Circular Window Research Articles

Facies and mechanical stratigraphy control fracture intensity, topology and fractal dimension in folded turbidite sandstones, Northern Apennines, Italy

Fracture network intensity, topology and connectivity have been frequently analysed using circular scan windows, an efficient method for geometrical properties characterization, although affected by truncation and censoring. Many studies that use circular scans focus on the spatial variation of the geometrical properties in relation to tectonic structures such as faults and folds, and at the regional scale. A lower amount of information is available in the literature on the relations between depositional features, mechanical and petrophysical properties of facies, and the corresponding fracture network geometrical attributes. In this contribution, we focus on these relationships, which are fundamental controlling factors for predicting fracture geometry in the subsurface and for improving modelling in exploration, production and management of reservoirs for fluid exploitation and storage. We characterized these properties in 35 selected turbidite beds of the Marnoso-arenacea Fm., in the Northern Apennines of Italy, exposed along a 250 m-thick section. Moreover, we calculate the fractal dimensions of fracture networks through the box-counting method. Our data indicate that depositional facies control porosity and uniaxial compressive strength, as well as fracture intensity and fracture network topology. We show that fracture intensity is invariant and unrelated to the sandstone facies thickness in medium-grained turbidite beds. On the other hand, a strong control on fracture intensity in fine-grained turbidite beds is also exerted by the thickness of bounding claystone, which is higher when the bounding claystone is thicker. Moreover, we observe that the cross joint pattern and strike could be influenced by the depositional structures.

Single-Step Production of Doubly Re-entrant Microstructures Through Reaction-Diffusion Photolithography for Omniphobic Surfaces.

Omniphobic surfaces, which repel virtually any liquid regardless of its wettability, have been developed using doubly re-entrant microstructures. Although various microfabrication techniques have been explored, these often require multiple complex steps. In this study, reaction-diffusion photolithography (RDP) is used to fabricate micropost arrays with doubly re-entrant geometries through a single-step ultraviolet (UV) exposure process. To create a doubly re-entrant structure consisting of a central, elongated micropost topped with a short ridge, a photomask featuring a circular window surrounded by a narrow ring-shaped window is employed. This configuration is utilized in the RDP process, which relies on radical polymerization. Vertical growth of the structure from the photomask is achieved by introducing strong UV attenuation along the vertical axis, enabled by increasing the photoinitiator concentration. Concurrently, the growth of the ridge is slowed down by designing the ring window with minimal dimensions. This promotes rapid oxygen diffusion into the ring region, where the radicals generated by UV exposure are consumed through reactions with oxygen, thereby delaying the polymerization. This approach enables precise fabrication of full-length central microposts with short ridges in a single step. The resulting doubly re-entrant structures show high contact angles across various liquids and exhibit robust omniphobic performance.

Edge segmentation method for Si3N4 bearing rolling elements microcracks with profile-distortion

For the profile-distortion of Si3N4 bearing rolling elements microcracks image. A coupling method of variable-scale truncated median filtering and detection-driven active contour model is proposed. Details are as follows. The sigmoid activation function is constructed to dynamically adjust the circular window. The local threshold truncation is designed to adapt to the characteristics of different regions in the image. The area threshold is defined to eliminate non-target features. The mean peak signal-to-noise ratio (PSNR) of filtered images is about 40.263. The edge overlap rate is as low as 0.1027. The mean accuracy of feature extraction is about 94.33 %. To sum up, the influence result from profile-distortion on the accuracy of feature extraction of Si3N4 bearing rolling elements microcracks is effectively solved.

Research on Microclimate Optimization of Traditional Residential Buildings in Central Anhui Based on Humid and Hot Climate Characteristics and Regional Architectural Features

This study examines how building space, materials, and structure affect the microclimate of Liu Tong Xinglongzhuang in central Anhui, known for its hot, humid climate and distinct architecture. Using qualitative and quantitative methods, including field monitoring, PHOENICS simulation, and Ladybug Tools, the following conclusions were drawn: (1) Building materials: wood reduces indoor temperature; a glass roof over the patio lowers temperature and improves wind speed; and reducing tile area decreases solar radiation. (2) Building space: optimal comfort in stairwells is achieved with a length-to-width ratio of 1.5:1 and height-to-width ratio of 2:1; courtyards are most comfortable with a length-to-width ratio of 2.5:1 and height-to-width ratio of 1.5:1; walk-through halls are optimal with a length-to-width ratio of 2:1 and height-to-width ratio of 1.5:1; and wings achieve the highest comfort with a length-to-width ratio of 2.5:1 and height-to-width ratio of 1.5:1. (3) Building structure: optimal wind speed and temperature are achieved with specific window height-to-width ratios of 1:1 for exterior and 1.5:1 for interior windows; the lowest temperatures occur at 2:1 ratios; hexagonal exterior and circular interior windows maximize wind speed and temperature reduction; and smaller exterior door openings lower indoor temperatures and are best achieved with an interior door ratio of 2:1. These findings offer valuable data and methods for optimizing the microclimate of traditional houses, with significant practical implications.

Trapping of particles diffusing in cylindrical cavity of arbitrary length and radius by two small absorbing disks on the cavity side wall: Narrow escape theory and beyond.

Narrow escape theory deals with the first passage of a particle diffusing in a cavity with small circular windows on the cavity wall to one of the windows. Assuming that (i) the cavity has no size anisotropy and (ii) all windows are sufficiently far away from each other, the theory provides an analytical expression for the particle mean first-passage time (MFPT) to one of the windows. This expression shows that the MFPT depends on the only global parameter of the cavity, its volume, independent of the cavity shape, and is inversely proportional to the product of the particle diffusivity and the sum of the window radii. Amazing simplicity and universality of this result raises the question of the range of its applicability. To shed some light on this issue, we study the narrow escape problem in a cylindrical cavity of arbitrary size anisotropy with two small windows arbitrarily located on the cavity side wall. We derive an approximate analytical solution for the MFPT, which smoothly goes from the conventional narrow escape solution in an isotropic cavity when the windows are sufficiently far away from each other to a qualitatively different solution in a long cylindrical cavity (the cavity length significantly exceeds its radius). Our solution demonstrates the mutual influence of the windows on the MFPT and shows how it depends on the inter-window distance. A key step in finding the solution is an approximate replacement of the initial three-dimensional problem by an equivalent one-dimensional one, where the particle diffuses along the cavity axis and the small absorbing windows are modeled by delta-function sinks. Brownian dynamics simulations are used to establish the range of applicability of our approximate approach and to learn what it means that the two windows are far away from each other.

POLarizer-FENestration (POLFEN) a novel concept of a glazing technology to modulate daylighting and glare in buildings

A conceptual new window/glazing technology called POLarizer-FENestration (POLFEN) is presented. In 1935, Edwin Land's pioneering work on polarizers successfully developed a sheet polarizer that was used in various applications. Subsequently, polarizers were employed in windows to modulate daylight and reduce associated glare. Early prototype windows were circular in shape so that the polarizing filter could be rotated to modulate the daylight. However, compared to rectangular-shaped windows that are ubiquitous, circular windows are rare in buildings. Therefore, a new window/glazing technology, discussed in this paper, was developed that utilizes a polarizing film in a conventional rectangular or square window, arranged in the form of strips or a checker-grid over the window panes to modulate daylighting levels and control glare. In this new research, a working prototype of the glazing technology is developed and demonstrated including simulations using the Radiance daylighting simulation software. In the current work, two types of measurements were taken: photographs of the indoor luminous environment and illumination levels (Lux) at selective points on the work-plane both in a physical prototype as well as in the simulation model under two sky conditions: CIE clear sky with sun, and overcast sky. The results indicate that the new glazing technology resulting from this concept can effectively modulate daylighting levels, minimized associated glare to an acceptable level while maintaining a view to the outdoor environment. The technology can be classified as Variable Transmission Glazing (VTG), similar to Electrochromic (EC) glazing since it also offers a dynamic modulation of solar optical properties (Tvis).

AdaPIP: Adaptive picture-in-picture guidance for 360° film watching

Abstract360° videos enable viewers to watch freely from different directions but inevitably prevent them from perceiving all the helpful information. To mitigate this problem, picture-in-picture (PIP) guidance was proposed using preview windows to show regions of interest (ROIs) outside the current view range. We identify several drawbacks of this representation and propose a new method for 360° film watching called AdaPIP. AdaPIP enhances traditional PIP by adaptively arranging preview windows with changeable view ranges and sizes. In addition, AdaPIP incorporates the advantage of arrow-based guidance by presenting circular windows with arrows attached to them to help users locate the corresponding ROIs more efficiently. We also adapted AdaPIP and Outside-In to HMD-based immersive virtual reality environments to demonstrate the usability of PIP-guided approaches beyond 2D screens. Comprehensive user experiments on 2D screens, as well as in VR environments, indicate that AdaPIP is superior to alternative methods in terms of visual experiences while maintaining a comparable degree of immersion.

Simulation study of secondary electron multiplication on microwave dielectric window

The phenomenon of dielectric breakdown in microwave windows restricts the enhancement of power capacity in high-power microwave systems. This process starts with the secondary electron multiplication, which significantly influences the breakdown threshold. In this study, we developed 3D simulation models for rectangular, circular, and annular windows to examine the secondary electron multiplication on their surfaces using the particle-in-cell method. Through a comparative study, we assessed the effects of various input powers, initial emission current densities from particle sources, and magnetic fields. Our findings reveal that the initial field emission current density, which ranges from 10−9 to 109 μA/cm2, marginally affects electron multiplication. However, increased microwave power (with power density ranging from 0.1 to 18.75 MW/cm2) accelerates this multiplication. Strong magnetic fields and non-uniform electric fields further enhance the electron multiplication process.

Local Water Inrush Risk Assessment Method Based on Moving Window and Its Application in the Liangshuijing Mining Area

Most of the existing coal mine water inrush risk assessment methods are global assessment methods, which have the following problems: they ignore the difference in importance of the evaluation indicators at different locations in the study area and assign the same weight value; the area of the danger zone in the evaluation results is thus too large. The evaluation results improve the prediction accuracy by reducing the safe zone area. To address the aforementioned issues, this study employs a local analysis method based on a moving circular window, taking into account the spatial heterogeneity of criterion indicators in the decision-making process. By traversing each position of the raster with a circular moving window, the method performs local standardization and calculates local weights of indicators within the local window range. Based on the obtained maps of locally standardized evaluation criteria and local weights, a local water inrush risk assessment model is established using Geographic Information Systems (ArcGIS), considering the differences in the importance of evaluation indicators within the study area. Taking the Liangshuijing mining area as an example, both global and local evaluation models were employed to assess its water inrush risk. The evaluation results obtained from these two models were compared and validated against geological survey data and historical water inrush points. The comparative analysis between the two methods reveals that the local evaluation model demonstrates higher accuracy. It offers a more precise delineation of the distribution of water inrush risk zones, which better corresponds to the actual conditions within the mine. The localized water inrush risk assessment method proposed in this paper breaks away from the traditional approach of uniformly weighting evaluation indicators across the entire area, offering a novel method for assessing water inrush risk.

A radiometric and rotational invariant feature descriptor for multimodal remote sensing image matching using the monogenic signal

ABSTRACT Multimodal remote sensing image matching is still a challenging task, and its matching performance will highly influence the subsequent applications, such as image fusion, image mosaic and change detection. To tackle this problem, we propose a radiometric and rotational invariant descriptor (RRID) using the monogenic signal for multimodal image matching. First of all, a multiscale feature asymmetry measurement is proposed to obtain more reliable detected points, in which the multiscale feature asymmetry can be computed from the intermediate results from the monogenic signal; then a multiscale local phase orientation computation algorithm is designed for the main orientation assignment of each keypoint, in which the local phase orientation can also be provided by the monogenic signal construction; finally, a circular local window around each keypoint is extracted, a histogram of local phase orientations weighted by phase congruency is calculated for the log-polar feature description. RRID is compared with other state-of-the-art multimodal image matching methods on three publicly available datasets. Experimental results have demonstrated that our proposed method outperforms the compared methods in terms of precision, recall, and F1-measure criteria and also has achieved the lowest RMSEs.

Polarization conversion filter based on electromagnetically induced transparency-like effect

Owing to the large losses in the conversion process of traditional polarization converters, there is an increasing demand for metasurfaces with excellent transmission performance. In this work, an efficient polarization conversion metasurface is proposed based on electromagnetically induced transparency-like (EIT-like) effect in the terahertz band. The multi-level bright mode paths are excited by an asymmetric structure to obtain orthogonal circular polarization conversion windows. The transmission window is generated by the mutual interference of two sets of bright modes with similar resonant frequencies. Then an asymmetric structure is constructed to achieve transmission window shift under TE polarization and TM polarization, thereby realizing dual-frequency polarization conversion. The metamaterial unit structure consists of four open metal resonant rings and four metal resonant strips. The working mechanism is explored by analyzing the surface current distribution, frequency response, and incident angle characteristics. The results show that electromagnetically induced transparency can be achieved under different polarizations. Furthermore, based on the EIT resonance between the two incident polarizations, the conversion from linear polarization to right-hand circular polarization is achieved at 0.692 THz, and the conversion from linear polarization to left-hand circular polarization is realized at 0.782 THz, transmission coefficients are 0.7 and 0.68 respectively. According to the Stokes parameters, the corresponding ellipticity <i>η</i> values are 96% and 98%, respectively. This EIT-based polarization conversion metasurface with low loss and ultra-thin characteristics has great potential applications in compact antennas, derived radar phased arrays, and military detectors.

Utilizing semantic-level computer vision for fracture trace characterization of hard rock pillars in underground space

This study utilizes a semantic-level computer vision-based detection to characterize fracture traces of hard rock pillars in underground space. The trace images captured by photogrammetry are used to establish the database for training two convolutional neural network (CNN)-based models, i.e., U-Net (University of Freiburg, Germany) and DeepLabV3+ (Google, USA) models. Chain code technology, polyline approximation algorithm, and the circular window scanning approach are combined to quantify the main characteristics of fracture traces on flat and uneven surfaces, including trace length, dip angle, density, and intensity. The extraction results indicate that the CNN-based models have better performances than the edge detection methods-based Canny and Sobel operators for extracting the trace and reducing noise, especially the DeepLabV3+ model. Furthermore, the quantization results further prove the reliability of extracting the fracture trace. As a result, a case study with two types of traces (i.e., on flat and uneven surfaces) demonstrates that the applied semantic-level computer vision detection is an accurate and efficient approach for characterizing the fracture trace of hard rock pillars.

Fast Grayscale Morphology for Circular Window

AbstractMorphological operations are among the most popular classic image filters. The filter assumes the maximum or minimum value within a window and is often used for light object thickening and thinning operations, which are important components of various workflows, such as object recognition and stylization. Circular windows are preferred over rectangular windows for obtaining isotropic filter results. However, the existing efficient algorithms focus on rectangular or binary input images. Efficient morphological operations with circular windows for grayscale images remain challenging. In this study, we present a fast grayscale morphology heuristic computation algorithm that decomposes circular windows using the convex hull of circles. We significantly accelerate traditional methods based on Minkowski addition by introducing new decomposition rules specialized for circular windows. As our morphological operation using a convex hull can be computed independently for each pixel, the algorithm is efficient for modern multithreaded hardware.

A perceptual field test in object experts using gaze-contingent eye tracking

A hallmark of expert object recognition is rapid and accurate subordinate-category recognition of visually homogenous objects. However, the perceptual strategies by which expert recognition is achieved is less known. The current study investigated whether visual expertise changes observers’ perceptual field (e.g., their ability to use information away from fixation for recognition) for objects in their domain of expertise, using a gaze-contingent eye-tracking paradigm. In the current study, bird experts and novices were presented with two bird images sequentially, and their task was to determine whether the two images were of the same species (e.g., two different song sparrows) or different species (e.g., song sparrow and chipping sparrow). The first study bird image was presented in full view. The second test bird image was presented fully visible (full-view), restricted to a circular window centered on gaze position (central-view), or restricted to image regions beyond a circular mask centered on gaze position (peripheral-view). While experts and novices did not differ in their eye-movement behavior, experts’ performance on the discrimination task for the fastest responses was less impaired than novices in the peripheral-view condition. Thus, the experts used peripheral information to a greater extent than novices, indicating that the experts have a wider perceptual field to support their speeded subordinate recognition.

Inhibition of Silver Diamine Fluoride-induced Tooth Discoloration by Using Natural Antioxidant: In Vitro Study.

Silver diamine fluoride (SDF) is a well-known caries preventive aid capable of arresting carious lesions and preventing secondary caries formation. Despite having the caries prevention potential, the clinical use of SDF is limited due to the tooth discoloration caused by SDF. The objective of this study was to evaluate the efficiency of natural antioxidants to inhibit SDF-induced tooth discoloration. A total of 32 bovine teeth were polished to create a 6 mm circular window on the middle 1/3 (for enamel) or on the cervical 1/3 (for dentin) of the labial surface. Specimens were treated either with SDF alone or SDF followed by ascorbic acid (AA)/alpha lipoic acid (ALA)/7th generation bonding materials. The color parameters Lightness (L*), Chroma (C*), and Hue (H*) of the tooth window were measured at pretreatment, 1-hour, 1-week, and 1-month posttreatment using a digital color chromometer. Repeated measure ANOVA showed a significant tooth color alteration at 1-hour posttreatment. The L* and H* values dropped and C* value elevated significantly in 1-hour posttreatment measurement. All experimental groups showed significant tooth color alteration after treatment (p < 0.05) and were unable to reverse the discoloration even after 1-month period except the ALA group which did not show any significant (p > 0.05) color alteration compared with the pretreatment value. Within the limitation of the in vitro model and according to the results of this study, it can be concluded that ALA has the potential to prevent SDF-induced tooth discoloration; however, AA was unable to prevent the discoloration. SDF induces discoloration of enamel and dentin can be reversed by applying Alpha lipoic acid immediacy after SDF application.

A spatial analysis of co-circulating dengue and chikungunya virus infections during an epidemic in a region of Northeastern Brazil

The aim of this study was to describe, through spatial analysis, the cases of arboviruses (dengue and chikungunya), including deaths, during the first epidemic after the circulation of the chikungunya virus (CHIKV) in the state of Pernambuco, Northeastern Brazil. This was an ecological study in both Pernambuco and the state capital, Recife, from 2015 to 2018. The odds ratios (OR) were estimated, and the statistical significance was considered p≤0.05. For the spatial analysis, Kulldorff's space-time scan statistics method was adopted to identify spatial clusters and to provide the relative risk (RR). In order to assess the significance at a level of p < 0.01 of the model, the number of Monte Carlo replications was 999 times. To perform the scan statistics we used the Poisson probability model, with a circular scanning window; annual temporal precision and retrospective analysis. A total of 227 deaths and 158,728 survivors from arboviruses was reported during the study period, with 100 deaths from dengue and 127 from CHIKV. The proportion of deaths from dengue was 0.08% and from chikungunya was 0.35%. The proportion of all those infected (deaths plus survivors) with dengue was 77.42% and with chikungunya was 22.58%. Children aged 0 to 9 years were around 3 times more likely to die than the reference group (OR 2.84; CI95% 1.16–5.00). From the age of 40, the chances of death increased significantly: 40–49 (OR 2.52; CI95% 1.19–5.29), 50–59 (OR 5.55; CI95% 2.76–11.17) and 60 or more (OR 14.90; CI95% 7.79–28.49). Males were approximately twice as likely to die as females (OR 1.77; CI95% 1.36–2.30). White-skinned people were less likely to die compared to non-white (OR 0.60; CI95% 0.41–0.87). The space-time analysis of prevalence in the state of Pernambuco revealed the presence of four clusters in the years 2015 and 2016, highlighting the Metropolitan Macro-region with a relative risk=4 and the Agreste and Hinterland macro-regions with a relative risk=3.3. The spatial distribution of the death rate in the municipality of Recife smoothed by the local empirical Bayesian estimator enabled a special pattern to be identified in the southwest and northeast of the municipality. The spatiotemporal analysis of the death rate revealed the presence of two clusters in the year 2015. In the primary cluster, it may be noted that the aforementioned aggregate presented a RR=7.2, and the secondary cluster presented a RR=6.0. The spatiotemporal analysis with Kulldorff's space-time scan statistics method, proved viable in identifying the risk areas for the occurrence of arboviruses, and could be included in surveillance routines so as to optimize prevention strategies during future epidemics.

DICE: An open-source MATLAB application for quantification and parametrization of digital outcrop model-based fracture datasets

An open-source MATLAB application (app) named Discontinuity Intensity Calculator and Estimator (DICE) was developed in order to quantitatively characterize the fractures, or in more general, discontinuities within a rocky outcrop in three-dimensional (3D) digital data, such as digital outcrop model (DOM). The workflow proposed for the parametrization of the discontinuities consists of the following steps: (1) Analysis and mapping of the fractures detected within the 3D DOMs; (2) Calculation of the orientation, position and dimensions of discontinuities that are represented by best-fit circular planes; (3) Determining the discontinuity parameters (dimension, distribution, spacing and intensity) by the DICE algorithm using different 3D oriented sampling techniques (3D oriented scanline, 3D oriented circular scan window and spherical scan volume). Different sampling methods were bench tested with a synthetic, as well as a natural case study, and compared in order to understand the advantages and limitations of each technique. The 3D oriented circular scan window appears to be the most effective method for fracture intensity estimation with high accuracy (error 0.4%) and stability with variations in scan radius.

Exploring the Influence of Input Feature Space on CNN‐Based Geomorphic Feature Extraction From Digital Terrain Data

AbstractMany studies of Earth surface processes and landscape evolution rely on having accurate and extensive data sets of surficial geologic units and landforms. Automated extraction of geomorphic features using deep learning provides an objective way to consistently map landforms over large spatial extents. However, there is no consensus on the optimal input feature space for such analyses. We explore the impact of input feature space for extracting geomorphic features from land surface parameters (LSPs) derived from digital terrain models (DTMs) using convolutional neural network (CNN)‐based semantic segmentation deep learning. We compare four input feature space configurations: (a) a three‐layer composite consisting of a topographic position index (TPI) calculated using a 50 m radius circular window, square root of topographic slope, and TPI calculated using an annulus with a 2 m inner radius and 10 m outer radius, (b) a single illuminating position hillshade, (c) a multidirectional hillshade, and (d) a slopeshade. We test each feature space input using three deep learning algorithms and four use cases: two with natural features and two with anthropogenic features. The three‐layer composite generally provided lower overall losses for the training samples, a higher F1‐score for the withheld validation data, and better performance for generalizing to withheld testing data from a new geographic extent. Results suggest that CNN‐based deep learning for mapping geomorphic features or landforms from LSPs is sensitive to input feature space. Given the large number of LSPs that can be derived from DTM data and the variety of geomorphic mapping tasks that can be undertaken using CNN‐based methods, we argue that additional research focused on feature space considerations is needed and suggest future research directions. We also suggest that the three‐layer composite implemented here can offer better performance in comparison to using hillshades or other common terrain visualization surfaces and is, thus, worth considering for different mapping and feature extraction tasks.

Protection of Coastal Shelter Forests Using UAVs: Individual Tree and Tree-Height Detection in Casuarina equisetifolia L. Forests

Casuarina equisetifolia L. plays a significant role in sandy, coastal regions for sand stabilization and windbreaks. However, C. equisetifolia forests are susceptible to plant diseases and insect pests, resulting in mortality due to pure stands and a harsh natural environment. Mapping the distribution of C. equisetifolia and detecting its height can inform forest-management decisions. Unmanned aerial vehicle (UAV) imagery, coupled with the classical detection method, can provide accurate information on tree-level forest parameters. Considering that the accuracy of a forest-parameter estimation is impacted by various flight altitudes and extraction parameters, the purpose of this study is to determine the appropriate flight altitude and extraction parameters for mapping C. equisetifolia using UAV imagery and the local maxima algorithm in order to monitor C. equisetifolia more accurately. A total of 11 different flight altitudes and 36 combinations of circular smoothing window size (CSWS) and fixed circular window size (FCWS) were tested, and 796 trees with corresponding positions in the UAV image and ground–tree heights were used as reference. The results show that the combination of a 0.1 m CSWS and a 0.8 m FCWS for individual tree detection (ITD) and tree-height detection achieved excellent accuracy (with an F1 score of 91.44% for ITD and an estimation accuracy (EA) of 79.49% for tree-height detection). A lower flight altitude did not indicate a higher accuracy for individual tree and tree-height detection. The UAV image obtained within a flight altitude of 60 m–80 m can meet the accuracy requirements for the identification of C. equisetifolia tree-height estimation (F1 score &gt; 85% for ITD; EA &gt; 75% for tree-height estimation). This study provides a foundation for monitoring C. equisetifolia by using UAV imagery and applying the local maxima algorithm, which may help forestry practitioners detect C. equisetifolia trees and tree heights more accurately, providing more information on C. equisetifolia growth status.

Lung Segmentation of Chest Radiograph Using Circular Window Based Local Contrast Thresholding (CWLCT) and Adaptive Median Outlier (CWAMO)

Certain chest illnesses, such as TB, adenocarcinoma, squamous cell carcinoma, large cell carcinoma, atelectasis, etc., can be diagnosed in chest radiographs, and the development of a CAD system relies in part on accurate lung segmentation. In order to partition the lungs in chest radiographs, this work introduces an unsupervised learning approach based on a circular window and local thresholding. The procedure involves pre-processing, a preliminary estimate of the lung field, and the elimination of noise. Images are initially scaled down to 1024x1024 and enhanced using adaptive histogram equalization. Then chest radiographs are binarized using the proposed method. Based on the geometrical and special characteristics, lungs are separated from the chest radiographs. The final step in picture segmentation is the use of morphological processes. Local thresholding, omitting extraneous body parts, filling in gaps, and filtering regions based on their attributes all contribute to preliminary estimates of the lung field.Morphological processes are used as a means of eliminating background noise. A public bone shadow eliminated JSRT dataset consisting of 247 chest x-rays is used to measure the performance of the proposed method. The effectiveness of the proposed method results’ performance is evaluated by comparing it with Active Shape Model (ASM) based lung segmentation for various performance metrics such as F-score, overlap percentage, accuracy rate, sensitivity, specificity, and precision rates. All the parameters for the proposed method are over and above 90%. Our investigations indicate that the suggested method is an unsupervised learning approach that does not require any training.