt–chelator complex on the biodistribution of anti-HER3 affibody molecule (HE)3-ZHER3 and compared the best radiocobalt-labeled variant with a recently optimized gallium-labeled variant. Affibody conjugates (HE)3-ZHER3-X (X = NOTA, NODAGA, DOTA, DOTAGA) were labeled with [57Co]Co (surrogate for 55Co). Affinity measurements, binding specificity and cellular processing were studied in two HER3-expressing cancer cell lines. Biodistribution was studied 3 and 24 h post-injection (pi) in mice with HER3-expressing BxPC-3 xenografts and compared to [68Ga]Ga-(HE)3-ZHER3-NODAGA. Micro-single-photon emission tomography/computed tomography (microSPECT/CT) and micro-positron emission tomography/computed tomography (microPET/CT) imaging was performed 3 and 24 h pi. Stably labeled conjugates bound to HER3 with subnanomolar affinity. [57Co]Co-(HE)3-ZHER3-DOTA had the best tumor retention and a significantly lower concentration in blood than other conjugates, leading to superior tumor-to-blood and tumor-to-liver ratios 24 h pi. Compared to [68Ga]Ga-(HE)3-ZHER3-NODAGA 3 h pi, [57Co]Co-(HE)3-ZHER3-DOTA provided superior imaging contrast in liver 24 h pi. Concluding, the composition and charge of the [57Co]Co–chelator complex influenced the uptake in tumors and normal tissue. [57Co]Co-(HE)3-ZHER3-DOTA provided the best imaging properties among the cobalt-labeled conjugates. Delayed imaging of HER3 expression with [57Co]Co-(HE)3-ZHER3-DOTA improved imaging contrast compared to early-time-point imaging with [68Ga]Ga-(HE)3-ZHER3-NODAGA.

Soft ultrasound priors in optoacoustic reconstruction: Improving clinical vascular imaging

Using the same ultrasound detector, hybrid optoacoustic-ultrasound (OPUS) imaging provides concurrent scans of tissue slices or volumes and visualizes complementary sound- and light-based contrast at similar resolutions. In addition to the benefit of hybrid contrast, spatial co-registration enables images from one modality to be employed as prior information for improving an aspect of the performance of the other modality. We consider herein a handheld OPUS system and utilize structural information from ultrasound images to guide regional Laplacian regularization-based reconstruction of optoacoustic images. Using phantoms and data from OPUS scans of human radial and carotid arteries, we show that ultrasound-driven optoacoustic inversion reduces limited-view artefacts and improves image contrast. In phantoms, prior-integrated reconstruction leads to a 50 % higher contrast-to-noise ratio (CNR) of the image than standard reconstruction, and a 17 % higher structural similarity (SSIM) index. In clinical data, prior-integrated reconstruction detects deep-seated radial arteries with higher CNR than the standard method at three different depths. In this way, the prior-integrated method offers unique insights into atherosclerotic carotid plaques in humans (with p<0.01 between patients and healthy volunteers), potentially paving the way for new abilities in vascular imaging and more generally in optoacoustic imaging.

Automated Tortuosity Analysis of Nerve Fibers in Corneal Confocal Microscopy.

Precise characterization and analysis of corneal nerve fiber tortuosity are of great importance in facilitating examination and diagnosis of many eye-related diseases. In this paper we propose a fully automated method for image-level tortuosity estimation, comprising image enhancement, exponential curvature estimation, and tortuosity level classification. The image enhancement component is based on an extended Retinex model, which not only corrects imbalanced illumination and improves image contrast in an image, but also models noise explicitly to aid removal of imaging noise. Afterwards, we take advantage of exponential curvature estimation in the 3D space of positions and orientations to directly measure curvature based on the enhanced images, rather than relying on the explicit segmentation and skeletonization steps in a conventional pipeline usually with accumulated pre-processing errors. The proposed method has been applied over two corneal nerve microscopy datasets for the estimation of a tortuosity level for each image. The experimental results show that it performs better than several selected state-of-the-art methods. Furthermore, we have performed manual gradings at tortuosity level of four hundred and three corneal nerve microscopic images, and this dataset has been released for public access to facilitate other researchers in the community in carrying out further research on the same and related topics.