Image Thresholding Research Articles

Human Epithelial Cell Image Analysis and Segmentation using Threshold Based Fusion Technique

The most demanding aspect of digital image processing is segmenting an image efficiently. Cell segmentation or classifying cells in an image is essential while analyzing cell images in medical research, especially in spot diagnosis, cancer cell detection, and live-cell imaging segmentation forms a crucial component. This research examines existing segmentation algorithms and suggests a new segmentation technique that employs image filtering and thresholding. Thresholding is an essential part of image analysis and segmentation. Finally, the segmented image and the FCM (fuzzy C-means) based clustered image are merged. In terms of accuracy, sensitivity, dice-coefficient, and Jaccard-coefficient, the simulation coupled with ground truth data is proven to produce better segmentation outcomes.

A novel method to determine the optimal threshold of SEM images

The determination of threshold is crucial for the binarization of scanning electron microscopy (SEM) images. In this paper, a new method for determining the optimal threshold considering the three-dimensional characteristics of shale samples is proposed based on GIS. Firstly, the SEM images of seven shale samples at six magnifications are 3D reconstructed and visualized using ArcMap and ArcScene. Relevant parameters are obtained to calculate the 3D porosity φ3D. Secondly, the effects of magnification and segmentation size on φ3D are considered. By establishing the relationship between threshold and porosity, the optimal magnification, optimal segmented size and optimal threshold are obtained for the seven shale samples, respectively. Finally, the optimal threshold obtained by this method is applied to the finite element model of the pore structure and fractal dimension, respectively. The results show that the overall effect of 3D reconstruction and visualization is good. The optimal thresholds of seven shale samples are 94, 93, 105, 93, 81, 98 and 88, respectively. The porosity obtained from the finite element model of the pore structure based on the optimal threshold is suitable for SEM images with larger sizes. The fractal dimensions obtained based on the optimal threshold are all larger than those obtained at six magnifications, which can characterize a more complex pore structure and a more inhomogeneous pore size distribution. This method fully considers the three-dimensional characteristics of different samples and improves the accuracy of the threshold study of SEM images.

An enhanced exponential distribution optimizer and its application for multi-level medical image thresholding problems

In this paper, an enhanced version of the Exponential Distribution Optimizer (EDO) called mEDO is introduced to tackle global optimization and multi-level image segmentation problems. EDO is a math-inspired optimizer that has many limitations in handling complex multi-modal problems. mEDO tries to solve these drawbacks using 2 operators: phasor operator for diversity enhancement and an adaptive p-best mutation strategy for preventing it converging to local optima. To validate the effectiveness of the suggested optimizer, a comprehensive set of comparative experiments using the CEC'2020 test suite was conducted. The experimental results consistently prove that the suggested technique outperforms its counterparts in terms of both convergence speed and accuracy. Moreover, the suggested mEDO algorithm was applied for image segmentation using the multi-threshold image segmentation method with Otsu's entropy, providing further evidence of its enhanced performance. The algorithm was evaluated by comparing its results with those of existing well-known algorithms at various threshold levels. The experimental results validate that the proposed mEDO algorithm attains exceptional segmentation results for various threshold levels.

Diagnostic performance of photon-counting detector CT for differentiation between adrenal adenomas and metastases

ObjectivesAim of this study was to assess the value of virtual non-contrast (VNC) reconstructions in differentiating between adrenal adenomas and metastases on a photon-counting detector CT (PCD-CT).Material and methodsPatients with adrenal masses and contrast-enhanced CT scans in portal venous phase were included. Image reconstructions were performed, including conventional VNC (VNCConv) and PureCalcium VNC (VNCPC), as well as virtual monochromatic images (VMI, 40–90 keV) and iodine maps. We analyzed images using semi-automatic segmentation of adrenal lesions and extracted quantitative data. Logistic regression models, non-parametric tests, Bland–Altman plots, and a random forest classifier were used for statistical analyses.ResultsThe final study cohort consisted of 90 patients (36 female, mean age 67.8 years [range 39–87]) with adrenal lesions (45 adenomas, 45 metastases). Compared to metastases, adrenal adenomas showed significantly lower CT-values in VNCConv and VNCPC (p = 0.007). Mean difference between VNC and true non-contrast (TNC) was 17.67 for VNCConv and 14.85 for VNCPC. Random forest classifier and logistic regression models both identified VNCConv and VNCPC as the best discriminators. When using 26 HU as the threshold in VNCConv reconstructions, adenomas could be discriminated from metastases with a sensitivity of 86.7% and a specificity of 75.6%.ConclusionVNC algorithms overestimate CT values compared to TNC in the assessment of adrenal lesions. However, they allow a reliable discrimination between adrenal adenomas and metastases and could be used in clinical routine in near future with an increased threshold (e.g., 26 HU). Further (multi-center) studies with larger patient cohorts and standardized protocols are required.Clinical relevance statementVNC reconstructions overestimate CT values compared to TNC. Using a different threshold (e.g., 26 HU compared to the established 10 HU), VNC has a high diagnostic accuracy for the discrimination between adrenal adenomas and metastases.Key Points• Virtual non-contrast reconstructions may be promising tools to differentiate adrenal lesions and might save further diagnostic tests.• The conventional and a new calcium-preserving virtual non-contrast algorithm tend to systematically overestimate CT-values compared to true non-contrast images.• Therefore, increasing the established threshold for true non-contrast images (e.g., 10HU) may help to differentiate between adrenal adenomas and metastases on contrast-enhanced CT.

Correction: Boosted Aquila Arithmetic Optimization Algorithm for multi-level thresholding image segmentation

Correction: Boosted Aquila Arithmetic Optimization Algorithm for multi-level thresholding image segmentation

Gold nanoparticle-coating reduces the acoustic pressure threshold for nanodroplet vaporization

Nanodroplets with a low-boiling-point perfluorocarbon (PFC) may vaporize spontaneously at physiological temperatures. Using a high-boiling-point PFC, e.g., perfluorohexane (PFH), can enable ultrasound-triggered vaporization for applications in imaging and drug delivery. However, PFH requires relatively high ultrasound (US) pressures for vaporization compared to low-boiling-point PFCs, making its use challenging. We investigated the feasibility of reducing the vaporization threshold by gold-coating lipid-encapsulated PFH nanodroplets (Au-PFH-NDs). We synthesized Au-PFH-NDs with 200 nm mean particle size and 5.12 × 10−4 pg mass of gold-coating per nanodroplet. B-mode images of the emulsion perfused in a flow phantom were used to determine the pressure threshold for ND vaporization upon exposure to 2 MHz focused US (f-number of 1.27, 0.5% duty cycle). The pressure threshold for Au-PFH-NDs (3.97 ± 0.63 MPa) was significantly lower (p < 0.05) than that of NDs without gold-coating (7.07 ± 0.02 MPa), indicating that gold-coating reduced the vaporization pressure threshold. In addition, the pressure threshold of the Au-PFH-NDs was not significantly different (p > 0.05) from that of perfluoropentane (PFP) NDs (4.16 ± 0.01 MPa). These results suggest that the Au-PFH-NDs can be vaporized at similar pressures as PFP NDs, but are more stable at physiological temperatures. These findings are the first step towards employing gold-coated PFC nanodroplets with a lower vaporization pressure threshold for multimodal imaging and drug delivery.

Integrated Automatic Optical Inspection and Image Processing Procedure for Smart Sensing in Production Lines.

An integrated automatic optical inspection (iAOI) system with a procedure was proposed for a printed circuit board (PCB) production line, in which pattern distortions and performance deviations appear with process variations. The iAOI system was demonstrated in a module comprising a camera and lens, showing improved supportiveness for commercially available hardware. The iAOI procedure was realized in a serial workflow of image registration, threshold setting, image gradient, marker alignment, and geometric transformation; furthermore, five operations with numerous functions were prepared for image processing. In addition to the system and procedure, a graphical user interface (GUI) that displays sequential image operation results with analyzed characteristics was established for simplicity. To demonstrate its effectiveness, self-complementary Archimedean spiral antenna (SCASA) samples fabricated via standard PCB fabrication and intentional pattern distortions were demonstrated. The results indicated that, compared with other existing methods, the proposed iAOI system and procedure provide unified and standard operations with efficiency, which result in scientific and unambiguous judgments on pattern quality. Furthermore, we showed that when an appropriate artificial intelligence model is ready, the electromagnetic characteristic projection for SCASAs can be simply obtained through the GUI.

Method of comparing and transforming images obtained using UAV

The subject matter of this article involves reviewing and developing methods for the comparison and transformation of images obtained using UAV via Computer Vision tools. The goal is to improve methods for image comparison and transformation. Various image-processing methods were employed to achieve the goal of this study,thereby contributing to the development of practical algorithms and approaches for image analysis and comparison. The tasks can be described as follows: 1) development of image comparison methods: design tools for the comparison of images from UAV that efficiently detect differences using algorithms such as cv2.absdiff and the PIL module; 2) Image transformation: implement transformation methods for images from UAV, including perspective transformation and thresholding, to enhance the quality and accuracy of image analysis. The methods used were algorithm development, image transformation methods, statistical analysis, experimental testing, and performance evaluation. The metrics used in this article are response time and accuracy. Algorithms for image comparison have also been refined, particularly those transformed through Global Threshold Value, Adaptive Mean Thresholding, and Adaptive Gaussian Thresholding. A novel change filtering method was introduced to enhance the precision of image comparison by filtering out insignificant alterations following image transformation. Comprehensive investigation of image comparison involving edge detection methods has been systematically presented. The results contain the development of practical algorithms and approaches for image analysis and comparison applicable in diverse areas such as military, security, and agriculture. Possibilities of applying our methods and algorithms in the context of drones were also considered, which is particularly relevant in tasks related to computer vision in unmanned aerial vehicles, where limited resources and the need for real-time processing of a large volume of data create unique challenges. Conclusions. The results contain OpenCV and PIL image comparison methods. OpenCV pixel-by-pixel comparison algorithm showed a better response time with the same accuracy. OpenCV method has 92,46% response time improvement compared with PIL and is 276ms. As for image thresholding with comparison, a method based on Global Threshold Value showed the shortest response time (266ms) and the lowest accuracy. The highest accuracy and response time (366ms) were obtained using the Adaptive Gaussian Thresholding method.

Fiber dispersion as a quality assessment metric for pultruded thermoplastic composites

Fiber-reinforced polymer composites are often assessed by measuring void content via thresholding of optical microscope cross-sectional images, relying on contrast to differentiate between voids and surrounding material. This is effective when dealing with primarily closed voids, a characteristic common to thermoset matrix composites. However, composites that exhibit fiber impregnation issues, often due to high matrix viscosity as in thermoplastic composites, will typically contain open pores which are infiltrated by the mounting resin used to embed the sample for grinding and polishing. Voids filled with this resin can be difficult to optically distinguish from the matrix, leading to inaccurate void content measurements. To circumvent this, a strategy for assessing impregnation quality based on fiber dispersion is proposed. During impregnation processes, the fiber network exhibits a tendency to become locally compacted by flow fronts, leading to low permeability regions with fiber clusters that are less likely to become fully impregnated. Fiber dispersion quantifies the percentage of non-clustered fibers from positional data and provides information on degree of impregnation and the locations of interfiber voids. The effects of processing speed and temperature on fiber dispersion are investigated in the production of continuous carbon fiber-reinforced polyetherimide (PEI) additive manufacturing feedstock material from commingled yarn using multi-die pultrusion. Fiber dispersion is observed to increase with slower pultrusion speeds, correlating with higher flexural strength and stiffness properties. This increase in fiber dispersion with longer die residency time indicates that the decompaction of fiber clusters is highly time-dependent and occurs by migration of fibers away from cluster boundaries.

Behavioral Discrimination in Chicken Flocks: A Camera-Based Movement Analysis for Distinguishing Normal and Scared Behaviors

Assessing chicken behavior in coops is crucial for high-quality meat production and stress prevention. This paper introduces an innovative system for evaluating behavior in chicken flocks by integrating collective area and its rate of change, surpassing traditional metrics like individual chicken distance walked, velocity, and acceleration. The novelty lies in simultaneously using multiple methods, providing a nuanced understanding of chicken responses and enhancing precision in behavior discrimination. The proposed system utilizes camera-based movement analysis, employing procedural image processing and data association for early detection and intervention in closed cage systems. This cost-effective solution is accessible to local farmers, contributing to improved poultry farming practices. The hardware setup includes a camera with a 470 nm bandpass lens, yielding computable results suitable for procedural system processes, especially in streamlining thresholding image processing.

Low-Cost Plant-Protection Unmanned Ground Vehicle System for Variable Weeding Using Machine Vision.

This study presents a machine vision-based variable weeding system for plant- protection unmanned ground vehicles (UGVs) to address the issues of pesticide waste and environmental pollution that are readily caused by traditional spraying agricultural machinery. The system utilizes fuzzy rules to achieve adaptive modification of the Kp, Ki, and Kd adjustment parameters of the PID control algorithm and combines them with an interleaved period PWM controller to reduce the impact of nonlinear variations in water pressure on the performance of the system, and to improve the stability and control accuracy of the system. After testing various image threshold segmentation and image graying algorithms, the normalized super green algorithm (2G-R-B) and the fast iterative threshold segmentation method were adopted as the best combination. This combination effectively distinguished between the vegetation and the background, and thus improved the accuracy of the pixel extraction algorithm for vegetation distribution. The results of orthogonal testing by selected four representative spraying duty cycles-25%, 50%, 75%, and 100%-showed that the pressure variation was less than 0.05 MPa, the average spraying error was less than 2%, and the highest error was less than 5% throughout the test. Finally, the performance of the system was comprehensively evaluated through field trials. The evaluation showed that the system was able to adjust the corresponding spraying volume in real time according to the vegetation distribution under the decision-making based on machine vision algorithms, which proved the low cost and effectiveness of the designed variable weed control system.

Boosted Aquila Arithmetic Optimization Algorithm for multi-level thresholding image segmentation

Boosted Aquila Arithmetic Optimization Algorithm for multi-level thresholding image segmentation

Gallbladder volvulus: Putting a spin on acute cholecystitis

First reported in 1898, gallbladder volvulus is rare. It occurs when the gallbladder twists on its longitudinal axis greater than 180 degrees, resulting in occlusion of the cystic vessels and duct. Herein, we describe the case of a 73-year old woman who was diagnosed with acalculus gallbladder volvulus, with the aid of computerised tomography. This was managed by urgent laparoscopic cholecystectomy and the patient continues to do well 15 months postoperatively. Although rare, a high index of suspicion of the condition must remain and a low threshold for imaging is needed to prevent the complications of gallbladder necrosis, perforation and biliary peritonitis.

Adipose Tissue Analysis Toolkit (ATAT) for automated analysis of adipocyte size and extracellular matrix in white adipose tissue.

The pathological expansion of white adipose tissue (WAT) in obesity involves adipocyte hypertrophy accompanied by expansion of the collagen-rich pericellular extracellular matrix (ECM) and development of crown-like structures (CLS). Traditionally, WAT morphology is assessed through immunohistochemical analysis of WAT sections. However, manual analysis of large histological sections is time-consuming, and the available digital tools for analyzing adipocyte size and pericellular ECM are limited. To address this gap, the authors developed the Adipose Tissue Analysis Toolkit (ATAT), an ImageJ plugin facilitating analysis of adipocyte size, WAT ECM, and CLS. ATAT utilizes local and image-level differentials in pixel intensity to independently threshold image background, distinguishing adipocyte-free tissue without user input. It accurately captures adipocytes in histological sections stained with common dyes and automates the analysis of adipocyte cross-sectional area, total-field, and localized region-of-interest ECM. ATAT allows fully automated batch analysis of histological images using default or user-defined adipocyte detection parameters. ATAT provides several advantages over existing WAT image analysis tools, enabling high-throughput analyses of adipocyte-specific parameters and facilitating the assessment of ECM changes associated with WAT remodeling due to weight changes and other pathophysiological alterations that affect WAT function.

Adaptive Segmentation Algorithm for Subtle Defect Images on the Surface of Magnetic Ring Using 2D-Gabor Filter Bank

In order to realize the unsupervised segmentation of subtle defect images on the surface of small magnetic rings and improve the segmentation accuracy and computational efficiency, here, an adaptive threshold segmentation method is proposed based on the improved multi-scale and multi-directional 2D-Gabor filter bank. Firstly, the improved multi-scale and multi-directional 2D-Gabor filter bank was used to filter and reduce the noise on the defect image, suppress the noise pollution inside the target area and the background area, and enhance the difference between the magnetic ring defect and the background. Secondly, this study analyzed the grayscale statistical characteristics of the processed image; the segmentation threshold was constructed according to the gray statistical law of the image; and the adaptive segmentation of subtle defect images on the surface of small magnetic rings was realized. Finally, a classifier based on a BP neural network is designed to classify the scar images and crack images determined by different threshold segmentation methods. The classification accuracies of the iterative method, the OTSU method, the maximum entropy method, and the adaptive threshold segmentation method are, respectively, 85%, 87.5%, 95%, and 97.5%. The adaptive threshold segmentation method proposed in this paper has the highest classification accuracy. Through verification and comparison, the proposed algorithm can segment defects quickly and accurately and suppress noise interference effectively. It is better than other traditional image threshold segmentation methods, validated by both segmentation accuracy and computational efficiency. At the same time, the real-time performance of our algorithm was performed on the advanced SEED-DVS8168 platform.

Airborne Hyperspectral Images and Machine Learning Algorithms for the Identification of Lupine Invasive Species in Natura 2000 Meadows

The mapping of invasive plant species is essential for effective ecosystem control and planning, especially in protected areas. One of the widespread invasive plants that threatens the species richness of Natura 2000 habitats in Europe is the large-leaved lupine (Lupinus polyphyllus). In our study, this species was identified at two Natura 2000 sites in southern Poland using airborne HySpex hyperspectral images, and support vector machine (SVM) and random forest (RF) classifiers. Aerial and field campaigns were conducted three times during the 2016 growing season (May, August, and September). An iterative accuracy assessment was performed, and the influence of the number of minimum noise fraction (MNF) bands on the obtained accuracy of lupine identification was analyzed. The highest accuracies were obtained for the August campaign using 30 MNF bands as input data (median F1 score for lupine was 0.82–0.85), with lower accuracies for the May (F1 score: 0.77–0.81) and September (F1 score: 0.78–0.80) campaigns. The use of more than 30 MNF bands did not significantly increase the classification accuracy. The SVM and RF algorithms allowed us to obtain comparable results in both research areas (OA: 89–94%). The method of the multiple classification and thresholding of frequency images allowed the results of many predictions to be included in the final map.

Road detection by using gradient magnitude image and adaptive thresholding based Watershed

In last decade, a lot of road detection algorithms are researched in remote sensing and aerial images, but they are not valuable for very dark and vague road images, hence, a new algorithm for road detection in a vaguer image is studied in this paper. It firstly uses a Gaussian filter to enhance the original image; then, an new edge detection algorithm is utilized to result in a gradient magnitude image by using different orientation information; subsequently, the road segments in the magnitude image is detected by applying a Watershed based algorithm, and a binary image is obtained; and last, in the binary image, the roads are finalized by removing noise in the image and filling gaps between the segments. To validate the proposed algorithm, the testing images were selected from both public dataset and our own dataset, and the testing results show that the new algorithm results are better than several traditional algorithms and recent semantic methods for the road detection, and the road detection accuracy between 92% and 95%. It is satisfactory for the road detection not only for normal images and also for the dark and vague road images. The new algorithm can be also applied into other applications, such as river detection and image enhancement, etc.

Receive coil quality assurance procedure and automated analysis for ViewRay MRIdian MR-Linac.

Regular receiving coil quality assurance (QA) is required to ensure image quality of an MRIdian Linac system. The manufacturer provides a spherical phantom and positioning tube for single-slice signal-to-noise ratio (SNR) and uniformity assessments. We aimed to improve imaging setup and coverage and eliminate inter-scan variability by employing multi-slice imaging of a stable phantom. Additionally, we strived to expedite analysis by developing objective, automated analysis software. A 5300mL cylindrical plastic bottle placed in plastic bins was scanned at isocenter using a spin-echo sequence with NEMA-recommended parameters and 18 axial slices, avoiding phantom repositioning. Acquisition was repeated with and without prescan normalization filtering and by saving uncombined element images. Obtained data were analyzed using custom open-source MATLAB code. Signal and noise images were automatically assigned, and ROIs for SNR and uniformity calculations were defined using image thresholding. SNR and uniformity pass/fail decisions were made using baseline comparisons. The proposed method was successfully implemented as monthly coil QA for 3.5 years. Setup and scanning took 41 min on average for a coil set. Automated image analysis was completed in a few minutes. Signal intensity peaked around +90 or -90mm for Torso or Head/Neck coil unfiltered images. Noise peaked and minimized SNR inside±30mm from isocenter, while maximizing it around±130mm. Prescan normalization smoothed signal response, reduced SNR and increased uniformity. Individual coil element image analysis identified their position, signal or noise response and SNR. SNR and uniformity pass/fail thresholds were set for already tested and new coils. Conspicuous and subtle Torso coil malfunctions were detected considering baseline deviations of combined and individual element results. Our QA method eliminated observer bias and provided insights into coil function, image filtering performance and coil element location. It provided SNR and uniformity thresholds and identified faulty coil elements.

Accurate multilevel thresholding image segmentation via oppositional Snake Optimization algorithm: Real cases with liver disease

Liver-related diseases significantly contribute to global mortality rates. Accurate segmentation of liver disease from CT scans is essential for early diagnosis and treatment selection, particularly in computer-aided diagnosis (CAD) systems. To address challenges posed by inconsistent liver presence and unclear boundaries, an enhanced Snake Optimization (SO) algorithm is proposed that integrates with opposition-based learning (OBL) called (SO-OBL), proving effective in global optimization and multilevel image segmentation. Experiments using CEC’2022 test functions compare SO-OBL with eleven recent and state-of-the-art metaheuristic algorithms, demonstrating its superior performance. Additionally, an advanced liver disease segmentation model based on SO-OBL incorporates an optimized multilevel thresholding technique, leveraging Otsu’s function. Notable segmentation metric results, including FSIM = 0.947, SSIM = 0.941, PSNR = 24.876, MSE = 236.88, and execution time = 0.281, underscore the model’s efficiency and potential for accurate diagnosis in CAD systems.

Thresholding of Polarimetric SAR Images of Coastal Zones Based on Three-Component Decomposition and Likelihood Ratio

Thresholding of Polarimetric SAR Images of Coastal Zones Based on Three-Component Decomposition and Likelihood Ratio