Histogram Method Research Articles

Multi-Method Diagnosis of Blood Microscopic Sample for Early Detection of Acute Lymphoblastic Leukemia Based on Deep Learning and Hybrid Techniques.

Leukemia is one of the most dangerous types of malignancies affecting the bone marrow or blood in all age groups, both in children and adults. The most dangerous and deadly type of leukemia is acute lymphoblastic leukemia (ALL). It is diagnosed by hematologists and experts in blood and bone marrow samples using a high-quality microscope with a magnifying lens. Manual diagnosis, however, is considered slow and is limited by the differing opinions of experts and other factors. Thus, this work aimed to develop diagnostic systems for two Acute Lymphoblastic Leukemia Image Databases (ALL_IDB1 and ALL_IDB2) for the early detection of leukemia. All images were optimized before being introduced to the systems by two overlapping filters: the average and Laplacian filters. This study consists of three proposed systems as follows: the first consists of the artificial neural network (ANN), feed forward neural network (FFNN), and support vector machine (SVM), all of which are based on hybrid features extracted using Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM) and Fuzzy Color Histogram (FCH) methods. Both ANN and FFNN reached an accuracy of 100%, while SVM reached an accuracy of 98.11%. The second proposed system consists of the convolutional neural network (CNN) models: AlexNet, GoogleNet, and ResNet-18, based on the transfer learning method, in which deep feature maps were extracted and classified with high accuracy. All the models obtained promising results for the early detection of leukemia in both datasets, with an accuracy of 100% for the AlexNet, GoogleNet, and ResNet-18 models. The third proposed system consists of hybrid CNN–SVM technologies, consisting of two blocks: CNN models for extracting feature maps and the SVM algorithm for classifying feature maps. All the hybrid systems achieved promising results, with AlexNet + SVM achieving 100% accuracy, Goog-LeNet + SVM achieving 98.1% accuracy, and ResNet-18 + SVM achieving 100% accuracy.

Revisit to Histogram Method for ADC Linearity Test: Examination of Input Signal and Ratio of Input and Sampling Frequencies

Revisit to Histogram Method for ADC Linearity Test: Examination of Input Signal and Ratio of Input and Sampling Frequencies

Calculating relative protein-ligand binding affinities with the accelerated weight histogram method: a benchmark study

To identify a lead compound, hundreds of thousands of drug candidates are synthesized and tested in high-throughput screening assays. The corresponding time and resources could be saved if a computational workflow combining docking and atomistic molecular dynamics (MD) simulations scanned the initial compound library and recommended a set of tens of potential lead compounds to be actually synthesized.

Lithology and fluid discrimination using combined seismic attributes with the constraint of rock physics: a case study from W field, South Sumatra Basin

Reservoir characterization and fluid discrimination based on seismic reflection amplitude play important roles in seismic exploration industry. Stable fluid-sensitive attributes from seismic data can help reduce uncertainties of hydrocarbon prediction in interwell locations and increase the reliability of drilling plans. In this study, in combination with seismic AVO inversion impedance and the rock physics template analysis, we proposed a new attribute (DI) to discriminate the hydrocarbon-associated anomalies and predict the reservoir parameters including porosity and water saturation quantitatively in Upper Gumai formation of W field, South Sumatra Basin. The new attribute (DI) is constructed using a set of combined impedances derived from prestack seismic inversion with the constraint of rock physics. Numerical modelling based on patchy-saturated model was implemented to test the sensitivity and stability of the new attribute and results showed that the DI attribute can predict the existence of hydrocarbon-filled sands with less ambiguity. With known well log data in W field, a feasibility study including cross-plotting and histogram methods was carried out and concluded that the DI attribute contributes higher resolution in distinguishing the hydrocarbon-filled sands from the background trend. In the process of quantitative interpretation, the DI attribute shows a good regression relationship with porosity and water saturation properties within the hydrocarbon sands with the correlation coefficients reaching 92% and 85%, respectively. By using seismic AVO inversion impedance, combining the application of the DI attribute and Vp/Vs ratio for lithology and fluid contents discrimination was conducted to improve the reservoir prediction accuracy in the field. Application results show that low values of the DI attribute (<18,000 m/s.g/cc) was indicative of hydrocarbon-filled sands effectively while Vp/Vs ratio presented a higher resolution in separating wet sand, dry-sand from shale. By using the linear regression relationship derived from cross-plotting analysis with well data, we calculated the volumes of porosity and water saturation from DI attribute and successfully screened out the hydrocarbon accumulation distribution, which reveals the potential zones of exploration interest in South Sumatra Basin.

Determining the maximal inscribed rectangle of an irregularly shaped stone using machine vision

ABSTRACT This study uses machine vision approaches to develop a rotation histogram method with better area utilization and shorter execution time that automatically determines the maximal inscribed rectangle of an irregularly shaped stone. This image processing procedure is designed to enhance the stone image, reduce the contour noise, determine the stone contour and the calculation range of the computational algorithm, and speed up the determination of the maximal inscribed rectangle of irregularly shaped stones. The rotation histogram method transforms to a histogram the stone image after-image processing, and then uses the stack method to determine the maximal rectangle of the histogram. Finally, the stone image is rotated to determine the maximal inscribed rectangle and angle of the irregularly shaped stone. The rotation histogram method is applicable to irregularly shaped stones with internal voids, and the area utilization of the stone will not be influenced by the placement orientation. The experimental results show that the rotation histogram method, as designed in this study, not only increases the area utilization for achieving the average value of 3.27%, but it also significantly shortens the execution time to achieve an average value of 64.83% compared with the existing traversal center diffusion method.

Influence of the Mode of Discrete Drum Speed and the Number of Inputs on the Technological Parameters of the Yarn Produced

This article is devoted to the research work aimed at improving the quality of yarns obtained by pneumomechanical spinning. The yarn quality indicators obtained at different speed modes of the pneumomechanical spinning machine discrete drum were studied and analyzed. The effect of the number of incisions of the sawtooth coatings on the discrete drum on the quality indicators of the yarn produced was also studied. The results of the experiments were analyzed by graphical and histogram methods, and alternative options were suggested.

Brain Image Classification Using Time Frequency Extraction with Histogram Intensity Similarity

Brain medical image classification is an essential procedure in Computer-Aided Diagnosis (CAD) systems. Conventional methods depend specifically on the local or global features. Several fusion methods have also been developed, most of which are problem-distinct and have shown to be highly favorable in medical images. However, intensity-specific images are not extracted. The recent deep learning methods ensure an efficient means to design an end-to-end model that produces final classification accuracy with brain medical images, compromising normalization. To solve these classification problems, in this paper, Histogram and Time-frequency Differential Deep (HTF-DD) method for medical image classification using Brain Magnetic Resonance Image (MRI) is presented. The construction of the proposed method involves the following steps. First, a deep Convolutional Neural Network (CNN) is trained as a pooled feature mapping in a supervised manner and the result that it obtains are standardized intensified pre-processed features for extraction. Second, a set of time-frequency features are extracted based on time signal and frequency signal of medical images to obtain time-frequency maps. Finally, an efficient model that is based on Differential Deep Learning is designed for obtaining different classes. The proposed model is evaluated using National Biomedical Imaging Archive (NBIA) images and validation of computational time, computational overhead and classification accuracy for varied Brain MRI has been done.

A review on smart attendance system with protection

Attendance is an important task in any institution. Manual methods generally waste a lot of time. They are also not the safest and fastest option. In schools and colleges, the most common technique for attendance is calling the students' names, and in industries, the most common method is signing a register which generally needs extra time to do the attendance. A "Smart Attendance System with Protection" is proposed to solve these problems. This project is mainly divided into 3 parts according to its applications which includes, attendance, thermal screening and ultrasonic sensors. For the automatic attendance part, the Local Binary Pattern Histogram (LBPH) method is used for the face recognition of the person. LBPH is considered the most useful technique for face recognition. For the second part of the project, thermal screening, a sensor (MLX90614) is used to take the temperature of the person and check if it is normal or not. In the third part of the project, ultrasonic sensors are used to detect the hands of the person. Under the sanitization section, and after detection, a small pump motor is used to pour the sanitizer into the hands of the person for the sanitization of their hands. Manual records can be easily extracted from the Smart Attendance System when needed. Attendance record manipulation is not possible in the system. The proposed device uses the Raspberry Pi, IRT, Ultrasonic Sensor, Pi Camera, and Pump Motor. An attendance record is generated and stored in excel format.

A Novel Image Thresholding Method Combining Entropy With Parzen Window Estimation

Abstract Image thresholding is an important and efficient image segmentation technique, which is crucial and essential for image analysis and computer vision. In this paper, we proposed a new image thresholding method based on entropy and parzen window (PW) estimation. First, the probability of each gray-level distribution is approximate by using the PW estimation. Second, by combining the obtained probability information with entropic information of the foreground and background, a new objective function is created. At last, the ideal threshold value is obtained by optimizing the objective function. By comparing with some classical thresholding methods, such as inter class variance method (OTSU), minimum error thresholding method (MET), Kapur’s entropy based method (KAPUR) and the recent methods that take spatial information into consideration (2D-D histogram method, GLLV histogram method and Gabor histogram method), the proposed method, experiment on 10 images (one synthetic image, four nondestructive testing images and five real-world images), presents a better performance on the accuracy, robustness and visual effect of segmentation.

Methods of Estimating the Form of the Probability Distribution Density in Tasks of Processing Measurement Results

Issues associated with methods for estimating the shape of the probability distribution density curve are analyzed in order to classify them when processing measurement results. For example, such nonparametric methods as the method of histograms and frequency polygon, as well as the method of classification of distributions, are considered. It is shown that the values of the anticurtosis and entropy coefficient can be taken as independent features of the form of symmetric distributions. For probability distribution densities that have a one-sided character, such as multiplicative noise, a skewness coefficient should be added to the parameters to consider. Recurrent procedures for obtaining current estimates of numerical characteristics of analyzed random processes are given. The results of processing a random process based on recurrent procedures are presented. It is shown that when the number of samples increases, the estimates obtained by using recurrent and non-recurrent procedures converge. The scattering of estimates of probability distribution density parameters, such as variance, relative mean square error, and entropy error, is determined.

Model of Diagnosing and Searching for Incompatibilities in Aluminium Castings.

An essential element of any industry is castings, which is determined by the technical conditions for their reception. However, conducting production in the foundry technology is burdened with many difficulties associated with, for example, the inability to control all of the parameters that may affect the casting quality. Therefore, it is essential to undertake improvement actions in this area. Efforts are being made to use non-destructive testing (NDT) as a part of quality control, but these methods are rarely combined in a single diagnostic run. As a part of quality improvement, it is also essential to identify the root cause of the problem. For this reason, it is justified to develop a model of diagnosing and searching for non-conformities, which would combine NDT tests and quality management tools. The model included the visual, ultrasound, and eddy current examination in the diagnostic part, and the Pareto–Lorenz diagram correlated with ABC method, histogram, and 5WHY method (asking five questions why). The study’s originality is manifested in the combination of several NDT methods with quality management methods in one model. Using integrally configured methods in the proposed model, it was possible to: reduce diagnostic uncertainty, characterize the critical group of non-conformities, and identify the root causes of the quality problem. The model is a new and universal method that can be implied in any foundry company in order to ensure the stability of the production processes. The application of the model contributes to an increase in the detection speed and enables the reduction of non-conformities in aluminium castings, thus increasing the quality level of the offered products.

Pengenalan Wajah dengan Menggunakan Metode Local Binary Patterns Histograms (LBPH)

Rapid technological developments in the world have had a major impact on various fields, i.e. administration and data collection. One of the data collection systems that is often used is attendance. The attendance system initially only used an attendance sheet that was filled out manually, but there were shortcomings i.e. the possibility of forging signatures during attendance. Therefore, other methods are needed to overcome these problems. In this study, we propose to use facial features for attendance. The data used are 750 facial images consisting of 5 people with each person having 150 facial images with various expressions. The data is divided into two, namely 500 images as training data and 250 images as test data. The next stage is to find a facial features, we propose to use the Local Binary Patterns Histograms (LBPH) method. LBPH is a combination of the Local Binary Patterns (LBP) method with Histograms of Oriented Gradients (HOG). After that, we perform face recognition based on the features that have been obtained. Based on the research results obtained an accuracy rate of 86%

PENGENALAN JENIS MAKER DENGAN METODE COLOR HISTOGRAM DAN EUCLIDEAN DISTANCE

In the midst of the covid-19 virus pandemic the use of masks is increasing to prevent transmission, various types of masks are available among the public but not all types of masks are advised to avoid the transmission of the covid-19 virus. Among them the use of KN-95 masks is recommended to avoid transmission of the virus. This application program uses color histogram and euclidean distance methods to distinguish the types of medical and non-medical masks, the medical masks used in this application program are kn-95 masks that are white. Color histogram is a way to get color dissemination data in photos or images. While euclidean distance is used to get the distance difference between 2 points. A total of 97 photos of medical masks were used as training data, 59 photos of medical masks and 96 photos of non-medical masks were used as test data. The application program recognizes 38 medical masks and 49 non-medical masks, the success rate of this program for recognizing medical and non-medical masks is 58.27%.

Research and application on corn crop identification and positioning method based on Machine vision

Weeds that grow among crops are undesirable plants and have adversely affected crop growth and yield. Therefore, the study explores corn identification and positioning methods based on machine vision. The ultra-green feature algorithm and maximum between-class variance method (OTSU) were used to segment maize corn, weeds, and land; the segmentation effect was significant and can meet the following shape feature extraction requirements. Finally, the identification and positioning of corn were achieved by morphological reconstruction and pixel projection histogram method. The experiment reveals that when a weeding robot travels at a speed of 1.6 km/h, the recognition accuracy can reach 94.1%. The technique used in this study is accessible for normal cases and can make a good recognition effect; the accuracy and real-time requirements of robot recognition are improved and reduced the calculation time.

Role of Endoscopic Ultrasound Elastography Strain Histograms in the Evaluation of Patients with Pancreatic Masses.

The Endoscopic Ultrasound (EUS) quantitative elastography strain ratio (SR) and strain histogram (SH) methods for noninvasive pancreatic masses differentiation have been recently developed. The aim of this research was to investigate the accuracy of the diagnostic differentiation methods for patients with pancreatic masses, based on the EUS SR and SH. This is a prospective study involving 100 cases with pancreatic masses. Patients were classified into 2 groups: group that was diagnosed with pancreatic malignancy with positive histopathology by biopsy obtained by fine-needle aspiration or postoperative pathology (72 patients) and the group diagnosed with pancreatitis with negative pathology and follow-up for at least 1 year (28 patients). Based on the ROC curve, the cut-off point for Mode 1 was set at 97. Values under it showed the presence of malignant pancreatic masses. Mode 1 achieved a sensitivity of 89% and a specificity of 43% with an overall accuracy of 76%. The predictive positive value was 70%, and the predictive negative value was 60%. The cut-off point for SR was set at 3.04, and the values were equal or above the suggested pancreatic malignancy. The SR achieved a sensitivity of 95.83%, a specificity of 61%, with an overall accuracy of 86%. The predictive positive and negative values were 86.2% and 85%, respectively. Mode 1 SH showed good sensitivity in the identification of pancreatic malignant tumors but were disappointingly of low specificity. Higher sensitivity, specificity, and overall accuracy were obtained by using the SR.

Tomato Leaf Diseases Classification Based on Leaf Images: A Comparison between Classical Machine Learning and Deep Learning Methods

Tomato production can be greatly reduced due to various diseases, such as bacterial spot, early blight, and leaf mold. Rapid recognition and timely treatment of diseases can minimize tomato production loss. Nowadays, a large number of researchers (including different institutes, laboratories, and universities) have developed and examined various traditional machine learning (ML) and deep learning (DL) algorithms for plant disease classification. However, through pass survey analysis, we found that there are no studies comparing the classification performance of ML and DL for the tomato disease classification problem. The performance and outcomes of different traditional ML and DL (a subset of ML) methods may vary depending on the datasets used and the tasks to be solved. This study generally aimed to identify the most suitable ML/DL models for the PlantVillage tomato dataset and the tomato disease classification problem. For machine learning algorithm implementation, we used different methods to extract disease features manually. In our study, we extracted a total of 52 texture features using local binary pattern (LBP) and gray level co-occurrence matrix (GLCM) methods and 105 color features using color moment and color histogram methods. Among all the feature extraction methods, the COLOR+GLCM method obtained the best result. By comparing the different methods, we found that the metrics (accuracy, precision, recall, F1 score) of the tested deep learning networks (AlexNet, VGG16, ResNet34, EfficientNet-b0, and MobileNetV2) were all better than those of the measured machine learning algorithms (support vector machine (SVM), k-nearest neighbor (kNN), and random forest (RF)). Furthermore, we found that, for our dataset and classification task, among the tested ML/DL algorithms, the ResNet34 network obtained the best results, with accuracy of 99.7%, precision of 99.6%, recall of 99.7%, and F1 score of 99.7%.

Automated scoring of CerbB2/HER2 receptors using histogram based analysis of immunohistochemistry breast cancer tissue images

Background and Objective: Visual expression of invasive breast cancer with immunohistochemistry (IHC) allows evaluation of CerbB2 receptors, such that CerbB2 mutated breast carcinomas are suitable for targeted therapy. Breast tumors are evaluated in four different scores as 0, 1, 2, 3 to decide if it is suitable for the CerbB2 protein specific treatment or not. Pathologists try to decide the scores by eye, which is laborious, and error-prone work with high inter-observer variability.Methods: We propose cell based image analysis termine the CerbB2/HER2 scores in breast tissue images in accordance with ASCO/CAP recommendations, automatically. The proposed ASCO/CAP recommendations compliant image analysis approach provides an explainable artificial intelligence solution for HER2 tissue scoring. Firstly, tissue images are separated into hematoxylin and diaminobenzidine color channels with color deconvolution. Cell nuclei and boundaries are segmented with a hybrid multi-level thresholding and radial line based method on hematoxylin channel. Following ASCO/CAP recommendations, cell based features representing the intensity and completeness of circumferential membrane staining are extracted with the proposed Membrane Intensity Histogram (MIH) method. Extracted features are, then, fed into a classifier, such as, k-nearest neighbours, decision trees and long-short term memory, to determine cell based HER2 scores. Individual cell scores are combined according to ASCO/CAP recommendations to obtain the final CerbB2/HER2 tissue score. Another contribution of the paper is the introduction of two publicly available image data sets on CerbB2/HER2 tissue scoring. Clinical data sets, ITU-MED-1 and ITU-MED-2, are created by digitizing IHC slides from real patients, that have ground truth CerbB2/HER2 scores.Result: The proposed automatic scoring method is tested on these clinical data sets, as well as, on a HER2 Contest data set. Performance of the proposed explainable artificial intelligence approach for HER2 tissue scoring is evaluated and compared with state-of-the-art techniques in the literature.Conclusion: Results suggest that, the proposed method is highly effective in HER2 tissue scoring on both balanced and unbalanced data sets.Significance: A hand-crafted feature extraction approach for CerbB2/HER2 scoring is proposed which provides an explainable artificial intelligence framework. The proposed HER2 scoring method can be adapted to updates in ASCO/CAP recommendations without the need for re-training and/or re-designing the model. Moreover, two publicly available data sets, namely, ITU-MED-1 and ITU-MED-2 are introduced with corresponding score labels.

Thai Sign Language Image Recognition for the Hearing-Impaired using Radial Inverse Force Histogram combined with Max-Min Boundary

Sign language image recognition is also a very interesting research topic. Because it can be applied to help normal people understand and use it as a communication tool for the hearing impaired. The objectives of this research were to: 1) study and analyze Thai Sign Language image recognition image data for hearing impaired, 2) develop Thai Sign Language image recognition system for hearing impaired by using new techniques, and 3) measure the efficiency of Thai sign language image recognition for hearing impaired using Radial Inverse Force Histogram and the Maximum and Minimum boundary values. The results of the research were as follows: 1) The study and analysis of image data of Thai Sign Language Image Recognition In this research, 62,694 sign language images were used, divided into 2 parts: 1) American Sign Language images, which consisted of 36 groups of images, namely 26 groups of letters (AZ) and 10 groups of numbers (0-9), and Part 2) Picture of Thai Sign Language consisting of 61 groups of images, including 44 groups of letters (ก-ฮ), 7 groups of vowels and 10 groups of numbers (0-9). Each group of pictures is rotated, enlarged, and Image promotion There were 6 sub-groups of images in various forms, divided into 2 parts: 70% of the images for training and 30% of the images for testing. 3) The results of measuring the efficiency of image recognition. It is divided into two parts: American Sign Language Image Recognition and Thai Sign Language Image Recognition. Compared with the Angular histogram method, the mean image accuracy was 0.86, the recall of the mean American sign language was 0.91, and the accuracy of the Thai Sign Language was 0.78. The recognition performance for Thai Sign Language images averaged 0.89, while the recognition efficiency was achieved when using radial inverse force histograms in combination with image similarity measurements with maximum-minimum boundary values. Accuracy for Mean American Sign Language was 0.99 and Remembrance for Mean American Sign was 1.00, while Accuracy for Mean Thai Sign Language was 0.89. Mean Remembrance for Thai Sign Language was 0.96. The results of the visual recognition performance measurement of both the American Sign Language and the Thai Sign Language images were very good compared to the Angular Histogram method.

Perbandingan Metode KNN Dan LBPH Pada Klasifikasi Daun Herbal

Herbal plants are plants that can be used as alternatives in natural healing of diseases, parts of plants that can be used such as roots, stems, tubers and leaves, in Southeast Sulawesi there are currently 1000 herbal plants and 10 sub-ethnicities that have been inventoried, according to research conducted by the Ministry of Health (Kemenkes). Indonesia has 6,000 - 7,000 medicinal plants, Southeast Sulawesi Province has a variety of herbal plants that are not found in other areas, such as Komba - Komba or Balakacida (Chromolaena Odorata). However, in the present era, the number of herbal plants is not accompanied by the knowledge of the community about the herbal plants themselves. The purpose of this study is to classify herbal plants and to compare the performance results of the K-Nearest Neighbor Method and Local Binary Pattern Histogram. From the test results of five types of herbal leaves in Southeast Sulawesi with a total of 100 data sets, the accuracy value for the K-Nearest Neighbor (KNN) method is obtained total accuracy value is 97,5%, while for the Local Binary Pattern Histogram (LBPH) method the total value is 94% of total accuracy value.

Image Segmentation Using the EM/MPM Algorithm

In this article we propose to place our work in a Markovian framework for unsupervised image segmentation. We give one of the procedures for estimating the parameters of a Markov field, we limit the work to the EM estimation method and the Posterior Marginal Maximization (MPM) segmentation method. Estimating the number of regions who compones the image is relatively difficult, we try to solve this problem by the K-means Histogram method.