Binary Image Research Articles

Automatic thresholding method using single information entropy under product transformation of order difference filter response

To automatically threshold images with unimodal, bimodal, multimodal or non-modal gray level distributions within a unified framework, an automatic thresholding method using single information entropy under the product transformation of order difference filter response is proposed. The proposed method first performs the product transformation of order difference filter response on an input image at different scales to obtain the product transformation image. Critical or non-critical pixels are labelled on each pixel of the binary images corresponding to different thresholds to construct a series of binary label images that are used for distinguishing critical or non-critical regions. A single information entropy is finally used for characterizing the information obtained from the product transformation image with the critical regions of different binary label images, and the threshold corresponding to maximum information entropy is selected as final threshold. The proposed method is compared with seven state-of-the-art segmentation methods. Experimental results on 12 synthetic images and 98 real-world images show that the average Matthews correlation coefficients of the proposed method reached 0.994 and 0.966 for the synthetic images and the real-world images, which outperform the second-best method by 52.4 % and 27.8 %, respectively. The proposed method has more robust segmentation adaptability to test images with different modalities, despite not offering an advantage in terms of computational efficiency.

Precession, nutation, and dynamic trajectory of magnetic rod in axisymmetric magnetic field

Precession and nutation in dynamical systems have been of long-standing research interest. Manifesting in systems as diverse as spinning tops to celestial bodies like planets, understanding these motions offers a window into the intricate dance of forces governing such systems. One example of a previously unexplored system exhibiting precession and nutation is that of a magnetic rod in an axisymmetric magnetic field. This project aims to study this system through qualitative, quantitative, and experimental means. A theoretical model was formulated using Newton's Second Law and the torque equation, accounting for energy dissipation. One unique aspect of this system compared to other systems exhibiting precession and nutation is the presence of the non-linear magnetic force and torque, which were modelled by considering both magnets as current-carrying cylinders. Experimentally, the dynamic trajectory and relevant degrees of freedom of the rod were measured using image binarization and ellipse fitting. This was used for a systematic investigation into how the nutation, precession and petaloid-like trajectories are affected by the number of magnets, initial angular velocity and other relevant parameters. Theoretical predictions and experimental results show a good agreement. This work has various engineering applications such as centrifuge design.

Image compression encryption algorithm combining two-dimensional modular hyperchaotic map and compressed sensing

Recently, to offer better ensure for image privacy security, numerous new image encryption algorithms have been proposed. However, these algorithms still suffer from the problems of chaotic performance scarcity, low encryption effect, and high consumption of computational resources. To solve the above issues, we first construct a two-dimensional modular hyperchaotic map (2D-MHM). Then, we further develop an image encryption algorithm based on 2D-MHM and compressed sensing (CS). Several chaotic metrics verify the randomness and validity of 2D-MHM. These metrics include bifurcation diagram, Lyapunov exponent, initial value sensitivity, 0–1 test, and NIST test. Specifically, CS significantly reduces the ciphertext image size thereby reducing its resource consumption during transmission. Reality-preserving fractional DCT (RP-Fdct) diffusion is utilized to transform pixels into the frequency domain to enhance the encryption effect. Subsequently, lightweight index confusion and XOR diffusion further improve the algorithm security. The security of the algorithm is verified through various experiments. It is able to encrypt grayscale and color images of different sizes with good results. Notably, this algorithm also implements the encryption requirements for binary images. Due to our designs, it outperforms recently reported encryption algorithms in several areas, especially in reconstruction performance.

Three-stage binarization of color document images based on discrete wavelet transform and generative adversarial networks

The efficient extraction of text information from the background in degraded color document images is an important challenge in the preservation of ancient manuscripts. The imperfect preservation of ancient manuscripts has led to different types of degradation over time, such as page yellowing, staining, and ink bleeding, seriously affecting the results of document image binarization. This work proposes an effective three-stage network method to image enhancement and binarization of degraded documents using generative adversarial networks (GANs). Specifically, in Stage-1, we first split the input images into multiple patches, and then split these patches into four single-channel patch images (gray, red, green, and blue). Then, three single-channel patch images (red, green, and blue) are processed by the discrete wavelet transform (DWT) with normalization. In Stage-2, we use four independent generators to separately train GAN models based on the four channels on the processed patch images to extract color foreground information. Finally, in Stage-3, we train two independent GAN models on the outputs of Stage-2 and the resized original input images (512 × 512) as the local and global predictions to obtain the final outputs. The experimental results show that the Avg-Score metrics of the proposed method are 77.64, 77.95, 79.05, 76.38, 75.34, and 77.00 on the (H)-DIBCO 2011, 2013, 2014, 2016, 2017, and 2018 datasets, which are at the state-of-the-art level. The implementation code for this work is available at https://github.com/abcpp12383/ThreeStageBinarization.

OCTA-ReVA: an open-source toolbox for comprehensive retinal vessel feature analysis in optical coherence tomography angiography.

Optical coherence tomography angiography (OCTA) has significantly advanced the study and diagnosis of eye diseases. However, current clinical OCTA systems and software tools lack comprehensive quantitative analysis capabilities, limiting their full clinical utility. This paper introduces the OCTA Retinal Vessel Analyzer (OCTA-ReVA), a versatile open-source platform featuring a user-friendly graphical interface designed for the automated extraction and quantitative analysis of OCTA features. OCTA-ReVA includes traditional established OCTA features based on binary vascular image processing, such as blood vessel density (BVD), foveal avascular zone area (FAZ-A), blood vessel tortuosity (BVT), and blood vessel caliber (BVC). Additionally, it introduces new features based on blood perfusion intensity processing, such as perfusion intensity density (PID), vessel area flux (VAF), and normalized blood flow index (NBFI), which provide deeper insights into retinal perfusion conditions. These additional capabilities are crucial for the early detection and monitoring of retinal diseases. OCTA-ReVA demystifies the intricate task of retinal vasculature quantification, offering a robust tool for researchers and clinicians to objectively evaluate eye diseases and enhance the precision of retinal health assessments.

STORM image denoising and information extraction.

Stochastic optical reconstruction microscopy (STORM) is extensively utilized in the fields of cell and molecular biology as a super-resolution imaging technique for visualizing cells and molecules. Nonetheless, the imaging process of STORM is frequently susceptible to noise, which can significantly impact the subsequent image analysis. Moreover, there is currently a lack of a comprehensive automated processing approach for analyzing protein aggregation states from a large number of STORM images. This paper initially applies our previously proposed denoising algorithm, UNet-Att, in STORM image denoising. This algorithm was constructed based on attention mechanism and multi-scale features, showcasing a remarkably efficient performance in denoising. Subsequently, we propose a collection of automated image processing algorithms for the ultimate feature extractions and data analyses of the STORM images. The information extraction workflow effectively integrates automated methods of image denoising, objective image segmentation and binarization, and object information extraction, and a novel image information clustering algorithm specifically developed for the morphological analysis of the objects in the STORM images. This automated workflow significantly improves the efficiency of the effective data analysis for large-scale original STORM images.

Convolutional neural networks and seismogram fingerprints as a tool for recognizing the nature of seismic events

In the presented article, two facts are convincingly demonstrated based on the results of numerous experiments. Firstly, fingerprints are informative enough representations of signal seismograms, despite their compression, to carry information about the nature of a seismic event. Secondly, the study showed that it is practically possible to design and train an artificial neural network capable of classifying events by origin based on their fingerprints with high accuracy. Fingerprints are a tenthousandfold compressed representation of the original seismogram obtained using the onedimensional wavelet transform and the twodimensional Haar wavelet transform. They carry information about all significant frequencytime phenomena contained in the primary seismogram. Convolutional neural networks were chosen as a class of neural network classifier based on the conducted review of publications on this topic. They have proven themselves to be excellent in recognizing objects and persons in raster images. And the fingerprints used in this study are binary images measuring 64×64 pixels. The convolutional neural network prepared to work with them has one of the simplest architectures for this type of network and a very small number of adjustable parameters. By means of it, the classification accuracy of 95% was easily achieved. To prove that this result is not accidental, a strategy for modeling the architecture of convolutional neural networks using specially developed software, the Trova system, is demonstrated. This software allows the researcher to easily and conveniently operate seismograms, obtain binary fingerprints from them, correctly augment data, create, train and test neural networks. An important feature of the Trova system is the advanced functionality of the graphical representation of multidimensional modeling results.

High-Precision Automated Soybean Phenotypic Feature Extraction Based on Deep Learning and Computer Vision.

The automated collection of plant phenotypic information has become a trend in breeding and smart agriculture. Four YOLOv8-based models were used to segment mature soybean plants placed in a simple background in a laboratory environment, identify pods, distinguish the number of soybeans in each pod, and obtain soybean phenotypes. The YOLOv8-Repvit model yielded the most optimal recognition results, with an R2 coefficient value of 0.96 for both pods and beans, and the RMSE values were 2.89 and 6.90, respectively. Moreover, a novel algorithm was devised to efficiently differentiate between the main stem and branches of soybean plants, called the midpoint coordinate algorithm (MCA). This was accomplished by linking the white pixels representing the stems in each column of the binary image to draw curves that represent the plant structure. The proposed method reduces computational time and spatial complexity in comparison to the A* algorithm, thereby providing an efficient and accurate approach for measuring the phenotypic characteristics of soybean plants. This research lays a technical foundation for obtaining the phenotypic data of densely overlapped and partitioned mature soybean plants under field conditions at harvest.

Development of an image binarization software tool for net occlusion estimations

Marine biofouling poses a set of challenges to the salmon aquaculture industry, where an accumulation of biota on pens can lead to significant net mesh occlusion. This can reduce flow rates, risking oxygen depletion. The industry currently manages this challenge through regular cleaning of nets, with sporadic manual visual estimations of net occlusion an important but time-consuming task. This study developed a simple automated desktop application to more regularly and more robustly quantify pen net occlusion caused by biofouling. This software application pre-processes and binarizes images collected from cameras currently used by the industry into water and non-water pixels. The percentage of net occlusion is then calculated from the binary image. Accurate binarization of representative images was achieved by training a deep learning network on images collected in situ. The resulting network attained a validation accuracy of 96.4 % and a mean test accuracy of 93.5 %. From the test images, 98.3 % of pixels annotated as non-water and 88.8 % of pixels annotated as water were correctly classified by the network. This automated tool has the capacity to better inform industry and create a more efficient cleaning framework based on the needs of individual pens, based on data that can be more readily obtained as compared to manual net inspections.

Lung cancer classification model using convolutional neural network with feature ranking process

Abstract Lung cancer is the leading cause of cancer-related deaths worldwide, highlighting the importance of early detection to improve patient outcomes. The goal of this study is to create a computer-aided diagnosis (CAD) system that detects and classifies lung cancer based on medical images using a Convolutional Neural Network (CNN) and feature extraction techniques. The study employs the LIDC-IDRI dataset, a comprehensive collection of lung cancer-related medical images, to achieve this goal. Contrast Limited Adaptive Histogram Equalization (CLAHE) is used to improve contrast and detail while reducing noise. The Sobel operator is used to refine the segmentation process by enhancing edges and contours in binary images. Morphological operations such as dilation and filling are used to refine the segmented regions even further. Feature extraction is used to extract statistical data and texture characteristics from segmented regions. Mean and variance calculations reveal information about brightness and variability within regions, whereas co-occurrence matrices and Gray-Level Co-occurrence Matrix (GLCM) properties quantify texture features. The correlation between different regions is also evaluated to assess their relationships. Using the pre-processed and ranked features as inputs, a CNN model with five hidden layers is trained. To classify the segmented regions into cancerous and non-cancerous classes, the model learns patterns and relationships in the data. A confusion matrix is used to assess the accuracy, specificity, and sensitivity of the model's predictions, with an emphasis on correctly identifying lung cancer-affected regions. The results show promising results, with the proposed CAD system identifying lung cancer-affected regions with an accuracy of 99.4375%. The system also outperforms other existing methods with a specificity of 99.12% and a sensitivity of 99.26%. These findings highlight the developed system's potential as a valuable tool for early lung cancer detection, assisting doctors in making accurate diagnoses and improving patient outcomes.

Fourier coded aperture transform rapid spectral imaging

The Fourier coded aperture transform hyperspectral imaging system (FCTS), which we proposed in our previous work, has excellent physical interpretability compared to other encoding methods. Due to the excellent energy concentration characteristic of Fourier encoding, it can achieve high-quality spectral imaging even under undersampling conditions. However, since the grayscale encoding method limits the system's sampling speed, we propose a binary sampling Fourier-encoded spectral imaging method. Besides, to address the significant errors of reconstructed spectral image at low sampling rates, we construct a spectral image enhancement network, which introduces spatial and channel attention modules to successively enhance the spatial and spectral information of the reconstructed spectral images. During the spectral image refinement and enhancement stage, band grouping is performed to alleviate the difficulty in feature extraction and make the training process more stable. The effectiveness of our proposed method has been validated on both public datasets and our own datasets. When the sampling times are reduced by an order of magnitude, on the three public datasets, the PSNR improved by 10.79 dB, the SSIM increased by 0.35, and the SAM decreased by 12.1° on average. On our own datasets, the PSNR improved by 12 dB, the SSIM increased by 0.24, and the SAM decreased by 6.5°.

The crack propagation behaviors, microstructure and mechanical properties of T-welded joints for TIGW with crystal plasticity model and XFEM

In view of the significant impact of welding defects on the fracture behaviors involving the strength, stress concentration and bearing capacity of welded joint, etc., the propagation behaviors and mechanical properties of welded joints with porosity and micro crack are deeply investigated using a multiscale method. In this work, a crack nucleation and propagation model based on crystal plasticity theory, combined with the extended finite element method (XFEM), is established for the T-welded joint. The maximum slip on the predominant slip system method is applied to predict the crack propagation path of pores and micro cracks in the weld zone (WZ), and the effect of crystal orientation on crack growth is explored. Then, a continuous model is used to analyze the micro and macro fracture behaviors near the weld under tensile load, combined with the maximum principal stress method. The WZ and heat affected zone (HAZ) are observed using electron backscatter diffraction (EBSD) to study the microstructure evolution. Considering grain boundaries, the image information of crystal morphology is processed through binary image analysis for FE modeling. The local mechanical properties testing is carried out using micro-specimens of HAZ and WZ to calibrate the crystal plastic parameters. The results show that the resolved shear stress of the predominant slip system of crack initiation and propagation elements is increased by pore and crack defects. The fracture positions of tensile specimens through numerical simulation are in good agreement with the macroscopic experimental results. It will provide an analysis basis for preventing fracture failure and improving the service performance of thin-walled structures in future.

The Application of Tsallis Entropy Based Self-Adaptive Algorithm for Multi-Threshold Image Segmentation.

Tsallis entropy has been widely used in image thresholding because of its non-extensive properties. The non-extensive parameter q contained in this entropy plays an important role in various adaptive algorithms and has been successfully applied in bi-level image thresholding. In this paper, the relationships between parameter q and pixels' long-range correlations have been further studied within multi-threshold image segmentation. It is found that the pixels' correlations are remarkable and stable for images generated by a known physical principle, such as infrared images, medical CT images, and color satellite remote sensing images. The corresponding non-extensive parameter q can be evaluated by using the self-adaptive Tsallis entropy algorithm. The results of this algorithm are compared with those of the Shannon entropy algorithm and the original Tsallis entropy algorithm in terms of quantitative image quality evaluation metrics PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity). Furthermore, we observed that for image series with the same background, the q values determined by the adaptive algorithm are consistently kept in a narrow range. Therefore, similar or identical scenes during imaging would produce similar strength of long-range correlations, which provides potential applications for unsupervised image processing.

Efficient compressed storage and fast reconstruction of large binary images using chain codes

AbstractLarge binary images are used in many modern applications of image processing. For instance, they serve as inputs or target masks for training machine learning (ML) models in computer vision and image segmentation. Storing large binary images in limited memory and loading them repeatedly on demand, which is common in ML, calls for efficient image encoding and decoding mechanisms. In the paper, we propose an encoding scheme for efficient compressed storage of large binary images based on chain codes, and introduce a new single-pass algorithm for fast parallel reconstruction of raster images from the encoded representation. We use three large real-life binary masks to test the efficiency of the proposed method, which were derived from vector layers of single-class objects – a building cadaster, a woody vegetation landscape feature map, and a road network map. We show that the masks encoded by the proposed method require significantly less storage space than standard lossless compression formats. We further compared the proposed method for mask reconstruction from chain codes with a recent state-of-the-art algorithm, and achieved between $$12\%$$ 12 % and $$33\%$$ 33 % faster reconstruction on test data.

A Mumford-Shah Model-based Method for Inpainting Markers in Ultrasound Images

Abstract During ultrasound diagnosis in clinics, clinicians often mark the lesion area, resulting in the presence of unnecessary objects in the collected ultrasound images. These markers potentially affect subsequent image analysis. To address this problem, we proposed an image inpainting method that combines image processing with Mumford-Shah algorithm to remove markers from ultrasound images. The proposed method consists of two parts. First, the input ultrasound images were processed by several image processing algorithms, including contrast enhancement, image binarization, edge detection, hole filling, and connected domain marking. These algorithms were combined to extract the mask of the manual marker in the ultrasound images. Second, the original ultrasound images and mask images were put into the Mumford-Shah model to implement ultrasound image inpainting by minimizing the following energy functional. The continuous iteration of Euler-Lagrange equation was employed to update the iteration item. If the difference between the two output results was less than the pre-set threshold, the output result could be regarded as the repaired ultrasound image. Experimental results show that the designed image processing method accurately extracted the binary mask, whether they are colourful or binary, number or line in ultrasound images. Meanwhile, the improved Mumford-Shah model effectively implemented the ultrasound image inpainting, with the accuracy of 96.55%. Therefore, the proposed method can be used to remove unnecessary objects in ultrasound images, which is beneficial to improve the accuracy of the artificial intelligence model.

Morphology for hexagonal image processing: a comprehensive simulation analysis

<p>Morphological operators for binary and grayscale images are commonly used to eliminate noise, recognize contours or specific structures, and arrange shapes in image processing for physiological modeling and biomechanics applications. Even though morphology has been substantially developed in square-pixelbased-image-processing (SIP), no effort has been made to construct morphological operators in hexagonal-pixel-based-image-processing (HIP) yet. In this paper, we transform basic SIP-domain-morphological operators such as dilation, erosion, closing, and opening into HIP-domain and compare their performance with their SIP counterparts. It is the first time to give the fundamental morphological operators in the HIP domain. The operators developed in this paper initiate the research about morphology in the HIP domain by successfully filling a significant gap by eliminating HIP’s lack of basic operators, thus capable of producing enhanced images for better analysis in anatomical models related to biology and medicine research fields.</p>

Effect of raw material fineness on the properties of inorganic foam materials from solid waste

Solid waste inorganic lightweight materials not only compensate for the flammability of organic materials but also have a lower carbon footprint. They are environmentally safe and cost-effective compared with cement-based foaming materials, and they have good development prospects. This study aimed to improve the chemical foaming reaction kinetics by reducing the particle size of aluminum powder and alkaline solid waste, thereby increasing the expansion degree of solid waste slurry, to further reduce the absolute dry density of inorganic lightweight materials for solid waste and optimize their performance. The influence of the particle size of raw material on the absolute dry density, water absorption, and compressive strength of lightweight materials was investigated. The influence of the particle size of raw material on the macroscopic pore structure of foamed materials was analyzed through binarization of cross-sectional images of specimens. Furthermore, the hydration products and microstructure of materials were characterized using XRD, FTIR, TG-DTG, and SEM to provide a theoretical basis for related research. The experimental results showed that the smaller the particle size of the raw material, the easier it was to carbonize and the lower the flowability of the corresponding cementitious material slurry. Reducing the particle size of raw materials and aluminum powder increased the degree of slurry expansion, thereby reducing the absolute dry density of foaming materials. In addition, reducing the particle size of raw materials and using carbide slag wastewater instead of tap water increased the degree of foaming reaction and hydration reaction, which helped C-(A)-S-H gel formation and promoted the development of sample strength. Finally, the absolute dry density of inorganic lightweight materials was reduced to 0.35 g/cm3 at the minimum, and the corresponding 28-day compressive strength was 0.28 MPa, which met the requirements of the JG/T 266–2011 “foam concrete” specification.

Radar Based Secure Contactless Fall Detection Using Hybrid Optimizer with Convolution Neural Network

Introduction: senior citizens can lead to severe injuries. Existing wearable fall-alert sensors are often ineffective as seniors tend to avoid using them, highlighting the need for non-contact sensor applications in smart homes. This study proposes a CNN-based fall detection system using time-frequency analyses. A unique hybrid optimizer, GWO-ABC, combining Artificial Bee Colony (ABC) and Grey Wolf Optimizer (GWO), is employed to optimize CNN architectures. Radar return signals are transformed into spectrograms and binary images for training the HOCNN with fall and non-fall data.Methods: Radar signals are processed using short-time Fourier transformation to create time-frequency spectrograms, converted into binary images. These images are fed into a CNN optimized by the GWO-ABC algorithm. The CNN is trained on labelled fall and non-fall instances, focusing on high-level feature extraction.Results: The HOCNN showed superior accuracy in fall detection, successfully extracting critical high-level features from radar signals. Performance metrics, including precision, recall, and F1-score, demonstrated significant improvements over traditional methods.Conclusion: This study introduces a non-contact, automatic fall detection system for smart homes using GWO-ABC optimized CNNs, offering a promising solution for enhancing geriatric care and ensuring senior citizen safety. Index Terms—Grey Wolf Optimizer, Artificial Bee Colony algorithm, Convolutional neural network, fall detection, time-frequency analysis, ultra-wideband (UWB) radar

Automatic recognition system for concrete cracks with support vector machine based on crack features

Cracks are a common problem in concrete surfaces. With the continuous optimization of machine vision-based inspection systems, effective crack detection and recognition is the core of the entire system. In this study, support vector machine (SVM) was used to distinguish cracks from other regions. To complete the recognition system of the SVM, a framework consisting of an image processing and recognition model was proposed. An algorithm combining the Prewitt operator with the Otsu threshold was proposed for image segmentation. The binary image processed by the new algorithm combined with mathematical morphology can result in a more complete crack zone and fewer interference regions. After the initial parameter extraction, most of the impurity areas were screened by preliminary discrimination, removing 99% of the impurities. This processing step ensured the balance and effectiveness of the samples. To establish an automatic identification model based on SVM with a radial basis function, compactness, occupancy rate, and length–width ratio were selected as input parameters after comparing these three features with all six features of the crack. The recognition accuracy of this system reaches 97.14%, demonstrating that the proposed method is effective and satisfies practical requirements.

A Learning Dendritic Neuron-Based Motion Direction Detective System and Its Application to Grayscale Images.

In recent research, dendritic neuron-based models have shown promise in effectively learning and recognizing object motion direction within binary images. Leveraging the dendritic neuron structure and On-Off Response mechanism within the primary cortex, this approach has notably reduced learning time and costs compared to traditional neural networks. This paper advances the existing model by integrating bio-inspired components into a learnable dendritic neuron-based artificial visual system (AVS), specifically incorporating mechanisms from horizontal and bipolar cells. This enhancement enables the model to proficiently identify object motion directions in grayscale images, aligning its threshold with human-like perception. The enhanced model demonstrates superior efficiency in motion direction recognition, requiring less data (90% less than other deep models) and less time for training. Experimental findings highlight the model's remarkable robustness, indicating significant potential for real-world applications. The integration of bio-inspired features not only enhances performance but also opens avenues for further exploration in neural network research. Notably, the application of this model to realistic object recognition yields convincing accuracy at nearly 100%, underscoring its practical utility.