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A video enhancement method based on applying gamma correction is proposed. Its realization implies processing all video frames using a single algorithm. The proposed algorithm, due to the preservation of inter-frame coherence throughout the entire video, significantly reduces the time spent searching for the optimal value of the gamma parameter, which ensures the highest quality of the frame reaching the maximum possible level in the automatic mode in the sense of both visual observation and the determination of key points and the selection of contours of objects in images. The method is characterized by high adaptability to sudden changes in the lighting of the scene, preservation of interframe coherence, and the absence of any side negative artifacts in the enhanced video. A toolkit for automatically determining the optimal value of the gamma parameter for video frames is developed. It significantly increases the efficiency of video analytics systems, image and video segmentation, and processing due to reducing the negative impact of the scene lighting mode on image quality. [ABSTRACT FROM AUTHOR]

In image analysis and processing, image segmentation is one of the most important functions. The outcomes of segmentation have such significance on all subsequent image analysis operations, covering object tracking and description, feature measurement, and even higher-level tasks like object recognition. Malicious code detection is becoming increasingly significant, and current models must be improved. Hence forth the Image segmentation in the field of Malware image classification is a significant task. The sectional structure or region of interest must be identified and extracted during the segmentation process so that it can be evaluated independently. There are various reviews stating the traditional approach of image segmentation in various fields. The necessity of image segmentation in malicious image is extracting data for classification using CNN is discussed. In this work we use malimg_paper_dataset_imgs with 9,339 malware images. Various segmentation techniques were used to enhance the malware images. Those enhanced malicious image were applied in CNN architecture and Mal_CNN for classification and a comparative result is been discussed. The malicious images in dataset after incorporating segmentation have achieved 95% of accuracy in CNN architecture and 97% with Mal_CNN. [ABSTRACT FROM AUTHOR]

To explore the relationship between quantitative perfusion histogram parameters of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) with the expression of tumor tissue epidermal growth factor receptor (EGFR), vascular endothelial growth factor (VEGF) and EGFR gene mutations in non-small cell lung cancer (NSCLC). A total of 44 consecutive patients with known NSCLC were recruited from March 2018 to August 2021. Histogram parameters (mean, uniformity, skewness, energy, kurtosis, entropy, percentile) of each (Ktrans, Kep, Ve, Vp, Fp) were obtained by Omni Kinetics software. Immunohistochemistry staining was used in the detection of the expression of VEGF and EGFR protein, and the mutation of EGFR gene was detected by PCR. Corresponding statistical test was performed to compare the parameters and protein expression between squamous cell carcinoma (SCC) and adenocarcinoma (AC), as well as EGFR mutations and wild-type. Correlation analysis was used to evaluate the correlation between parameters with the expression of VEGF and EGFR protein. Fp (skewness, kurtosis, energy) were statistically significant between SCC and AC, and the area under the ROC curve were 0.733, 0.700 and 0.675, respectively. The expression of VEGF in AC was higher than in SCC. Fp (skewness, kurtosis, energy) were negatively correlated with VEGF (r = − 0.527, − 0.428, − 0.342); Ktrans (Q50) was positively correlated with VEGF (r = 0.32); Kep (energy), Ktrans (skewness, kurtosis) were positively correlated with EGFR (r = 0.622, r = 0.375, 0.358), some histogram parameters of Kep, Ktrans (uniformity, entropy) and Ve (kurtosis) were negatively correlated with EGFR (r = − 0.312 to − 0.644). Some perfusion histogram parameters were statistically significant between EGFR mutations and wild-type, they were higher in wild-type than mutated (P < 0.05). Quantitative perfusion histogram parameters of DCE-MRI have a certain value in the differential diagnosis of NSCLC, which have the potential to non-invasively evaluate the expression of cell signaling pathway-related protein. [ABSTRACT FROM AUTHOR]

This article discusses an alternative method for estimating marginal probability densities of the solution to stochastic differential equations (SDEs). Two algorithms for calculating the numerical–statistical projection estimate for distributions of solutions to SDEs using Legendre polynomials are proposed. The root-mean-square error of this estimate is studied as a function of the projection expansion length, while the step of a numerical method for solving SDE and the sample size for expansion coefficients are fixed. The proposed technique is successfully verified on three one-dimensional SDEs that have stationary solutions with given one-dimensional distributions and exponential correlation functions. A comparative analysis of the proposed method for calculating the numerical–statistical projection estimate and the method for constructing the histogram is carried out. [ABSTRACT FROM AUTHOR]

Teaching veterinary anatomy using digital platforms requires improved image quality, which may influence the fixation process. This study aimed to compare four embalming solutions for high‐colour‐quality images of different tissues compared to the original image. Four equine left pelvic limbs were cut into metameres and divided equally for application of 10% formaldehyde, 96% glycerine, 33% hypersaturated NaCl solution and modified Larssen solution, respectively, which was maintained for 3 days. After drying for 3 days at room temperature, photographs were obtained at time 0 (T0), without any fixation process (original colour); time 1 (T1), immediately after removal from the solutions; and every 24 h for 3 days (T2–T4). The image colour quality was investigated by digitally evaluating the cortical bone, tendon and bone marrow using histograms and CIEDE2000 as well as by 10 specialists in an online survey. CIEDE2000 and histograms revealed that all fixation solutions changed the original tissue colour at all the time points (p < 0.0001). According to the specialists, the 33% saline solution produced the best results compared to the original one. The modified Larssen solution demonstrated better results for the tendon, marrow and cortical bone at T3 (p = 0.0015). Considering the colour of digital images, the modified Larssen solution provided the best results; however, the visual evaluation by the specialists revealed the 33% saline solution as the best. [ABSTRACT FROM AUTHOR]

Introduction: Automated haematology analysers provide the histograms of different blood cells. The interpretation of histograms need careful analysis of all the parameters including red blood cells (RBC), white blood cells (WBC) and platelet distribution curves. This analysis is often neglected and underrated part of the hemogram generated by the analyser. If interpreted properly it has the potential to provide valuable diagnostic information even before a higher level investigation is ordered. This is certainly a time saving tool. This study was conducted to study the correlation between RBC histogram charts plotted by the automated haematology analyser and the peripheral smear findings in patients with anaemia in our tertiary care hospital, and how close are these findings with the gold standard of investigation that is microscopy. Material and method: 500 sample of patients of adult age group admitted in Chirayu Medical College, Bhopal were studied from March 2023 to August 2023. Patients with haemoglobin less than 12.5gm/dl were included. The blood samples were run in Mindray BC 6000 plus haematology analyser, which is a 5 part differential counter. The histogram is obtained on the display of the cell counter. Peripheral smears were prepared from same samples of patients received in EDTA vacutainer and stained by Leishman stain. Then microscopy of stained Peripheral smears were done. The peripheral smear findings were then correlated with the histogram charts from cell counter with due knowledge of relevant clinical history. Result: In our study of 500 patients, the histogram suggested 52.2% of cases as microcytic hypochromic anaemia, followed by normocytic normochromic anaemia 19.6%. The histogram suggested 12.6% of patients and 2.2 % of patients suffering from macrocytic anaemia and haemolytic anaemia respectively. In 13.4 % of cases histogram showed dimorphic picture. On confirming with Peripheral Smear finding we found that 53.4% of cases showed picture of microcytic hypochromic anaemia, followed by normocytic normochromic anaemia at 18.8%. The Peripheral Smear finding suggested 11.6% of patients and 3.6 % of patients suffering from macrocytic anaemia and haemolytic anaemia respectively. In 12.6 % of cases Peripheral smear showed dimorphic picture. Conclusion: The peripheral blood smear examination along with Histogram analysis can prove a very promising and complementary tool in diagnosis of anemias. Histogram can provide subtle information about the RBC pathology and many a times can act as time saving method. [ABSTRACT FROM AUTHOR]

Quality control in welding is usually carried out during the visual inspection process and is highly dependent on an operator experience. In this paper, an approach to automatic detection and classification of a defective region is proposed, in which the segmentation of the analyzed photographic image of a weld (i.e., its division into defective and defect-free regions) is performed using the region growing procedure. The starting points for this procedure are selected by the authors’ robust method of interval fusion with preference aggregation (IF&PA) on the base of image histogram analysis. Testing the proposed approach for real life photographic images showed its ability to detect different types of weld defects with higher accuracy compared to traditional methods, such as the Otsu method and k-means. [ABSTRACT FROM AUTHOR]

Purpose: To construct a nomogram based on subjective CT signs and artificial intelligence (AI) histogram parameters to identify invasiveness of lung adenocarcinoma presenting as pure ground-glass nodules (pGGNs) and to evaluate its diagnostic performance. Methods: 187 patients with 228 pGGNs confirmed by postoperative pathology were collected retrospectively and divided into pre-invasive group [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS)] and invasive group [minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC)]. All pGGNs were randomly assigned to training cohort (n = 160) and validation cohort (n = 68). Nomogram was developed using subjective CT signs and AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) curve. Results: The nomogram was constructed with nodule shape, 3D mean diameter, maximum CT value, and skewness. It showed better discriminative power in differentiating invasive lesions from pre-invasive lesions with area under curve (AUC) of 0.849 (95% CI 0.790–0.909) in the training cohort and 0.831 (95% CI 0.729–0.934) in the validation cohort, which performed better than nodule shape (AUC 0.675, 95% CI 0.609–0.741), 3D mean diameter (AUC 0.762, 95% CI 0.688–0.835), maximum CT value (AUC 0.794, 95% CI 0.727–0.862), or skewness (AUC 0.594, 95% CI 0.506–0.682) alone in training cohort (for all, P < 0.05). Conclusion: For pulmonary pGGNs, the nomogram based on subjective CT signs and AI histogram parameters had a good predictive ability to discriminate invasive lung adenocarcinoma from pre-invasive lung adenocarcinoma, and it has the potential to improve diagnostic efficiency and to help the patient management. [ABSTRACT FROM AUTHOR]

In the present scenario, developing an automatic and credible diagnostic system to analyze lung cancer type, stage, and level from computed tomography (C.T.) images is a very challenging task, even for experienced pathologists, due to the nonuniform illumination and artifacts. The nonuniform illumination and artifacts are the low‐frequency changes in image intensity that arise from the sensor and the person's movement while recording the C.T. scanned images. Although numerous machine learning techniques are used to improve the effectiveness of automatic lung cancer diagnostic systems, the classification accuracy of these systems still needs significant improvement to satisfy the real‐time requirement of the diagnostic situations. A new extreme learning machine (ELM) algorithm‐based model (hereafter called XlmNet) is proposed to classify the histopathology scans effectively. XlmNet utilizes The Cancer Imaging Archive (TCIA) dataset. After data collection, the initial stage in XlmNet is preprocessing, including noise removal, histogram equalization, and quality‐improved image. The enhanced Profuse Clustering (EPC) method is implemented for segmenting the affected regions from C.T. scans by image segment using superpixel clustering. The statistical attributes are extracted by using Principal Component Analysis (PCA). ELM classifier helps in classifying the lung nodules. The empirical results of the XlmNet model are related to some advanced classifiers concerning performance metrics. The evaluations of XlmNet on the TCIA dataset reveal that XlmNet outperforms other classification networks with the Accuracy of 0.965, a sensitivity of 0.964, a specificity of 0.865, a precision of 0.962, a Jaccard similarity score (JSS) of 0.95. [ABSTRACT FROM AUTHOR]

This work presents a numerical method to color the bifurcation diagram of any discrete map, based on the distribution of the map's values in its domain. This density-colored diagram reveals information on the uniformity of the map's value distribution across its domain set, as a bifurcation parameter is increased. This diagram can serve as a complementary visual tool for the analysis of chaotic maps, and can be vital for the application of chaotic dynamics. [ABSTRACT FROM AUTHOR]

Liver segmentation is a prolific and important area of research that has been deeply studied for the last three decades. Its prominence is increasing in modern Computer-Aided disease Diagnosis (CAD) to deal with a huge amount of images. In this paper, the CT scan image is resized to 256 X 256, and noise is reduced by a median filter, and then local peaks are acquired. The optimal clusters (k) to be formed by Expectation-Maximization (EM) algorithm are obtained by setting the distance between local peaks and height greater than 5. Formulate k number of clusters using the EM algorithm. Crop random section of liver and obtain all the local peaks greater than average of local peaks. This provides the minimum and maximum threshold values using which a threshold-based segmentation is performed. The anticipated algorithm that is verified on 55 CT scan images offers promising results. The experimental outcomes are compared with the existing cluster-based liver segmentation algorithms. [ABSTRACT FROM AUTHOR]

Window transformation can improve the accuracy of gray level recognition in medical CT images. The default window(127.5, 255)has strong limitations. Manual window transformation requires rich priori knowledge and is inefficient, making it unsuitable for widespread use. Therefore, a self-adaptation algorithm for window transformation in medical CT sequence images is proposed. Firstly, a histogram is drawn according to the maximum and minimum gray level values of the sequence images. Secondly, relevant parameters are set to traverse the histogram to remove parts with frequencies below the threshold T0. After traversing the histogram again, parts with a difference between adjacent groups of frequencies less than the threshold T1 are merged. Finally, the window width and window level are calculated based on the histogram. Experimental results showed that the algorithm improved the mean square error, signal-to-noise ratio and peak signal-to-noise ratio, which could effectively improve the accuracy of gray level recognition. [ABSTRACT FROM AUTHOR]

In clinical reports, patients with pulmonary nodules have been treated successfully with traditional Chinese medicine (TCM). However, due to the lack of pathological data on the reduction or disappearance of pulmonary nodules, whether TCM is effective in treating inflammatory nodules or earlystage cancer nodules remains controversial. The evaluation of the efficacy of TCM intervention in pulmonary nodules is often based on metrics obtaining through computed tomography(CT) such as nodule diameter, CT value, and tumor model integration, which do not comprehensively represent the current status of pulmonary nodule progression. CT radiomics technology provides more comprehensive data for the qualitative diagnosis of ground glass pulmonary nodules. By analyzing the CT histogram parameters using artificial intelligence technology, such as graphics (layers and wave shape), entropy, volume, mass, and skewness, benign and malignant nodules can be identified. It can also be used for evaluating the progress of the nodules, as it is dynamic, intuitive, and non-invasive, which maybe provide a new method for objectively evaluating the efficacy of TCM in treating pulmonary nodules. [ABSTRACT FROM AUTHOR]

Accurate and efficient point cloud registration is essential in various fields, such as robotics, autonomous driving and medical imaging. The size of point clouds presents a significant challenge for existing registration methods. In this paper, a novel point cloud sampling method to improve the performance of the point cloud registration process is proposed. Instead of geometric feature preservation, which is preferred in most existing sampling methods, our approach scales every point and groups the scaled points into clusters to generate a histogram for the point cloud. The histogram is then used to identify the most significant regions of the point cloud to create the downsampled output data. Experimental results demonstrate that the proposed method improves accuracy and is robust against noise. Registration errors are reduced by up to 7% in rotation and 116% in translation. Additionally, the proposed method filtered out up to 98% of noise from the point cloud that was uniformly distributed at a rate of 25%. [ABSTRACT FROM AUTHOR]

MRI segmentation and analysis are significant tasks in clinical cardiac computations. A cardiovascular MR scan with left ventricular segmentation seems necessary to diagnose and further treat the disease. The proposed method for left ventricle segmentation works as a combination of the intelligent histogram-based image enhancement technique with a Light U-Net model. This technique serves as the basis for choosing the low-contrast image subjected to the stretching technique and produces sharp object contours with good contrast settings for the segmentation process. After enhancement, the images are subjected to the encoder–decoder configuration of U-Net using a novel lightweight processing model. Encoder sampling is supported by a block of three parallel convolutional layers with supporting functions that improve the semantics for segmentation at various levels of resolutions and features. The proposed method finally increased segmentation efficiency, extracting the most relevant image resources from depth-to-depth convolutions, filtering them through each network block, and producing more precise resource maps. The dataset of MICCAI 2009 served as an assessment tool of the proposed methodology and provides a dice coefficient value of 97.7%, accuracy of 92%, and precision of 98.17%. [ABSTRACT FROM AUTHOR]

Gaze data are still uncommon in statistics education despite their promise. Gaze data provide teachers and researchers with a new window into complex cognitive processes. This article discusses how gaze data can inform and be used by teachers both for their own teaching practice and with students. With our own eye‐tracking research as an example, background information on eye‐tracking and possible applications of eye‐tracking in statistics education is provided. Teachers indicated that our eye‐tracking research created awareness of the difficulties students have when interpreting histograms. Gaze data showed details of students' strategies that neither teachers nor students were aware of. With this discussion paper, we hope to contribute to the future usage and implementation of gaze data in statistics education by teachers, researchers, educational and textbook designers, and students. [ABSTRACT FROM AUTHOR]

Classification and feature extraction are other planned areas of study, since they build upon the basis of computer picture processing technology and have a direct impact on the results of image recognition. We compared low-level features, texture features, and form-based features to generate an original feature set from the input segmented crop photographs. We used the Histogram of Gradient (as colour-based features) to extract low-level features because of its ease of use and effectiveness. Within the realm of shape-based features, EHD was used to obtain defining properties. In order to have a better understanding of textures, we used GLCM to extract features based on them. When all features are added together, a feature vector with several dimensions is produced. To conclude, the data were classified using artificial neural networks (ANN). MATLAB is used for simulation purposes. The current difficulty is to use smart image processing technology to extract picture properties from the original image with a strong representation. If the signs of a disease on a crop are vague or otherwise complicated, it will be quite difficult to identify those qualities. [ABSTRACT FROM AUTHOR]

Gaze data are still uncommon in statistics education despite their promise. Gaze data provide teachers and researchers with a new window into complex cognitive processes. This article discusses how gaze data can inform and be used by teachers both for their own teaching practice and with students. With our own eye‐tracking research as an example, background information on eye‐tracking and possible applications of eye‐tracking in statistics education is provided. Teachers indicated that our eye‐tracking research created awareness of the difficulties students have when interpreting histograms. Gaze data showed details of students' strategies that neither teachers nor students were aware of. With this discussion paper, we hope to contribute to the future usage and implementation of gaze data in statistics education by teachers, researchers, educational and textbook designers, and students. [ABSTRACT FROM AUTHOR]

This paper aims to early study of detection and diagnosis of kidney tumors and kidney stones using Computed Tomography Scanning CT scan images by digital image processing. Computerized Axial Tomography (CAT) is a special medical imaging technique that provides images with 3D, including much information about the body's construction consisting of bones and organs. A C.T scan uses X-rays to create cross-sectional images of the body and gives the doctor a full explanation of the diagnosis of the situation through the examination. It has been used in five cases of kidney images, including healthy, stones, tumors (cancer), cystic and renal fibrosis. The masking procedure is used to separate the required C.T. images from undesirable ones. The segmentation processes which was used in this study is a thresholding algorithm, applied on C.T. images, used to detect unhealthy tissues (tumor and stone). The results showed that the system is able to distinguish between different tumors of the kidney of cases mentioned above. It's important for the instructor to determine the area of the tumor and stone to avoid damaging the healthy tissue during the treatment. The histogram which was used to study the differences between the healthy and unhealthy depending on the intensity of the image. [ABSTRACT FROM AUTHOR]

The differential privacy histogram publishing method based on grouping cannot balance the grouping reconstruction error and Laplace noise error, resulting in insufficient histogram publishing accuracy. To address this problem, we propose a symmetric histogram publishing method DPHR (differential privacy histogram released). Firstly, the algorithm uses the exponential mechanism to sort the counting of the original histogram bucket globally to improve the grouping accuracy; secondly, we propose an optimal dynamic symmetric programming grouping algorithm based on the global minimum error, which uses the global minimum error as the error evaluation function based on the ordered histogram. This way, we can achieve a global grouping of the optimal error balance while balancing the reconstruction and Laplace errors. Experiments show that this method effectively reduces the cumulative error between the published histogram and the original histogram under long-range counting queries based on satisfying ε -differential privacy and improves the usability of the published histogram data. [ABSTRACT FROM AUTHOR]

The main goal of this paper is the proposal of a key-based steganographic system in which the ratio of capacity and image quality metrics that represents the stego object while reducing the detectability of hidden content was improved. The main contribution of the proposed steganographic system is a new algorithm for selecting stego areas. The area selection algorithm is based on clustering the pixels of the cover object into a predetermined number of clusters. The goal of this selection of areas (clusters) is to group as many homogeneous parts of the image as possible in order to cover these areas with as few rectangular shapes as possible. Since the data on the defined rectangles represent the key of the system, the capacity of the additional secret channel is minimized in this way. On the obtained stego-carriers, an embedding of test random content is performed in order to estimate its detectability. By combining the proposed area selection method with the Minimal Decimal Difference steganographic method, a system was created with an optimal trade-off between detectability of secret content, quality and capacity of the carrier, and the length of the stego-key. Finally, a comparison of the obtained results with relevant adaptive steganographic methods is presented. The proposed concept obtains its verification in one practical system for secure file transfer of controlled cryptographic strength. [ABSTRACT FROM AUTHOR]

Polynomials are common algebraic structures, which are often used to approximate functions, such as probability distributions. This paper proposes to directly define polynomial distributions in order to describe stochastic properties of systems rather than to assume polynomials for only approximating known or empirically estimated distributions. Polynomial distributions offer great modeling flexibility and mathematical tractability. However, unlike canonical distributions, polynomial functions may have non-negative values in the intervals of support for some parameter values; their parameter numbers are usually much larger than for canonical distributions, and the interval of support must be finite. Hence, polynomial distributions are defined here assuming three forms of a polynomial function. Transformations and approximations of distributions and histograms by polynomial distributions are also considered. The key properties of the polynomial distributions are derived in closed form. A piecewise polynomial distribution construction is devised to ensure that it is non-negative over the support interval. A goodness-of-fit measure is proposed to determine the best order of the approximating polynomial. Numerical examples include the estimation of parameters of the polynomial distributions and generating polynomially distributed samples. [ABSTRACT FROM AUTHOR]

Background: Red blood cell (RBC) indices and histogram obtained from automated hematology gives an idea about the morphological changes in anemia and therefore forms the fundamental process for diagnosis of anemia. Along with the peripheral smear, they can be used for anemia typing and to interpret the cause of anemia. Aims and Objectives: (1) Interpretation of histograms in normal persons and patients with different types of anemia. (2) Anemia typing based on RBC histograms complemented with Peripheral smear examination. Materials and Methods: The present prospective study is conducted in Haematology section of Department of Pathology, Shyam Shah Medical College and Sanjay Gandhi Medical Hospital Rewa, Madhya Pradesh. A total of 1000 patients blood sample is collected for complete blood count and anemia typing irrespective of age and gender and examined over a period of 1 year from August 2021 to July 2022. The patient's blood sample is collected in EDTA vacutainer and analyzed on the basis of histogram obtained and peripheral smear slide prepared. Results: In the present study, we noted that the predominant age group affected was 20-30 years. Peripheral smear finding shows normocytic normochromic anemia was seen in 6% cases (60/1000), microcytic hypochromic anemia seen in 24.2% cases (242/1000), macrocytic anemia seen in 14% cases (140/1000), dimorphic anemia seen in 50.2% cases (502/1000), and hemolytic anemia seen in 5.6% cases (56/1000). This peripheral smear finding shows well correlation with findings of histogram pattern and RBC indices, where normocytic normochromic seen in 6.4% of cases (64/1000), microcytic hypochromic anemia seen in 24.4% of cases (244/1000), macrocytic anemia seen in 13.8% of cases (138/1000), dimorphic anemia seen in 49.5% of cases (495/1000), and hemolytic anemia seen in 5.9% of cases (59/1000). The most common anemia is dimorphic anemia (combined nutritional deficiency) in 49.5% cases followed by microcytic hypochromic anemia in 24.4% of cases based on histogram findings along with RBC indices. Conclusion: Histogram guides a technologist about the cases that need actual detailed peripheral smear examination by experts. Our study shows a well correlation in findings of automated hematology analyzer with the microscopic examination. Histogram alone could be used as screening method and when combined with peripheral blood smear findings, they act as useful supplement and by correlating findings of both methods, we could diagnose majority of anemia. [ABSTRACT FROM AUTHOR]

We propose a new method of constructing a variable bin width histogram that can accommodate the unbalanced distribution of the samples yet retaining, as a whole, the good aspect of both equal width (EW) and equal-area (EA) histograms that are being used popularly for data visualization and analysis. We formulate this as an optimal change point detection problem in which the bin boundaries are determined by minimizing the sum of the absolute error or the squared error in each bin. The former is based on Distance Minimization (DM) and new, and the latter is based on Variance Minimization (VM) and is considered the state-of-the-art. The constructed histograms can effectively be used to detect and visualize hidden outliers/anomalies by applying the interquartile range method in each bin. The final histograms are obtained by adjusting bin boundaries and heights accordingly after removing the detected outliers/anomalies. We further propose a method to annotate the constructed bins if the data for annotation is given for each sample as a set of nominal variables, using z -score with respect to their distribution within each bin. We applied our method to both real vinyl greenhouse datasets and two different sets of three synthetic datasets, and confirmed that both DM and VM methods work as intended, both can represent the sample distribution with a smaller number of bins than those by EW and EA methods, The use of interquartile range method can detect anomalies as well as outliers, and the terms selected for annotation are interpretable and reasonable. EW and EA methods have contrasting properties. DM and VM methods lie in between, but the former is closer to EA method and the latter to EW method. DM method runs substantially faster than VM method and performs slightly better than VM method in outlier detection and annotation tasks. [ABSTRACT FROM AUTHOR]

Accurate assessment of radiation-induced breast toxicity is crucial for the management of breast radiation therapy (RT). Standard assessment of breast toxicity based on clinicians' visual inspection and palpation has considerable inter- and intra-observer variability. To overcome this challenge, we present an ultrasound histogram method that objectively evaluates radiation-induced breast toxicity longitudinally. In a prospective study, patients enrolled (n = 67) received ultrasound scans at four time points: prior to RT, last day of RT, 3-4 wk post-RT and 9-12-wk post-RT. Ultrasound scans were acquired at five locations (tumor bed and 3, 6, 9 and 12 o'clock) on both breasts. Two hundred sixty-four ultrasound scans and 2640 B-mode images were analyzed. The histogram differences between irradiated and contralateral breasts were calculated to evaluate radiation-induced breast changes. On the basis of the B-mode images, the severity of breast toxicity was graded as absent, mild, moderate or severe. The performance of the histogram method was assessed with the receiver operating characteristic (ROC) curve. The areas under the ROC curve ranged from 0.78 to 0.9 (sensitivity: 0.88-0.96, specificity: 0.53-0.83) at the lower quadrant for differentiating absent/mild from moderate/severe toxicity at various time points. This study provides preliminary evidence that ultrasound histogram differences can serve as an imaging biomarker to longitudinally assess radiation-induced acute toxicity. [ABSTRACT FROM AUTHOR]

In this article, due to the importance of embedding encrypted information in the image, a watermarking method is proposed based on local statistics in pixels. The advantages of the proposed method include its complete reversibility and very high security, which is due to the use of the quadtree concept, finding similar parts in the image, and embedding information in low‐energy and less significant pixels. This method is an alternative and simplifier of the quadtree method, dividing the image into identical non‐overlapping blocks and then choosing the block with less energy as more suitable for watermarking. Also, two criteria of mean‐squared‐error and peak signal‐to‐noise ratio (PSNR) have been calculated and the PSNR shows that image quality has not changed too much after hiding the information than the original image. It should be noted that since the proposed method is applied to black and white images, the result can be generalized to different color channels in the image, and color images have more visible capabilities in terms of cached memory than black and white images because they contain three dimensions of information. [ABSTRACT FROM AUTHOR]

In this study, the criteria of choosing a neighboring station by k-nearest neighbor method were evaluated through outlier detection. Monthly average temperature, average relative humidity and total precipitation data were used between 1926 and 2012 at 129 stations in Turkey. The first problem when choosing a neighbor according to the extent of nearness is that 0.04% - 3% of neighbors are more than 140 km away and less than 0.4 correlation because of choosing distant neighbors due to lack of data. The second problem is the more distant neighbors show a significantly higher correlation than the 5 closest neighbors at 0.1 - 3% of monthly series. When the neighbor is selected according to the correlation coefficient, the 1st neighbor of each month can be used for only that month in 2% - 8% of the stations. These percentages decrease for 2nd and 3rd neighbors. In 25% - 86% of stations, the 1st neighbors in certain months can be used for all months, but it should be determined which certain months are they. In addition, in 2.5% of the stations, the 1st neighbor of each variable can be used for only that variable. The percentage decreases for 2nd and 3rd neighbors. In 29% of the stations, the 1st neighbors in certain variables can be used for all the variables, but it should be determined which certain variables are they. Other than these, when the detected outliers are removed, the correlation coefficient becomes mostly 1-36% higher, sometimes 1-5% less than for the series with outliers. [ABSTRACT FROM AUTHOR]

Time-to-digital converters (TDCs) are major components for the measurements of time intervals. Recent developments in field-programmable gate array (FPGA) have enabled the opportunity to implement high-performance TDCs, which were only possible using dedicated hardware. In order to eliminate empty histogram bins and achieve a higher level of linearity, FPGA-based TDCs typically apply compensation methods either using multiple delay lines consuming more resources or postprocessing, leading to a permanent loss of temporal information. In this article, we propose a novel TDC with a single delay line and without compensation to realize a highly linear TDC by encoding the states of the delay lines instead of the thermometer code used in the conventional TDCs. The experimental results show our states-based approach achieves an improved differential nonlinearity of [ $-$ 0.998, $-$ 1.533] for time resolution of 5.00 ps, [ $-$ 0.44, 0.49] for 10.04 ps, [ $-$ 0.16, 0.19] for 21.65 ps, [ $-$ 0.10, 0.11] for 43.87 ps, [ $-$ 0.06, 0.07] for 64.12 ps, and [ $-$ 0.07, 0.05] for 87.73 ps, whilst no empty bins have been observed. To the best of authors’ knowledge, the achieved raw linearity together with the zero empty bins and a simple delay line structure exceeds previously reported of the FPGA-based TDCs. [ABSTRACT FROM AUTHOR]

Intensive care units (ICUs) are busy and noisy areas where patients and professional staff can be exposed to acoustic noise for long periods of time. In many cases, noise levels significantly exceed the levels recommended by the official health organisations. This situation can affect not only patient recovery but also professional staff, making ICUs unhealthy work and treatment environments. To introduce the measures and reduce the acoustic noise in the ICU, acoustic noise levels should first be measured and then appropriately analysed. However, in most studies dealing with this problem, measurements have been performed manually over short periods, leading to limited data being collected. They are usually followed by insufficient analysis, which in turn results in inadequate measures and noise reduction. This paper reviews recent works dealing with the problem of excessively high noise levels in ICUs and proposes a more thorough analysis of measured data both in the time and frequency domains. Applied frequency domain analysis identifies the cyclic behaviour of the measured sound pressure levels (SPLs) and detects the dominant frequency components in the SPL time series. Moreover, statistical analyses are produced to depict the patterns and SPLs to which patients in ICUs are typically exposed during their stay in the ICU. It has been shown that the acoustic environment is very similar every night, while it can vary significantly during the day or evening periods. However, during most of the observed time, recorded SPLs were significantly above the prescribed values, indicating an urgent need for their control and reduction. To effectively tackle this problem, more detailed information about the nature of noise during each of the analysed periods of the day is needed. This issue will be addressed in the continuation of this project. [ABSTRACT FROM AUTHOR]

Whilst several methods exist to provide sample estimates of the probability distribution function at several points, for the probability density of continuous stochastic variables, only a gross representation through the histogram is typically used. It is shown that the newly introduced concept of knowable moments (K-moments) can provide smooth empirical representations of the distribution function, which in turn can yield point and interval estimates of the density function at a large number of points or even at any arbitrary point within the range of the available observations. The proposed framework is simple to apply and is illustrated with several applications for a variety of distribution functions. [ABSTRACT FROM AUTHOR]

Objectives: To determine the role of apparent diffusion coefficient (ADC) histogram analysis in the identification of MYCN-amplification status in neuroblastomas. Methods: We retrospectively evaluated imaging records from 62 patients with neuroblastomas (median age: 15 months (interquartile range (IQR): 7–24 months); 38 females) who underwent magnetic resonance imaging at our institution before the initiation of any therapy or biopsy. Fourteen patients had MYCN-amplified (MYCNA) neuroblastoma. Histogram parameters of ADC maps from the entire tumour was obtained from the baseline images and the normalised images. The Mann-Whitney U test was used to compare the absolute and normalised histogram parameters amongst neuroblastomas with and without MYCN-amplification. Receiver operating characteristic (ROC) curves and area under the curves (AUC) were generated for the statistically significant histogram parameters. Cut-offs obtained from the ROC curves were evaluated on an external validation set (n-15, MYCNA-6, F-7, age 24 months (10–60)). A logistic regression model was trained to predict MYCNA by combining statistically significant histogram parameters and was evaluated on the validation set. Results: MYCN-amplified neuroblastomas had statistically significant higher maximum ADC and lower minimum ADC than non-amplified neuroblastomas. They also demonstrated higher entropy, variance, energy, and lower uniformity than non-amplified neoplasms (p > 0.05). Energy, entropy, and maximum ADC had AUC of 0.85, 0.79, and 0.82, respectively. Conclusions: Whole tumour ADC histogram analysis of neuroblastomas can differentiate between tumours with and without MYCN-amplification. These parameters can help identify areas for targeted biopsies or can be used to predict subtypes of these high-risk tumours before biopsy results are available. Key Points : • MYCN-amplification significantly affects treatment decisions in neuroblastomas. • MYCN-amplified neuroblastomas had significantly different ADC histogram metrics as compared to tumours without amplification. • ADC histogram metrics can be used to predict MYCN-amplification status based on imaging. [ABSTRACT FROM AUTHOR]

Background: Medulloblastoma, a high-grade embryonal tumor, is the most common primary brain malignancy in the pediatric population. Molecular medulloblastoma groups have documented clinically and biologically relevant characteristics. Several authors have attempted to differentiate medulloblastoma molecular groups and histology variants using diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps. However, literature on the use of ADC histogram analysis in medulloblastomas is still scarce.Objective: This study presents data from a sizable group of pediatric patients with medulloblastoma from a single institution to determine the performance of ADC histogram metrics for differentiating medulloblastoma variants and groups based on both histological and molecular features.Materials and Methods: In this retrospective study, we evaluated the distribution of absolute and normalized ADC values of medulloblastomas. Tumors were manually segmented and diffusivity metrics calculated on a pixel-by-pixel basis. We calculated a variety of first-order histogram metrics from the ADC maps, including entropy, minimum, 10th percentile, 90th percentile, maximum, mean, median, skewness and kurtosis, to differentiate molecular and histological variants. ADC values of the tumors were also normalized to the bilateral cerebellar cortex and thalami. We used the Kruskal-Wallis and Mann-Whitney U tests to evaluate differences between the groups. We carried out receiver operating characteristic (ROC) curve analysis to evaluate the areas under the curves and to determine the cut-off values for differentiating tumor groups.Results: We found 65 children with confirmed histopathological diagnosis of medulloblastoma. Mean age was 8.3 ± 5.8 years, and 60% (n = 39) were male. One child was excluded because histopathological variant could not be determined. In terms of medulloblastoma variants, tumors were classified as classic (n = 47), desmoplastic/nodular (n = 9), large/cell anaplastic (n = 6) or as having extensive nodularity (n = 2). Seven other children were excluded from the study because of incomplete imaging or equivocal molecular diagnosis. Regarding medulloblastoma molecular groups, there were: wingless (WNT) group (n = 7), sonic hedgehog (SHH) group (n = 14) and non-WNT/non-SHH (n = 36). Our results showed significant differences among the molecular groups in terms of the median (P = 0.002), mean (P = 0.003) and 90th percentile (P = 0.002) ADC histogram metrics. No significant differences among the various medulloblastoma histological variants were found.Conclusion: ADC histogram analysis can be implemented as a complementary tool in the preoperative evaluation of medulloblastoma in children. This technique can provide valuable information for differentiating among medulloblastoma molecular groups. ADC histogram metrics can help predict medulloblastoma molecular classification preoperatively. [ABSTRACT FROM AUTHOR]

In today's research, remote sensing applications play an important role. It might foresee any crisis, provide weather forecasting updates, geographical research, military security, and so forth. Object detection and recognition of remote sensing satellite images, however, is a difficult task. To solve the problem of distinct in remote sensing images, in this work the system, initially, the dataset goes under the phase of testing and training process. After the data is pre-processed with a fuzzy filter, the features are recovered utilizing an enhanced method by constructing effective CNN based Features from the Accelerated Segment Test feature extraction technique (FASTCNN) Convolutional Neural Network. Object detection classification is done via an optimized Naive Bayes (NB) classifier, which detects objects significantly faster and keeps the system running at a higher accuracy rate. The proposed feature extraction strategy outperforms the previous approach in terms of performance. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the dominant causes of death in the world. Mammography is the standard for early detection of breast cancer. In examining mammograms, the overall parenchyma pattern of the left and right breast was placed side by side for symmetry assessed of left and right breast tissue by radiologist. Thus, in building computer-aided diagnosis (CAD) system for screening mammography, it is necessary to adapt the working procedure of the radiologist. In this study, 30 training images and 30 testing images from Kotabaru Oncology Clinic in Yogyakarta were used. The first step was to enhance the image quality with median filter and contrast limited adaptive histogram equalization (CLAHE). Then, feature extraction was processed by histogram-based and by gray level co-occurrence matrix (GLCM) based. Furthermore, the similarity measurement process was used to measure the difference value between selected features, i.e. angular second moment (ASM), inverse difference moment (IDM), contrast, entropy based GLCM, and energy, on the left and right mammograms. This process was intended to assess the symmetry of left and right mammograms as radiologists do in mammography screening. The obtained results of the classification between normal and abnormal images with backpropagation algorithm were accuracy of 0.933, sensitivity of 0.833, and specificity of 1.000. [ABSTRACT FROM AUTHOR]

• Histogram features can be used to obtain black-white brightness ratio, eliminating the need for complex image processing methods. • Unified model allows for more precise soiling loss quantification with camera altitude angle information. In this study, a model correction and a new brightness ratio calculation method were proposed to improve a previously reported checkered pattern image-based soiling quantification technique. The image's black-white brightness ratio (BWR) was obtained for various levels of soiling and camera altitude angles, using only the histogram of the visible-light image of a checkered pattern, consequently denoted hBWR. Normal-incidence soiling loss of a PV reference cell subjected to the same soiling levels as the checkered pattern was determined using the short-circuit current method and a solar simulator. The hBWR quantity was found to be effectively identical to the BWR quantity determined from a more complex image processing method previously used. Also, a unified relationship was found between hBWR and soiling loss corrected by the sine of the camera altitude angle, for all soiling levels and all camera altitude angles. These findings show the feasibility of implementing the image-based soiling quantification technique using readily available photography equipment and generic image processing tools. [ABSTRACT FROM AUTHOR]

Simple Summary: Glioma represent approximately one-third of all brain tumors. Although they differ clinically, histologically and genetically, they often are not distinguishable by morphological magnetic resonance imaging (MRI) diagnostics. We therefore investigated in this retrospective study whether diffusion weighted imaging (DWI) using a radiomic approach could provide complementary information with respect to tumor differentiation and cell proliferation, as well as the underlying genetic and epigenetic tumor profile. We identified several histogram features that could facilitate presurgical tumor grading and potentially enable one to draw conclusions about tumor characteristics on a cellular and subcellular scale. (1) Background: Astrocytic gliomas present overlapping appearances in conventional MRI. Supplementary techniques are necessary to improve preoperative diagnostics. Quantitative DWI via the computation of apparent diffusion coefficient (ADC) histograms has proven valuable for tumor characterization and prognosis in this regard. Thus, this study aimed to investigate (I) the potential of ADC histogram analysis (HA) for distinguishing low-grade gliomas (LGG) and high-grade gliomas (HGG) and (II) whether those parameters are associated with Ki-67 immunolabelling, the isocitrate-dehydrogenase-1 (IDH1) mutation profile and the methylguanine-DNA-methyl-transferase (MGMT) promoter methylation profile; (2) Methods: The ADC-histograms of 82 gliomas were computed. Statistical analysis was performed to elucidate associations between histogram features and WHO grade, Ki-67 immunolabelling, IDH1 and MGMT profile; (3) Results: Minimum, lower percentiles (10th and 25th), median, modus and entropy of the ADC histogram were significantly lower in HGG. Significant differences between IDH1-mutated and IDH1-wildtype gliomas were revealed for maximum, lower percentiles, modus, standard deviation (SD), entropy and skewness. No differences were found concerning the MGMT status. Significant correlations with Ki-67 immunolabelling were demonstrated for minimum, maximum, lower percentiles, median, modus, SD and skewness; (4) Conclusions: ADC HA facilitates non-invasive prediction of the WHO grade, tumor-proliferation rate and clinically significant mutations in case of astrocytic gliomas. [ABSTRACT FROM AUTHOR]

Storing, transferring, and processing high-dimensional electroencephalogram (EGG) signals is a critical challenge. The goal of EEG compression is to remove redundant data in EEG signals. Medical signals like EEG must be of high quality for medical diagnosis. This paper uses a compression system with near-zero Mean Squared Error (MSE) based on Discrete Cosine Transform (DCT) and double shift coding for fast and efficient EEG data compression. This paper investigates and compares the use or non-use of delta modulation, which is applied to the transformed and quantized input signal. Double shift coding is applied after mapping the output to positive as a final step. The system performance is tested using EEG data files from the CHB-MIT Scalp EEG Database. Compression Ratio (CR) is used to evaluate the compression system performance. The results are encouraging when compared with previous works on the same data samples. [ABSTRACT FROM AUTHOR]

Purpose: The present study assesses whether malignant and benign lesions can be distinguished through histogram analysis of non-fat-suppressed T1-weighted and fatsuppressed T2-weighted breast magnetic resonance images (MRIs). Materials and Methods: MRIs of 20 malignant and 20 benign breast lesions were reviewed retrospectively by histogram analysis performed using Osirix V.4.9 software. The regions of interest (ROIs) were drawn manually to include almost the entire lesion, and values from these ROIs were used to calculate gray-level intensity mean, standard deviation, entropy, uniformity, skewness, kurtosis, and percentile values. Results: In non-fat-suppressed T1-weighted images, the minimum, 1st, 3rd, 5th, 10th and 25th percentile values were significantly lower in the malignant lesions than in the benign lesions. The minimum value had sensitivity of 70% and specificity of 63.2%. On the fat-suppressed T2- weighted images, skewness was significantly higher while uniformity was significantly lower in malignant lesions than benign lesions. Skewness had 68.4% sensitivity and 60% specificity, and uniformity had 65% sensitivity and 68.4% specificity. Conclusion: The results of this study demonstrated that histogram analysis of non-fat-suppressed T1-weighted and fat-suppressed T2-weighted images can be used to differentiate malignant and benign lesions in breast MRI. [ABSTRACT FROM AUTHOR]

In this study, a new approach is proposed for the stability analysis of tailings storage facility (TSF) dams. Given the considerable scale of such objects, te correct identification of geotechnical layers for computational models and the determination of reliable parameters can present significant challenges. Therefore, the investigation focuses on distinguishing additional sublayers within a geological substratum to enhance the credibility of stability assessment. The procedure of sublayer separation is directly based on soil parameters derived from geotechnical tests and it aims at reducing the variability in the parameters of individual sublayers. Specifically, the segmentation technique known as the Gaussian mixture model (GMM) is employed for this purpose. Subsequently, the spatial arrangement of sublayers within the finite element model domain is identified using the kriging technique. Utilizing Monte Carlo simulations statistical distribution of factor of safety (FOS) is obtained for the structure. Finally, the stability is assessed using the Cornell reliability index. The application of this developed methodology is demonstrated through a case study of the Żelazny Most TSF. Additional sublayers are identified in Neogene clays constituting the dominant layer beneath the facility. In total, six different segmentation variants are proposed and their performance is evaluated. The results show that the segmentation of additional sublayers produces a clear reduction in the scatter of the FOS. Consequently, the estimated simplified reliability index surpasses that of the case, which does not involve the separation of additional layers. [Display omitted] • Segmentation of sublayers improves credibility of the TSF stability assessment. • Performance of segmentation is demonstrated in the case study of real TSF. • Segmentation of sublayers provides computational model with more accurate information on subsoil. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the usefulness of diffusion-weighted MR imaging with ADC value and histogram analysis of ADC in the prediction of response to neoadjuvant chemotherapy (NAC) in patients with muscle-invasive bladder cancer (MIBC). Methods: Fifty-eight consecutive patients with clinical T2-4aN0M0 MIBC who underwent MRI before and after NAC were enrolled in the prospective study. The evaluation of response to NAC was based on the pathologic T (pT) stage after surgery. Patients with non-muscle-invasive residual cancer (pTa, pTis, pT1) were defined as responders, while those with muscle-invasive residual cancer (≥ pT2) were defined as non-responders. The ADC value measured from a single-section region of interest and ADC histogram parameters derived from whole-tumor volume of interest in responder and non-responder were compared using the Mann–Whitney U test or independent samples t test. ROC curve analysis was used to evaluate the diagnostic performance of ADC value and ADC histogram parameters in predicting the response to NAC. Results: The pretreatment ADC value of responders ([1.33 (± 0.21)] × 10−3mm2/s) was significantly higher than that of non-responders ([1.09 (± 0.08)] × 10−3mm2/s) (P <.001). Most of the pretreatment ADC histogram parameters (Mean, 10th, 25th, 50th, 75th, and 90th percentiles) of responders were significantly higher than that of non-responders (P <.001). The AUC was highest for the pretreatment ADC value (0.88; 95% confidence interval: 0.77, 0.95; P <.001). Conclusion: Diffusion-weighted MR imaging with ADC value and histogram analysis of ADC are useful to predict NAC response in patients with MIBC. [ABSTRACT FROM AUTHOR]

I introduce binscatterhist, a command that extends the functionality of the popular binscatter command (Stepner, 2013, Statistical Software Components S457709, Department of Economics, Boston College). binscatter allows researchers to summarize the relationship between two variables in an informative and versatile way by collapsing scattered points into bins. However, information about the variables' frequencies gets lost in the process. binscatterhist solves this issue by allowing the user to further enrich the graphs by plotting the variables' underlying distribution. The binscatterhist command includes options for different regression methods, including reghdfe (Correia, 2014, Statistical Software Components S457874, Department of Economics, Boston College) and areg, and robust and clustered standard errors, with automatic reporting of estimation results and sample size. [ABSTRACT FROM AUTHOR]

In this study, we propose a deep learning related framework to analyze S&P500 stocks using bi-dimensional histogram and autoencoder. The bi-dimensional histogram consisting of daily returns of stock price and stock trading volume is plotted for each stock. Autoencoder is applied to the bi-dimensional histogram to reduce data dimension and extract meaningful features of a stock. The histogram distance matrix for stocks are made of the extracted features of stocks, and stock market network is built by applying Planar Maximally Filtered Graph(PMFG) algorithm to the histogram distance matrix. The constructed stock market network represents the latent space of bi-dimensional histogram, and network analysis is performed to investigate the structural properties of the stock market. we discover that the structural properties of stock market network are related to the dispersion of bi-dimensional histogram. Also, we confirm that the autoencoder is effective in extracting the latent feature of the bi-dimensional histogram. Portfolios using the features of bi-dimensional histogram network are constructed and their investment performance is evaluated in comparison with other benchmark portfolios. We observe that the portfolio consisting of stocks corresponding to the peripheral nodes of bi-dimensional histogram network shows better investment performance than other benchmark stock portfolios. [ABSTRACT FROM AUTHOR]

Contact orientation distributions are widely used to describe the fabric of granular assemblies and its evolution under load. Different types of visualisations, mainly histograms, are used in the literature to plot these distributions. While there are different ways to create such histograms, however, there is generally no discussion of how the chosen way affects the visualisation of the distribution and its interpretation. We develop in this paper a novel, rigorous framework for discussing contact orientations and their visualisation through histograms. This allows presenting, for the first time, in a unified way several existing visualisations and explaining how they are computed. We identify first some issues in how existing visualisations represent the main features of the contact distribution. We then exploit the framework to introduce new histogram types that avoid these issues and provide a better insight into the granular fabric. [ABSTRACT FROM AUTHOR]

In these days, when complex, IT-controlled systems have found their way into many areas, models and the data on which they are based are playing an increasingly important role. Due to the constantly growing possibilities of collecting data through sensor technology, extensive data sets are created that need to be mastered. In concrete terms, this means extracting the information required for a specific problem from the data in a high quality. For example, in the field of condition monitoring, this includes relevant system states. Especially in the application field of machine learning, the quality of the data is of significant importance. Here, different methods already exist to reduce the size of data sets without reducing the information value. In this paper, the multidimensional binned reduction (MdBR) method is presented as an approach that has a much lower complexity in comparison on the one hand and deals with regression, instead of classification as most other approaches do, on the other. The approach merges discretization approaches with non-parametric numerosity reduction via histograms. MdBR has linear complexity and can be facilitated to reduce large multivariate data sets to smaller subsets, which could be used for model training. The evaluation, based on a dataset from the photovoltaic sector with approximately 92 million samples, aims to train a multilayer perceptron (MLP) model to estimate the output power of the system. The results show that using the approach, the number of samples for training could be reduced by more than 99 % , while also increasing the model's performance. It works best with large data sets of low-dimensional data. Although periodic data often include the most redundant samples and thus provide the best reduction capabilities, the presented approach can only handle time-invariant data and not sequences of samples, as often done in time series. [ABSTRACT FROM AUTHOR]

Extraction of keyframes from the coronary angiogram video is an essential task in the medical community to analyze the image and proceed for further treatment. There are different medical modalities such as MRI, CT, X-ray, etc. to capture the images. This research focuses on coronary artery disease which is identified by the X-ray coronary angiogram test. The process of acquiring images in X-ray angiogram data is generally capturing the video at 15-30 frames per second. During the analysis of the coronary angiogram video the cardiologist often manually pauses the video at a regular interval frame to examine the artery. Such a frame which represents the vessels or artery with complete visual content is a keyframe. In this paper a method to extract the keyframe automatically from the angiogram video is proposed. This proposed method has three phases, In the first phase video is converted into frames. In the second phase frames are pre-processed to improve the quality of the frames. In the third phase, by comparing connected components properties of each frame, keyframes are extracted. This keyframe is used for further processes like segmentation and detection of the stenosis in the artery and the results of the proposed method is compared with the existing method. [ABSTRACT FROM AUTHOR]

The paper considers the main ways of describing the process that characterizes the arrival of packets to a multiservice node of a telecommunications network. The features of the process under consideration are best represented by the cumulative distribution function A(t). It determines the distribution of the interval size between the moments of arrival of neighboring packets to the multiservice node. These intervals are random values. If it is not possible to perform measurements that allow the choosing of the A(t) function, then the distribution law of random variables is selected based on reasonable assumptions. For telephone switching nodes, the Poisson flow hypothesis was used, which is often similar to the symmetric distribution of the number of calls at time interval t. The results of traffic measurements for multiservice switching nodes showed that the studied distribution is inherently asymmetric. This paper mainly considers the possibility of choosing the A(t) function based on the measurement results presented in a form of the histogram a(t), which contains a series of values. This histogram allows us to obtain the desired distribution as a stepwise function by integration of the a(t). Practical interest is associated with the possibility of reducing the number of readings used to assess the A(t) function. The methods used by some authors are based on the application of arbitrarily chosen functions A(t) with so-called heavy tails. The proposed approach is based on real distributions defined at a finite time interval. As a result of this research, a methodology has been developed to accurately describe the process of packet arrival at the input of the multiservice node. The proposed methodology is based on analytical methods. It guarantees error minimization when investigating the probabilistic characteristics of a switching node in a multiservice network. [ABSTRACT FROM AUTHOR]

BACKGROUND: The early detection of infected hydronephrosis is critical before lithotripsy. A feasible and noninvasive diagnostic method is of considerable clinical attention. OBJECTIVES: This retrospective study was performed to find some quantitative evaluation parameters of B-mode Gray-scale ultrasound histogram analysis that might assist the early diagnosis of infected hydronephrosis and test their diagnostic efficacy. MATERIALS AND METHODS: The ultrasound images and clinical data of 245 patients with hydronephrosis were retrospectively analyzed. Image J software was applied to obtain the gray-scale maps and the analysis results of the signal strength. The difference in the data between the infected and non-infected groups and the diagnostic value of the parameters were calculated. RESULTS: In this retrospective study, 70 patients with infected hydronephrosis and 175 patients with non-infected hydronephrosis were enrolled. The echogenicity of internal effusion and the echogenicity ratio of infected cases were significantly higher than those of non-infected cases (p < 0.05). The cutoff values were 23.82 (AUC = 0.859) of echogenicity of internal effusion, while 0.27 (AUC = 0.832) of echogenicity ratio. CONCLUSION: The quantitative evaluation of gray-scale ultrasound histogram is an objective and reliable method in differentiating infected from non-infected hydronephrosis. [ABSTRACT FROM AUTHOR]

Simple Summary: Metachronous metastases are the main factors affecting survival in rectal cancer, and 15–25% of patients will develop them at a 5-year follow-up. Early identification of patients with higher risk of developing distant metachronous metastases would help to improve therapeutic protocols and could allow for a more accurate, personalized management. Apparent diffusion coefficient (ADC) represents an MRI quantitative biomarker, which can assess the diffusion characteristics of tissues, depending on the microscopic mobility of water, showing information related to tissue cellularity. First-order histogram-based features statistics describe the frequency distribution of intensity values within a region of interest, revealing microstructural alterations. In our study, we demonstrated that whole-tumor ADC first-order features may provide useful information for the assessment of rectal cancer prognosis, regarding the occurrence of metachronous metastases. This study aims the ability of first-order histogram-based features, derived from ADC maps, to predict the occurrence of metachronous metastases (MM) in rectal cancer. A total of 52 patients with pathologically confirmed rectal adenocarcinoma were retrospectively enrolled and divided into two groups: patients who developed metachronous metastases (n = 15) and patients without metachronous metastases (n = 37). We extracted 17 first-order (FO) histogram-based features from the pretreatment ADC maps. Student's t-test and Mann–Whitney U test were used for the association between each FO feature and presence of MM. Statistically significant features were combined into a model, using the binary regression logistic method. The receiver operating curve analysis was used to determine the diagnostic performance of the individual parameters and combined model. There were significant differences in ADC 90th percentile, interquartile range, entropy, uniformity, variance, mean absolute deviation, and robust mean absolute deviation in patients with MM, as compared to those without MM (p values between 0.002–0.01). The best diagnostic was achieved by the 90th percentile and uniformity, yielding an AUC of 0.74 [95% CI: 0.60–0.8]). The combined model reached an AUC of 0.8 [95% CI: 0.66–0.90]. Our observations point out that ADC first-order features may be useful for predicting metachronous metastases in rectal cancer. [ABSTRACT FROM AUTHOR]

To explore the efficacy of the quantitative parameter histogram analysis of intravoxel incoherent motion (IVIM) for different molecular prognostic factors of breast cancer. A total of 72 patients with breast cancer who were confirmed by surgical pathology and underwent preoperative magnetic resonance imaging (MRI) were analyzed retrospectively. A region of interest (ROI) was drawn in each slice of the IVIM images. Whole-tumor histogram parameters were obtained with Firevoxel's software by accumulating all ROIs. Next, Kolmogorov-Smirnov test, Student's t- test, Mann-Whitney U test, receiver operating characteristic curve analysis and spearman rank correlation analysis were used to assess the relationship between histogram parameters and molecular prognostic factors of breast cancer. Among estrogen receptor (ER)-negative ROCs, the apparent diffusion coefficient (ADC) 10th percentile had the highest ROC of 0.792, with a cut-off value of 0.788 × 10−3 mm2/s, and sensitivity and specificity of 0.714 and 0.867, respectively. The negative correlation between lymph node metastasis status and ADC standard deviation was significant (ρ = −0.44, the correlation coefficients was represented by ρ). Positive correlations were observed between hormonal expression of ER and progesterone receptor (PR) with heterogeneity metrics of ADC or perfusion fraction (f), such as ADC inhomogeneity (ρ = 0.37, ρ = 0.29) and f skewness (ρ = 0.32, ρ = 0.28). Negative correlations were observed with numerical metrics, such as the ADC median (ρ = −0.31, ρ = −0.34) and f 45th percentile (ρ = −0.35, ρ = −0.28). The positive correlations between human epidermal receptor factor-2 (HER2) and pseudo-diffusivity (Dp) numerical metrics, Ki-67 expression, and heterogeneity metrics of Dp were high. The ADC 10th percentile had the largest area under the curve in the ER-negative ROC analysis, and the ADC standard deviation was the most valuable in the correlation analysis of lymph node metastasis. Whole-lesion quantitative histogram parameters of IVIM could, therefore, provide a scientific basis for radiomics to further guide clinical practice in the prognosis of breast cancer. [ABSTRACT FROM AUTHOR]