2D Histogram Research Articles

Histogram Analysis of Apparent Diffusion Coefficient on Diffusion Weighted Magnetic Resonance Imaging in Differentiation between Low and High Grade Serous Ovarian Cancer.

Ovarian cancer is a leading cause of death in gynecological malignancies. Being the most common subtype in OEC, ovarian serous cancer also includes two subtypes: low grade serous ovarian cancer (LGSC) and high grade serous ovarian cancer (HGSC) (1). The study aims to assess the capability of apparent diffusion coefficient (ADC) histogram analysis and conventional measurements on magnetic resonance imaging (MRI) in differentiating between LGSC and HGSC. We retrospectively recruited 38 patients with pathologically proven ovarian serous epithelial cancer. The mean ADC value was measured by one technician using two methods on post-processed workstation. The ADC value and histogram parameter difference between LGSC and HGSC groups were compared. The correlation between the ADC value and the Ki-67 expression was calculated across both groups. The repeatability of ADC measurements across two methods was good; the ROI method (ADC-roi) had better performance repeatability than the area method (ADC-area). The value of ADC-mean , ADC-min, ADC-max, and ADC-area significantly differed between both groups (p < 0.001). The value of ADC-area correlated inversely with ki-67 expression in the whole group (Pearson coefficient = -0.382, p = 0.02). The 3D computerized-diagnostic model had the best discriminative performance in determining HGSC than 2D and conventional ADC measurements. The 3D model yielded a sensitivity of 100%, a specificity of 95.45%, and an accuracy of 97.73%. In the present study, the 3D ADC histogram model help differentiate HGSC from LGSC with a better performance than conventional ADC measurements.

A Novel Reversible Data Hiding Scheme Based on Pixel-Residual Histogram

Prediction-error expansion (PEE) is the most popular reversible data hiding (RDH) technique due to its efficient capacity-distortion tradeoff. With the generated prediction-error histogram (PEH) and adaptively selected expansion bins, the image redundancy is well exploited by PEE. However, for the most widely used rhombus predictor, the rounding operation which groups different prediction-errors into one value is completely unnecessary. The embedding can be extended to a general case by removing the rounding operation, and more histogram bins can be derived for expansion with a new mapping mechanism. Therefore, in this article, instead of pixel prediction-error, we propose to compute the pixel residuals without the rounding operation, and a new embedding mechanism based on pixel-residual histogram (PRH) modification is devised. In PRH, four bins correspond to one bin in PEH. Then, different from the one-to-one mapping between the prediction-error and pixel modification, a four-to-one mapping between the pixel-residual and pixel modification is established, and the performance is optimized by adaptively selecting four expansion bin pairs for embedding. Since more modification selections are considered, better performance can be obtained. Moreover, the proposed scheme is extended to the two-dimensional (2D) histogram and multiple histograms based embedding, and the performance is further enhanced. The superiority of the proposed method is experimentally verified by comparing it with some state-of-the-art works.

Salp swarm algorithm with iterative mapping and local escaping for multi-level threshold image segmentation: a skin cancer dermoscopic case study

AbstractIf found and treated early, fast-growing skin cancers can dramatically prolong patients’ lives. Dermoscopy is a convenient and reliable tool during the fore-period detection stage of skin cancer, so the efficient processing of digital images of dermoscopy is particularly critical to improving the level of a skin cancer diagnosis. Notably, image segmentation is a part of image preprocessing and essential technical support in the process of image processing. In addition, multi-threshold image segmentation (MIS) technology is extensively used due to its straightforward and effective features. Many academics have coupled different meta-heuristic algorithms with MIS to raise image segmentation quality. Nonetheless, these meta-heuristic algorithms frequently enter local optima. Therefore, this paper suggests an improved salp swarm algorithm (ILSSA) method that combines iterative mapping and local escaping operator to address this drawback. Besides, this paper also proposes the ILSSA-based MIS approach, which is triumphantly utilized to segment dermoscopic images of skin cancer. This method uses two-dimensional (2D) Kapur’s entropy as the objective function and employs non-local means 2D histogram to represent the image information. Furthermore, an array of benchmark function test experiments demonstrated that ILSSA could alleviate the local optimal problem more effectively than other compared algorithms. Afterward, the skin cancer dermoscopy image segmentation experiment displayed that the proposed ILSSA-based MIS method obtained superior segmentation results than other MIS peers and was more adaptable at different thresholds.

Reversible Data Hiding for JPEG Images With Adaptive Multiple Two-Dimensional Histogram and Mapping Generation

Reversible data hiding based on joint photographic experts group (JPEG) images has been extensively studied to enhance embedding performance in terms of visual quality and file size preservation at the desired payload. In this paper, an efficient adaptive RDH method for JPEG images with multiple two-dimensional (2D) histogram modification is proposed. Firstly, the proposed method proposes the block smoothness estimator and the band smoothness estimator, and then combines the two estimators to reduce the embedding distortion as much as possible at the desired payload. Instead of adopting a fixed 2D mapping or choosing one from several empirically-designed mappings for each 2D histogram, the proposed method designs an adaptive 2D mapping generation strategy to adaptively generate a large number of mappings with considering the local characteristics of histogram distribution. Since exhaustively searching for the optimal mapping achieving the highest embedding performance for each 2D histogram is time-consuming, an improved discrete particle swarm optimization is utilized in the proposed method to speed up the optimization process. Extensive experimental results also demonstrate the effectiveness of the proposed method in terms of visual quality and file size increment of the stego image.

Differentiable Histogram Loss Functions for Intensity-based Image-to-Image Translation.

We introduce the HueNet - a novel deep learning framework for a differentiable construction of intensity (1D) and joint (2D) histograms and present its applicability to paired and unpaired image-to-image translation problems. The key idea is an innovative technique for augmenting a generative neural network by histogram layers appended to the image generator. These histogram layers allow us to define two new histogram-based loss functions for constraining the structural appearance of the synthesized output image and its color distribution. Specifically, the color similarity loss is defined by the Earth Mover's Distance between the intensity histograms of the network output and a color reference image. The structural similarity loss is determined by the mutual information between the output and a content reference image based on their joint histogram. Although the HueNet can be applied to a variety of image-to-image translation problems, we chose to demonstrate its strength on the tasks of color transfer, exemplar-based image colorization, and edges → photo, where the colors of the output image are predefined. The code is available at https://github.com/mor-avi-aharon-bgu/HueNet.git.

Noninvasive polarization-based technique for hematocrit monitoring

Subject of study. Polarization images corresponding to the 2D distributions of polarization state parameters for light scattered by biological specimens with different hematocrit levels were obtained by shining laser light in a given polarization state of blood-containing tissue through the nail bed of the left fifth finger. Aim of work. The aim of this work is to determine whether active video polarimetry is suitable for noninvasive real-time monitoring of blood hematocrit and to develop data-based criteria to identify hematocrit levels corresponding to various body states of human test subjects. Method. Physical models of changes in the parameters of polarized radiation during interaction with a biological specimen are used. A prototype optical design was developed for a Stokes video polarimeter capable of recording the irradiance distribution within a single analysis field on the sensitive area of a photodetector array. The digital images were processed using the Stokes–Mueller matrix formalism. A comparative statistical analysis of the 3D distributions for various parameters describing the polarization state (degree of polarization and the azimuth and ellipticity of the polarization ellipse) was performed to determine whether it is possible to detect differences in the hematocrit levels of human blood. Main results. A new approach to noninvasive hematocrit monitoring based on optical polarization is proposed. We obtained 3D distributions and histograms of light polarization state parameters for two human test subjects known to have different hematocrits. Statistical analysis revealed that significant differences in the second–fourth order statistical moments of the light polarization parameters are the most relevant diagnostic indicators for comparative analysis of biological tissues having different blood hematocrit levels. Practical significance. With further development, the proposed method can be applied to both medical diagnosis and emergency treatment. Timely detection of significant deviations from the normal hematocrit based on automated calculation and analysis of the data parameters identified will enable continuous hematocrit monitoring during surgical operations and effective real-time adjustment of blood viscosity.

Micromechanics of non-embedded spruce wood: Novel polishing and indentation protocol

Since the start of nanoindentation in wood, cell walls have been supported by epoxy resin during the microtoming of surfaces. However, wood embedding in epoxy resin was initially developed to harvest thin sections destined for transmission electron microscopy. In nanoindentation, embedding can affect the identified mechanical properties and might be difficult for modified wood samples. For this reason, we propose a protocol that does not involve resin within the wood specimen, uses a more robust miller to achieve surfaces with root means squared roughness of around 35 nm, and can exclude invalid edge indents through a novel evaluation procedure based on fitting 2D histograms with a linear combination of bi-variate lognormal probability density functions. We examined twelve samples of Norway spruce (Picea abies) late wood and their counterparts soaked with a biological preservation pretreatment. A nanoindentation protocol on every sample led to a mean reduced modulus and mean hardness of around 12 % and 26 %, respectively, lower than those found in the literature for embedded Norway spruce. These results agree well with previous results found for Loblolly pine (Pinus taeda L.) when comparing embedded and non-embedded specimens.

Environmental performances evaluation through building information models

The construction industry produces several negative environmental impacts. To promote solutions that reduce these negative impacts, it is crucial to increase awareness among all parties involved in the supply chain. Currently, certification protocols, environmental labels and declarations are the most common tools for promoting sustainability in the construction industry. However, these tools are generally non-mandatory and require specific competencies to thoroughly understand and interpret their outputs, especially for non-specialized users in sustainability assessment. This paper presents a tool for visualizing environmental impacts directly through a Building Information Model, by assigning different colours to model objects according to their environmental performances. Understanding environmental performances data is thus more accessible to non-expert users. Unlike several BIM LCA integration studies that primarily focus on the needs of designers, this contribution considers the perspective of manufacturers. The developed tool focuses on building products and has been carried out with the Autodesk Revit BIM authoring platform and its VPL-based Dynamo plug-in. Through the combination of 3D models and histograms, the tool facilitates manufacturers to visualize with real time updates which phase of a product life cycle requires sustainable innovation, which sub components or process determine the major environmental impacts and calculate the environmental cost indicator of a product.

Мобильный спелеологический параллельный робот с пространственной тактильной системой распознавания контактной поверхности

The paper considers the problem of conducting speleological studies of natural and artificial caves, including channels of the underground rivers, as well as various mine workings in dangerous or inaccessible conditions for a speleologist. In this case, external environment of the objects under study could turn out to be opaque for optical, radio, ultrasonic and other physical methods of monitoring the inner surface, for example, due to partial or complete flooding of the object with water with inhomogeneous suspensions. A solution to the problem is proposed by robotizing the research using a mobile speleological parallel robot (MSPR) with a spatial tactile system for identification of the contact surface. MSPR is created in the form of an active octahedral structure, which edges are rods with linear drives. The rod ends are pivotally connected to the corresponding vertices of the active octahedral structure ensuring its geometric invariability when the linear drives are turned off. As a result, MSPR is able to carry out contact (tactile) mapping of the inner surface under study regardless of the external environment transparency and its geodetic binding to the base coordinate system with visualization in the form of histograms. At the same time, MSPR is able to self-move along the internal surfaces regardless of their spatial orientation. MSPR and its functionalities are described, one of which is the ability to build the 3D histograms of the surrounding space bind to the base (inertial) coordinate system through the mechanical contact. The tactile system of identifying the contact surface makes it possible not only to move the MSPR, but also to tactilely monitor in the external environment that is opaque for optical, radio, ultrasonic and other physical control methods. MSPR allows robotizing speleological research in dangerous, hard-to-reach and inaccessible places for a speleologist, as well as to carry out rescue operations and ensure delivery of the required cargo.

Cover Feature: Structure and Orientation of Water and Choline Chloride Molecules around a Methane Hydrophobe: A Computer Simulation Study (ChemPhysChem 21/2022)

The Cover Feature illustrates the distribution of the different components of aqueous choline chloride solution around the hydrophobe methane. The hydrogen bonds formed between water molecules, choline cations, and chloride ions are shown using light yellow dots. The preferred orientations of water with respect to methane at different distances from the latter are quantified in a 2D histogram. More information can be found in the Research Article by Rajarshi Chakrabarti and co-workers.

CRAAS: A European Cloud Regime dAtAset Based on the CLAAS-2.1 Climate Data Record

Given the important role of clouds in our planet’s climate system, it is crucial to further improve our understanding of their governing processes as well as the resulting spatio-temporal variability of their properties. This co-variability of different cloud optical properties is adequately represented through the well-established concept of cloud regimes. The focus of the present study lies on the creation of a cloud regime dataset over Europe, named “Cloud Regime dAtAset based on the CLAAS-2.1 climate data record” (CRAAS), in order to analyze their variability and their changes at different spatio-temporal scales. In addition, co-occurrences between the cloud regimes and large-scale weather patterns are investigated. The CLoud property dAtAset using Spinning Enhanced Visible and Infrared (SEVIRI) edition 2.1 (CLAAS-2.1) data record, which is produced by the Satellite Application Facility on Climate Monitoring (CM SAF), was used as the basis for the derivation of the cloud regimes over Europe for a 14-year period (2004–2017). In particular, the cloud optical thickness (COT) and cloud top pressure (CTP) products of CLAAS-2.1 were used in order to compute 2D histograms. Then, the k-means clustering algorithm was applied to the generated 2D histograms in order to derive the cloud regimes. Eight cloud regimes were identified, which, along with the geographical distribution of their frequency of occurrence, assisted in providing a detailed description of the climate of the cloud properties over Europe. The annual and diurnal variabilities of the eight cloud regimes were studied, and trends in their frequency of occurrence were also examined. Larger changes in the frequency of occurrence of the produced cloud regimes were found for a regime associated to alto- and nimbo-type clouds and for a regime connected to shallow cumulus clouds and fog (−0.65% and +0.70% for the time period of the study, respectively).

Reversible Data Hiding With Brightness Preserving Contrast Enhancement by Two-Dimensional Histogram Modification

Recently, contrast enhancement with reversible data hiding (CE-RDH) has been proposed for digital images to hide useful data into contrast-enhanced images. In existing schemes, one-dimensional (1D) or two-dimensional (2D) histogram is equalized during the process of CE-RDH so that an original image can be exactly recovered from its contrast-enhanced version. However, noticeable brightness change and color distortion may be introduced by applying these schemes, especially in the case of over enhancement. To preserve image quality, this paper presents a new 2D histogram based CE-RDH scheme by taking brightness preservation (BP) into account. In particular, the row or column of histogram bins with the maximum total height are chosen to be expanded at each time of histogram modification, while the row or column of bins to be expanded next are adaptively chosen according to the change of image brightness. Experimental results on three color image sets demonstrate efficacy and reversibility of the proposed scheme. Compared with the schemes using 1D histogram, image brightness can be preserved more finely by modifying the generated 2D histogram. Moreover, our proposed scheme preserves image color and brightness while achieving better image quality than the existing schemes.

Performance optimization of water cycle algorithm for multilevel lupus nephritis image segmentation

Lupus nephritis (LN) is one of the most common and serious clinical manifestations of systemic lupus erythematosus (SLE), which causes serious damage to the kidneys of patients. To effectively assist the pathological diagnosis of LN, many researchers utilize a scheme combining multi-threshold image segmentation (MIS) with metaheuristic algorithms (MAs) to classify LN. However, traditional MAs-based MIS methods tend to fall into local optima in the segmentation process and find it difficult to obtain the optimal threshold set. Aiming at this problem, this paper proposes an improved water cycle algorithm (SCWCA) and applies it to the MIS method to generate an SCWCA-based MIS method. Besides, this MIS method uses a non-local means 2D histogram to represent the image information and utilizes Renyi's entropy as the fitness function. First, SCWCA adds a sine initialization mechanism (SS) in the initial stage of the original WCA to generate the initial solution to improve the population quality. Second, the covariance matrix adaptation evolution strategy (CMA-ES) is applied in the population location update stage of WCA to mine high-quality population information. To validate the excellent performance of the SCWCA-based MIS method, the comparative experiment between some peers and SCWCA was carried out first. The experimental results show that the solution of SCWCA was closer to the global optimal solution and can effectively deal with the local optimal problems. In addition, the segmentation experiments of the SCWCA-based MIS method and other equivalent methods on LN images showed that the former can obtain higher-quality segmented LN images.

A fine‐to‐coarse‐to‐fine weakly supervised framework for volumetric SD‐OCT image segmentation

AbstractObtaining accurate segmentation of central serous chorioretinopathy in spectral‐domain optical coherence tomography (SD‐OCT) is critical for the determination of the disease severity. Although existing methods achieve considerable segmentation results, they heavily depend on large‐scale data with high‐quality annotations. Also, the lesions bear a large shape variation across different patients, which are often difficult to encode. To address the above problems, we propose a fine‐to‐coarse‐to‐fine weakly supervised framework. Specifically, global alternate max‐avg pooling (GTP) network can be employed to locate the lesion regions accurately by using only image‐level annotations. A network module based on the GTP network and a semantic transfer module are proposed to iteratively guide the network to continuously discover and expand the target lesion regions. Then, we employ 3D grey distribution histogram to generate pseudo‐volumetric labels. Finally, a novel 3D level set loss function is proposed to perform coarse‐to‐fine volumetric segmentation. Experiments on a challenging dataset demonstrate that the performance of our proposed method is closer to those of models trained with pixel‐level supervision.

Novel Deep-Learning Modulation Recognition Algorithm Using 2D Histograms over Wireless Communications Channels.

Modulation recognition (MR) has become an essential topic in today’s wireless communications systems. Recently, convolutional neural networks (CNNs) have been employed as a potent tool for MR because of their ability to minimize the feature’s susceptibility to its surroundings and reduce the need for human feature extraction and evaluation. In particular, these investigations rely on the unrealistic assumption that the channel coefficient is typically one. This motivates us to overcome the previous constraint by providing a novel MR suited to fading wireless channels. This paper proposes a novel MR algorithm that is capable of recognizing a broad variety of modulation types, including M-ary QAM and M-ary PSK, without enforcing any restrictions on the modulation size, M. The analysis has shown that each modulation choice has a distinct two-dimensional in-phase quadrature histogram. This property is beneficially utilized to design a convolutional neural-network-based MR algorithm. When compared to the existing techniques, Monte Carlo simulations demonstrated the success of the proposed design.

A Content-Aware Non-Local Means Method for Image Denoising

Parameter setting and information redundancy are essential issues for the non-local means (NLM) algorithm. This paper introduces a new factor based on the Hessian matrix to adapt the smoothing parameter. Then, a strategy is proposed to implement the NLM by representing patches in terms of features, which uses the 2D histogram and summed-area table. Compared with other methods, the metric for patch similarity in this paper is based on statistical features of patches instead of Euclidean distance. More importantly, not many predefined thresholds are needed. Experimental results show that the proposed algorithm obtains better visual quality and numerical results, especially for images with rich contents and high noise.

Location Privacy Protection for IoT-based WBANs using Anonymous Authentication and Improved AES Encryption

Advances in wireless communications, embedded systems, and integrated circuit technologies have enabled the wireless body area network (WBAN) to become a promising networking paradigm. Over the last decade, as an important part of the Internet of Things, we have witnessed WBANs playing an increasing role in modern medical systems because of its capabilities to collect real-time biomedical data through intelligent medical sensors in or around the patients' body and send the collected data to remote medical personnel for clinical diagnostics. WBANs not only bring us conveniences but also bring along the challenge of keeping data's confidentiality and preserving patients' privacy. In the previous, anonymous authentication (AA) schemes for WBANs were proposed to enhance security by protecting patients' identities and by encrypting medical data. However, many of these schemes are not secure enough. The AA scheme for WBANs and point out that it is not secure for medical applications by proposing an impersonation attack. In this project, propose an Improved AES with secure anonymous authentication framework for WBANs and prove that it is provably secure. The comprehensive analysis section shows that the proposed scheme overcomes the security weaknesses in the existing schemes and also provides low computation cost during anonymous authentication. Keywords—: Cross-modality, contrast enhancement, 2D histogram specification (HS), SSIM gradient, tumor segmentation.

Reversible Data Hiding Scheme Based on Coefficient Pair Mapping for Videos H.264/AVC without Distortion Drift

Reversible data hiding is a technique for embedding secret data into a cover media. Such technique has the ability to recover marked cover media to its original version after extracting the secret data. In this paper, a new reversible data hiding algorithm for videos H.264/AVC is proposed to improve the embedding capacity while the distortion drift is prevented. To embed the secret data into videos H.264/AVC without any intra-frame distortion drift, in our proposed scheme, the relationship of QDCT coefficients is explored. Then, a coefficient pair mapping mechanism in 2D histogram is introduced for embedding data. The experimental results demonstrated that the proposed scheme obtains reversibility. The proposed scheme prevents intra-frame distortion drift on the marked videos. In addition, the embedding capacity of the proposed scheme is superior to that of existing schemes while guaranteeing the high visual quality of marked videos.

Fusion of Multidimensional CNN and Handcrafted Features for Small-Sample Hyperspectral Image Classification

Hyperspectral image (HSI) classification has attracted widespread concern in recent years. However, due to the complexity of the HSI gathering environment, it is difficult to obtain a great number of HSI labeled samples. Therefore, how to effectively extract the spatial–spectral feature with small-scale training samples is the crucial point of HSI classification. In this paper, a novel fusion framework for small-sample HSI classification is proposed to fully combine the advantages of multidimensional CNN and handcrafted features. Firstly, a 3D fuzzy histogram of oriented gradients (3D-FHOG) descriptor is proposed to fully extract the handcrafted spatial–spectral feature of HSI pixels, which is suggested to be more robust by overcoming the local spatial–spectral feature uncertainty. Secondly, a multidimensional Siamese network (MDSN), which is updated by minimizing both contrastive loss and classification loss, is designed to effectively exploit the CNN-based spatial–spectral features from multiple dimensions. Finally, the proposed MDSN combined with 3D-FHOG is utilized for small-sample HSI classification to verify the effectiveness of our proposed fusion framework. The experimental results on three public data sets indicate that the proposed MDSN combined with 3D-FHOG is significantly better than the representative handcrafted feature-based and CNN-based methods, which in turn demonstrates the superiority of the proposed fusion framework.

High‐Resolution Time‐Correlated Single‐Photon Counting Using Electro‐Optic Sampling

AbstractA simple, practical method based on electro‐optic gating is experimentally shown to improve the temporal resolution of single‐photon detection by more than 16 times. Delay times between ultrafast single photons and a reference clock are stretched by a desired programmable sampling gate factor, allowing reconstruction of delay histograms with ≈0.001 photons per pulse to within 60 ps. By transferring the bandwidth of RF electronics to single‐photon counting, complex single‐photon signals with large time‐bandwidth products &gt; 2000 are temporally stretched up so that they can be resolved by slow detectors, irrespective of the quality of the detector instrument response function. This method is also applied to biphotons, in order to reconstruct the 2D histogram of joint detection delays with 15 times better resolution. The phenomenon of nonlocal dispersion, which is not resolvable directly with the slow detectors, is then observable to within the 98 ps level. The proof‐of‐concept demonstration uses off‐the‐shelf commercial fiber‐integrated LiNbO3 modulators and RF electronics, and the method is readily integrable on‐chip and to speeds &gt;100 GHz, offering a practical solution to ultrafast time‐correlated single‐photon counting beyond the research laboratory.