Grayscale Research Articles

Novel method of image analysis that combines Radon and Fourier image transformations for the purpose of differentiating between malignant and benign colonic biopsies.

Colorectal cancer is the third most common type of cancer. It develops slowly as a polyp that can turn into a cancerous tumor. This study aimed to develop a decision-making algorithm of microscopic images using texture analysis that is orientation free, to be used for automated classification of normal and neoplastic (malignant or premalignant) colonic biopsies. Forty-nine colonic adenocarcinomas, 41 adenomas and a control group of adjacent normal colonic mucosa were included in the texture analysis. Radon transform followed by the Fast Fourier transform were applied to the images. Subsequently, the gray level co-occurrence matrix (GLCM) transform was applied allowing the extraction of four textural variables (homogeneity, contrast, correlation, and entropy). For classification and prediction of the diagnosis, a statistical multivariate regression model and a neural network (NNET) model were used and compared. The statistical model provided a sensitivity of 71.3% and a specificity of 50% (Area under the ROC curve: 0.67) for classifying the neoplastic and the normal images, respectively. The NNET model was superior to the statistical model and produced a sensitivity of 97.9% and specificity of 88% (Area under the ROC curve: 0.92). To our knowledge, this is the first study that used a combination of Radon, FFT, and GLCM transformations in order to overcome the tissue orientation problem in texture analysis of microscopic images of colonic biopsies. The NNET classifier trained by the extracted textural features proved to be superior to the statistical classifier, thus predicting colonic neoplasia with high accuracy. RESEARCH HIGHLIGHTS: We propose a novel decision-making algorithm of orientation invariant image texture analysis, fast and easily implemented for automated differentiation between benign and neoplastic epithelial tumors of the colon. This method can reduce the turnaround time allowing to prioritize the biopsies during their examination and diagnosis by the pathologist.

Improving acute stroke assessment in non-enhanced computed tomography: automated tool for early ischemic lesion volume detection.

ASPECTs is a widely used marker to identify early stroke signs on non-enhanced computed tomography (NECT), yet it presents interindividual variability and it may be hard to use for non-experts. We introduce an algorithm capable of automatically estimating the NECT volumetric extension of early acute ischemic changes in the 3D space. We compared the power of this marker with ASPECTs evaluated by experienced practitioner in predicting the clinical outcome. We analyzed and processed neuroimaging data of 153 patients admitted with acute ischemic stroke. All patients underwent a NECT at admission and on follow-up. The developed algorithm identifies the early ischemic hypodense region based on an automatic comparison of the gray level in the images of the two hemispheres, assumed to be an approximate mirror image of each other in healthy patients. In the two standard axial slices used to estimate the ASPECTs, the regions identified by the algorithm overlap significantly with those identified by experienced practitioners. However, in many patients, the regions identified automatically extend significantly to other slices. In these cases,the volume marker provides supplementary and independent information.Indeed,theclinical outcome of patients with volume marker = 0 can be distinguished with higher statistical confidence than the outcome of patients with ASPECTs = 10. The volumetric extension and the location of acute ischemic region in the 3D-space, automatically identified by our algorithm, provide data that are mostly in agreement with the ASPECTs value estimated by expert practitioners, and in some cases complementary and independent.

Black and White Image Colorization with Deep Learning

Abstract: The project "Black and White Image Colorization with Deep Learning" develops a system to automatically infuse colors into grayscale images through cutting-edge deep learning approaches. Utilizing convolutional neural networks (CNNs) and generative adversarial networks (GANs), the system aims to deliver both realistic and visually pleasing colorization outcomes. This process includes training the deep learning models on diverse image datasets, optimizing the model parameters, and using advanced loss functions to ensure precise color representation.This project addresses the complex challenges associated with adding colors to monochrome images and also seeks to improve user experience by introducing customizable options such as style modification and color tweaks. By merging deep learning with computer vision techniques, this initiative is poised to contribute significantly to various image processing fields, especially in the restoration of historical images and the enhancement of visual content production. With thorough experimentation and ongoing refinements, the project aspires to establish a reliable and easy-to-use system that advances the state of image colorization technology in the modern digital age..

Design of A Cataract Detection System based on The Convolutional Neural Network

Cataract, a condition characterized by clouding of the eye's lens, leads to decreased vision and potentially blindness. In Indonesia, it is the predominant cause of blindness, accounting for 81.2% of cases. Given the rising life expectancy, the incidence of degenerative diseases like cataracts is expected to increase. This research aims to develop a cataract detection system capable of classifying eye images as either indicative of cataracts or normal. Utilizing Convolutional Neural Networks (CNN) and RGB-based image processing—including edge detection techniques such as Canny and Prewitt—the system identifies eye contours. This facilitates image segmentation to ascertain the eye's condition. Therefore, image collection and processing models play a crucial role in this study. The research findings indicate that the system functions effectively, with a 98% success rate in accurately processing normal eye images through the CNN model without detecting cataracts. When tested using grayscale imaging, cataract-afflicted eyes—characterized by red spots in the images—were also successfully identified by the CNN model. These test results demonstrate that the designed cataract detection system can accurately classify images into normal or cataract-afflicted eyes with high precision. This system shows promise for use in early cataract detection, potentially helping to reduce the prevalence of cataract-related blindness in Indonesia.

An approach for encrypting images using a four-dimensional logistic map

Encrypting image transmission has been one of the most complex challenges with communication technology ever. Millions of people utilize and share photographs via the internet for personal and professional purposes. One approach to secure picture transfer over the network is to use encryption techniques to transform the original picture into an unreadable or garbled version. Several novel and encouraging possibilities for creating secure Image data encryption techniques are presented by cryptographic algorithms grounded in chaotic logistic theory. Developing a foolproof method for encrypting both black-and-white and color image is the primary focus of this work. Both grayscale and color images can be encrypted using the keys generated by combining the chaotic logistic with the image's density in the first stage of the proposed system's encryption process. The original image can then be recovered in the second stage, which is the inverse of the encryption process. Two publicly available standard grayscale and color photographs were analyzed to evaluate the suggested approach. Peak signal-to-noise ratio (PSNR), unified average changing intensity (UACI), and number of pixels change rate (NPCR) were found to be 6.6268, 51.3011, and 100, respectively, according to the test findings.

Implementation of Adaptive Coding Approach Based on Periodic Walsh Piecewise-Linear Transform for Digital Grayscale Image

Implementation of Adaptive Coding Approach Based on Periodic Walsh Piecewise-Linear Transform for Digital Grayscale Image

Experimental study on immiscible and miscible dynamic characteristics of CO2 and crude oil in visual slim tube

CO2 flooding for oil recovery is a dynamic process that requires further investigation of oil-gas interface change characteristics, interfacial mass transfer processes, and oil-gas composition variation during both immiscible and miscible displacement. Understanding these factors is crucial for better comprehending their impact on CO2-enhanced oil recovery (EOR). This research used a jointly developed CO2 miscible visual flooding experimental apparatus to study the horizontal dynamic characteristics of CO2 and crude oil under different pressures and flow rates in visual slim tube. At 10 MPa, the stratification results of CO2 and crude oil indicate that the experiment is immiscible flooding. The contact angle (7.9°) between the two phases of CO2 and crude oil at the flow rate of 15 cm/min is larger than that (5.2°) at 1.5 cm/min, and the grey scale of CO2 increases at 100 cm/min. The quantity, individual content, and shape of the light and medium hydrocarbon components condensed on the inner wall of the tube vary with different flow rates. At 15 MPa, the appearance of the CO2 and crude oil transition interval proves that the experiment is miscible flooding. At different flow rates, the inclination angle and distribution of black stripes vary. The whole transition interval is divided into 6 intervals, and the transition interval lengthens with increasing fluid velocity. The experiments visually demonstrate the occurrence of the miscible phase, and identify experimental pressure and fluid flow rate as key factors influencing the miscibility of CO2 and crude oil.

Automatic segmentation of spine x-ray images based on multiscale feature enhancement network.

Automatic segmentation of vertebrae in spinal x-ray images is crucial for clinical diagnosis, case analysis, and surgical planning of spinal lesions. However, due to the inherent characteristics of x-ray images, including low contrast, high noise, and uneven grey scale, it remains a critical and challenging problem in computer-aided spine image analysis and disease diagnosis applications. In this paper, a Multiscale Feature Enhancement Network (MFENet), is proposed for segmenting whole spinal x-ray images, to aid doctors in diagnosing spinal-related diseases. To enhance feature extraction, the network incorporates a Dual-branch Feature Extraction Module (DFEM) and a Semantic Aggregation Module (SAM). The DFEM has a parallel dual-branch structure. The upper branch utilizes multiscale convolutional kernels to extract features from images. Employing convolutional kernels of different sizes helps capture details and structural information at different scales. The lower branch incorporates attention mechanisms to further optimize feature representation. By modeling the feature maps spatially and across channels, the network becomes more focused on key feature regions and suppresses task-irrelevant information. The SAM leverages contextual semantic information to compensate for details lost during pooling and convolution operations. It integrates high-level feature information from different scales to reduce segmentation result discontinuity. In addition, a hybrid loss function is employed to enhance the network's feature extraction capability. In this study, we conducted a multitude of experiments utilizing dataset provided by the Spine Surgery Department of Henan Provincial People's Hospital. The experimental results indicate that our proposed MFENet demonstrates superior segmentation performance in spinal segmentation on x-ray images compared to other advanced methods, achieving 92.61±0.431 for MIoU, 92.42±0.329 for DSC, and 99.51±0.037 for Global_accuracy. Our model is able to more effectively learn and extract global contextual semantic information, significantly improving spinal segmentation performance, further aiding doctors in analyzing patient conditions.

Application of K-NN algorithm using gray level co-occurrence matrix for mango fruit classification cased on leaf image

Application of K-NN algorithm using gray level co-occurrence matrix for mango fruit classification cased on leaf image

APW: An ensemble model for efficient wheat spike counting in unmanned aerial vehicle images

The accurate estimation of wheat ear is of great significance to gain high yield for ensuring food security. The use of computer vision for wheat ear counting to improve the estimation efficiency and accuracy has been explored by many scholars. However, existing single-computer vision recognition methods have insufficient robustness and recognition of adherent wheat ears. This study mainly focused on to solve the problem of adherent wheat ears, and proposed a combined algorithm called “APW” that ensemble with alternating direction methods of multipliers (ADMM), Potts algorithm and Watershed to achieve fast recognition and counting of wheat ears. The images of wheat ears were collected under three different scenarios including growth stages, planting densities, and flight heights by using unmanned aerial vehicle (UAV), and the Potts model was used to recognise the images. The masked images of the wheat ear were converted into a greyscale image, and were layered using a greyscale gradient. Finally, the watershed algorithm was used to segment adherent wheat ears in the layered image, and the centroids were labelled and counted. The results shows that the best accuracy was obtained under low planting density scenario, with an R2 of 0.89 and a root mean square error (RMSE) of 3.72, suggesting that APW can handle scenarios with large background differences and low target adherence. These findings provides a new approach for efficient counting of wheat ears in UAV acquired images.

Investigation into Spatial and Temporal Differences in Carbon Emissions and Driving Factors in the Pearl River Delta: The Perspective of Urbanization

Urbanization is a significant indicator of city progress, and as urbanization advances, carbon emissions exhibit an increasing trend that must not be disregarded. Therefore, it is imperative to thoroughly examine the spatial and temporal variations as well as the factors influencing carbon emissions during the urbanization process. In this paper, based on the 2009–2019 PRD region, carbon emissions are measured from energy consumption, industrial production process, solid waste, and wastewater according to the IPCC coefficients, and spatial and spatial differences in carbon emissions are combined with spatial analysis and the drivers analyzed using the gray correlation scale. The results show that: (1) The total carbon emissions in the PRD region have been increasing over the study period, and the distribution of total carbon emissions shows a pattern of “strong in the east and weak in the west”, with energy consumption accounting for the highest proportion of carbon emissions. (2) The global Moran Index of carbon emissions in the PRD has been decreasing, with low and low clustering concentrated in Shaoguan and Zhaoqing, high and high clustering concentrated in Dongguan and Shenzhen, and low and high clustering concentrated in Shenzhen and Guangzhou, with cold spots mainly concentrated in Zhaoqing and hot spots mainly distributed in Guangzhou, Shenzhen, and Dongguan. (3) The degree of economic growth has a substantial influence on carbon emissions in the PRD cities, and the influence of technical advancement has intensified. Guangzhou City is propelled by low-carbon regulations that have a more equitable influence on all elements. Zhuhai City has a more significant influence on energy intensity, while Foshan City has a more noticeable decrease in the effect of foreign investment. To address the issue of carbon emissions, the government should establish appropriate rules to regulate carbon emissions in areas with high emissions, foster collaborative efforts across cities, and encourage the growth of environmentally friendly enterprises.

Tomato Plant Disease Identification via Deep Learning Technique

Tomatoes are edible berries that are utilized extensively around the world. Their scientific name is solanum lycopersicum. Many tomato varietals are primarily produced in temperate climates. Even though tomatoes are used as ingredients in food, they are botanically categorized as berries. Bacterial spot, Black mold, Gray spot, Late blight, and powdery mildew (PM) are the primary diseases that affect tomato plants. The most challenging process in this decade is also the early diagnosis of these disorders. In this study, the suggested method is applied to the assessment of disease severity and disease diagnosis in tomato plant images. There are various stages to the procedure. The tomato plant image is initially pre-processed using the Wavelet technique, and then the modified Watershed algorithm is used to segment the image using improved Double Sigmoid and Erosion (WSA-IDS&E). The segmented image is then processed in the feature extraction stage in order to extract features. The Gray Level Co-occurrence Matrix (GLCM), histogram, illness area, and pixel-based retrieved characteristics are fed into the CNN model, a deep learning method for identifying diseases. In addition, a novel training method, WUDHOA, which adjusts the CNN’s weight, is introduced in this study to improve the performance of deep learning techniques. In addition, the severity of the tomato plant illness is measured in order to improve disease identification outcomes, where this severity assessment is taken into account using a fresh evaluation. Finally, the performance of the detection outcome is compared with other existing works to prove its efficiency. The conventional techniques have the lowest specificity ratings, such as [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], whereas the WUDHOA achieved a specificity of 0.9384.

Amyloid beta is associated with carotid wall echolucency and atherosclerotic plaque composition

Circulating amyloid-beta 1–40 (Αb40) has pro-atherogenic properties and could serve as a biomarker in atherosclerotic cardiovascular disease (ASCVD). However, the association of Ab40 levels with morphological characteristics reflecting atherosclerotic plaque echolucency and composition is not available. Carotid atherosclerosis was assessed in consecutively recruited individuals without ASCVD (n = 342) by ultrasonography. The primary endpoint was grey scale median (GSM) of intima-media complex (IMC) and plaques, analysed using dedicated software. Vascular markers were assessed at two time-points (median follow-up 35.5 months). In n = 56 patients undergoing carotid endarterectomy, histological plaque features were analysed. Plasma Αb40 levels were measured at baseline. Ab40 was associated with lower IMC GSM and plaque GSM and higher plaque area at baseline after multivariable adjustment. Increased Ab40 levels were also longitudinally associated with decreasing or persistently low IMC and plaque GSM after multivariable adjustment (p < 0.05). In the histological analysis, Ab40 levels were associated with lower incidence of calcified plaques and plaques without high-risk features. Ab40 levels are associated with ultrasonographic and histological markers of carotid wall composition both in the non-stenotic arterial wall and in severely stenotic plaques. These findings support experimental evidence linking Ab40 with plaque vulnerability, possibly mediating its established association with major adverse cardiovascular events.

Assessment of pulmonary function in COPD patients using dynamic digital radiography: A novel approach utilizing lung signal intensity changes during forced breathing

ObjectivesTo investigate the association of lung signal intensity changes during forced breathing using dynamic digital radiography (DDR) with pulmonary function and disease severity in patients with chronic obstructive pulmonary disease (COPD). MethodsThis retrospective study included 46 healthy subjects and 33 COPD patients who underwent posteroanterior chest DDR examination. We collected raw signal intensity and gray-scale image data. The lung contour was extracted on the gray-scale images using our previously developed automated lung field tracking system and calculated the average of signal intensity values within the extracted lung contour on gray-scale images. Lung signal intensity changes were quantified as SImax/SImin, representing the maximum ratio of the average signal intensity in the inspiratory phase to that in the expiratory phase. We investigated the correlation between SImax/SImin and pulmonary function parameters, and differences in SImax/SImin by disease severity. ResultsSImax/SImin showed the highest correlation with VC (rs = 0.54, P < 0.0001), followed by FEV1 (rs = 0.44, P < 0.0001), both of which are key indicators of COPD pathophysiology. In a multivariate linear regression analysis adjusted for confounding factors, SImax/SImin was significantly lower in the severe COPD group compared to the normal group (P = 0.0004) and mild COPD group (P=0.0022), suggesting its potential usefulness in assessing COPD severity. ConclusionThis study suggests that the signal intensity changes of lung fields during forced breathing using DDR reflect the pathophysiology of COPD and can be a useful index in assessing pulmonary function in COPD patients, potentially improving COPD diagnosis and management.

Pengolahan Citra Digital EKG Rumah Sakit Tk.IV Samarinda

In the hospital itself, especially in the MCU Poly Radiology Installation, it is a unit that performs services such as Medical Check Up, making health certificates, and carrying out routine checks at an institution. ECG examination is one of the services most frequently performed routinely by patients. Examinations carried out include medical check-ups, monitoring of long-term therapy, patient to assessment before surgery, individual screening for high-risk jobs, examinations before participating in sporting events, and overall examinations as a condition of employment. This study carried out the process of withdrawing information or object descriptions or identifying objects contained in images by compressing or reducing image data. By using EKG data from TK.IV Samarinda Hospital by carrying out processing stages such as converting analog data to digital, cropping, and applying filters to EKG images. After the filter is applied to the image, it is found that after 5 types of image processing (RGB Image, Grayscale Image, Binary Image, Thresholding Image, and Edge Detection Image) on the results of the ECG signal it can be concluded that the processed signal retains the features of the existing signal and information. . Of the five types of processing methods, the clearest result is conversion to Edge Detection imagery.

GLCM-Based Feature Extraction for Alpha Matting on Natural Images

The main objective of this research is to determine the optimal threshold value in the unknown region in the alpha-matting operation of natural images. Alpha-mating serves to draw matte from the image used in segmentation. The alpha value is very influential on the quality of segmentation which is determined by the level of threshold value accuracy. The determination of the threshold begins by breaking the grayscale image into several sub-images using Region of Interest (RoI). Each sub-image was extracted using the Gray Level Co-occurrence Matrix (GLCM) considered by the parameters of contrast, energy, and entropy at angles of 0°, 45°, 90°, and 135 °. Each feature results in extractions, which are then averaged and normalized in each sub-image. The value is determined as the local threshold value used in the alpha matting operation. Experiments were carried out on 12 natural images from the image-mating dataset to evaluate the performance of the proposed algorithm. The increase in accuracy shows up to 63% by the measurements of experiments, compared to the calculation of adaptive threshold by using the fuzzy CMs Algorithm.

Transforming Color: A Novel Image Colorization Method

This paper introduces a novel method for image colorization that utilizes a color transformer and generative adversarial networks (GANs) to address the challenge of generating visually appealing colorized images. Conventional approaches often struggle with capturing long-range dependencies and producing realistic colorizations. The proposed method integrates a transformer architecture to capture global information and a GAN framework to improve visual quality. In this study, a color encoder that utilizes a random normal distribution to generate color features is applied. These features are then integrated with grayscale image features to enhance the overall representation of the images. Our method demonstrates superior performance compared with existing approaches by utilizing the capacity of the transformer, which can capture long-range dependencies and generate a realistic colorization of the GAN. Experimental results show that the proposed network significantly outperforms other state-of-the-art colorization techniques, highlighting its potential for image colorization. This research opens new possibilities for precise and visually compelling image colorization in domains such as digital restoration and historical image analysis.

Indexing Topological Numbers on Images by Transferring Chiral Magnetic Textures

AbstractTopological analysis is widely adopted in various research fields to unveil intricate features and structural relationships implied in geometrical objects. Especially, in the fields of data analysis, exploring the topological properties of various images offers rich insights into the intrinsic geometrical information within them. In this study, a novel approach is proposed to investigate the topological properties of arbitrary grayscale images by employing a straightforward procedure used in 2D magnetism studies to calculate topological numbers. This method utilizes machine learning techniques to transfer chiral magnetic textures onto the images. Then, the topological number is then computed directly from the converted images by integrating the solid angles formed by adjacent spin vectors. The method successfully identifies the topological numbers of various grayscale images, showing stable performances against small noises. Furthermore, two applications of the method: are demonstrated topological analysis of the Modified National Institute of Standards and Technology (MNIST) dataset and the counting of blood cells in microscopic images.

Design and implementation of a physical layer optical fiber security communication system based on a ZUC stream cipher

In this work, we have proposed a new physical layer encryption scheme to improve the security of optical fiber communication systems. The secrecy scheme is based on disturbance of the data cluster under the ZUC algorithm (named for Zu Chongzhi, a famous mathematician in ancient China), which is generated by optical XOR using a dual-drive Mach–Zehnder modulator (DD-MZM). A base scrambling pseudo random binary sequence (PRBS) generated by the ZUC stream cipher is introduced, which results in a better scrambling effect and randomness in the key stream of the block encryption structure. A simulation setup conducted in an encrypted transmission of the 64×64 grayscale image over 80 km of optical fiber shows that the authorized user can successfully decrypt the received signal, while the eavesdroppers cannot derive useful information with a bit error rate (BER) at approximately 0.5. In the simulation, after setting the delay compensation to 3.125 ps, a correct signal with a Q-factor as high as 11.3642 and a BER as low as 3.14295−30 can be obtained.

Uniform RIP analysis for the [formula omitted]-[formula omitted] minimization

This paper presents a uniform restricted isometry property (RIP) analysis of the ℓp−ωℓq minimization method for the recovery of sparse signals in both noiseless and noisy scenarios. The analysis is primarily based on a crucial sparse representation for any given signals. Moreover, we propose an effective algorithm for solving the ℓp−ωℓq minimization problem. The performance of this algorithm is validated through extensive numerical experiments on synthetic data, ECG signals, and grayscale images.