Threshold-based Image Segmentation Research Articles

An experimentation of objective functions used for multilevel thresholding based image segmentation using particle swarm optimization

An experimentation of objective functions used for multilevel thresholding based image segmentation using particle swarm optimization

CNN-IKOA: convolutional neural network with improved Kepler optimization algorithm for image segmentation: experimental validation and numerical exploration

Chest diseases, especially COVID-19, have quickly spread throughout the world and caused many deaths. Finding a rapid and accurate diagnostic tool was indispensable to combating these diseases. Therefore, scientists have thought of combining chest X-ray (CXR) images with deep learning techniques to rapidly detect people infected with COVID-19 or any other chest disease. Image segmentation as a preprocessing step has an essential role in improving the performance of these deep learning techniques, as it could separate the most relevant features to better train these techniques. Therefore, several approaches were proposed to tackle the image segmentation problem accurately. Among these methods, the multilevel thresholding-based image segmentation methods won significant interest due to their simplicity, accuracy, and relatively low storage requirements. However, with increasing threshold levels, the traditional methods have failed to achieve accurate segmented features in a reasonable amount of time. Therefore, researchers have recently used metaheuristic algorithms to tackle this problem, but the existing algorithms still suffer from slow convergence speed and stagnation into local minima as the number of threshold levels increases. Therefore, this study presents an alternative image segmentation technique based on an enhanced version of the Kepler optimization algorithm (KOA), namely IKOA, to better segment the CXR images at small, medium, and high threshold levels. Ten CXR images are used to assess the performance of IKOA at ten threshold levels (T-5, T-7, T-8, T-10, T-12, T-15, T-18, T-20, T-25, and T-30). To observe its effectiveness, it is compared to several metaheuristic algorithms in terms of several performance indicators. The experimental outcomes disclose the superiority of IKOA over all the compared algorithms. Furthermore, the IKOA-based segmented CXR images at eight different threshold levels are used to train a newly proposed CNN model called CNN-IKOA to find out the effectiveness of the segmentation step. Five performance indicators, namely overall accuracy, precision, recall, F1-score, and specificity, are used to disclose the CNN-IKOA’s effectiveness. CNN-IKOA, according to the experimental outcomes, could achieve outstanding outcomes for the images segmented at T-12, where it could reach 94.88% for overall accuracy, 96.57% for specificity, 95.40% for precision, and 95.40% for recall.

Toward Real-Time Animal Tracking with Integrated Stimulus Control for Automated Conditioning in Aquatic Eco-Neurotoxicology.

Aquatic eco-neurotoxicology is an emerging field that requires new analytical systems to study the effects of pollutants on animal behaviors. This is especially true if we are to gain insights into one of the least studied aspects: the potential perturbations that neurotoxicants can have on cognitive behaviors. The paucity of experimental data is partly caused by a lack of low-cost technologies for the analysis of higher-level neurological functions (e.g., associative learning) in small aquatic organisms. Here, we present a proof-of-concept prototype that utilizes a new real-time animal tracking software for on-the-fly video analysis and closed-loop, external hardware communications to deliver stimuli based on specific behaviors in aquatic organisms, spanning three animal phyla: chordates (fish, frog), platyhelminthes (flatworm), and arthropods (crustacean). The system's open-source software features an intuitive graphical user interface and advanced adaptive threshold-based image segmentation for precise animal detection. We demonstrate the precision of animal tracking across multiple aquatic species with varying modes of locomotion. The presented technology interfaces easily with low-cost and open-source hardware such as the Arduino microcontroller family for closed-loop stimuli control. The new system has potential future applications in eco-neurotoxicology, where it could enable new opportunities for cognitive research in diverse small aquatic model organisms.

Gumbel (EVI)-Based Minimum Cross-Entropy Thresholding for the Segmentation of Images with Skewed Histograms

In this study, we delve into the realm of image segmentation, a field characterized by a multitude of approaches; one frequently used technique is thresholding-based image segmentation. This process divides intensity levels into different regions based on a specified threshold value. Minimum Cross-Entropy Thresholding (MCET) stands out as an independent objective function that can be applied with any distribution and is regarded as a mean-based thresholding method. In certain cases, images exhibit diverse structures that result in different histogram distributions. Some images possess symmetric histograms, while others feature asymmetric ones. Traditional mean-based thresholding methods are well-suited for symmetric image histograms, relying on Gaussian distribution definitions for mean estimations. However, in situations involving asymmetric distributions, such as left and right-skewed histograms, a different approach is required. In this paper, we propose the utilization of a Maximum Likelihood Estimation (MLE) of Gumbel’s distribution or Extreme Value Type I (EVI) distribution for the objective function of an MCET. Our goal is to introduce a dedicated image-thresholding model designed to enhance the accuracy and efficiency of image-segmentation tasks. This model determines optimal thresholds for image segmentation, facilitating precise data analysis for specific image types and yielding improved segmentation results by considering the impact of mean values on thresholding objective functions. We compare our proposed model with original methods and related studies in the literature. Our model demonstrates better performance in terms of segmentation accuracy, as assessed through both unsupervised and supervised evaluations for image segmentation.

Simplified expression and recursive algorithm of multi-threshold Tsallis entropy

Image segmentation is an important step in obtaining image information, and it has always been an extremely critical link in the fields of computer vision and pattern recognition. Because of its simplicity and effectiveness, threshold-based image segmentation technology has received significant attention and research by researchers. Tsallis entropy and Renyi entropy are two important global threshold selection methods in image thresholding. They not only have a special correspondence in mathematical expression, but also have a certain relationship in the field of image segmentation. Considering the number of thresholds increases, the complexity of the expression of Tsallis entropy criterion function also increases sharply, which is difficult to apply in multiple thresholds. Given this, this paper proposes a simplified expression of Tsallis entropy with multiple thresholds, which is proved by mathematical induction. Simultaneously, it is concluded that the multiple-threshold segmentation of Tsallis entropy and Renyi entropy with the same parameters is equivalent. To overcome the inconvenience of high calculation cost under multi-threshold and to improve calculation efficiency, recursive algorithms based on simplified Tsallis entropy and Renyi entropy under multi-threshold are given. The calculation costs of the traditional Tsallis entropy and Renyi entropy threshold, simplified Tsallis entropy and Renyi entropy threshold, and the recursive simplified Tsallis entropy and Renyi entropy are compared through experiments. The results have revealed that the calculation time of simplified Tsallis entropy and Renyi entropy is shorter than the traditional methods, and the calculation time of the recursive simplified Tsallis entropy and Renyi entropy is much lower than that of the simplified expression. Finally, we use the three optimization algorithms including particle swarm, differential evolution and Harris hawks algorithm to solve the optimal solutions of the six expressions in the case of multilevel thresholds. Experiments also show that recursive simplified Tsallis entropy and Renyi entropy have the lowest calculation time cost under the same parameters.

An Efficient Multilevel Threshold Segmentation Method for Breast Cancer Imaging Based on Metaheuristics Algorithms: Analysis and Validations

Breast cancer is a hazardous disease that should be seriously tackled to reduce its danger in all aspects of the world. Therefore, several imaging ways to detect this disease were considered, but the produced images need to be accurately processed to effectively detect it. Image segmentation is an indispensable step in image processing to segment the homogenous regions that have similar features such as brightness, color, texture, contrast, form, and size. Several techniques like region-based, threshold-based, edge-based, and feature-based clustering have been developed for image segmentation; however, thresholding, which is divided into two classes: bilevel and multilevel, won the highest attention by the researchers due to its simplicity, ease of use and accuracy. The multilevel thresholding-based image segmentation is difficult to be tackled using traditional techniques, especially with increasing the threshold level; therefore, the researchers pay attention to the metaheuristic algorithms which could overcome several hard problems in a reasonable time. In this paper, a new hybrid metaheuristic algorithm based on integrating the jellyfish search algorithm with an effective improvement method is proposed for segmenting the color images of breast cancer, namely the hybrid jellyfish search algorithm HJSO. Experiments are extensively performed to appear the superiority of the proposed algorithm, including validating its performance using various breast cancer images and conducting an extensive comparison with several rival algorithms to explore its effectiveness. The experimental findings, including various performance metrics like fitness values, CPU time, Peak signal-to-noise ratio (PSNR), standard deviation, Features similarity index (FSIM), and Structural similarity index (SSIM), totally show the efficiency of HJSO.

Elucidation of infection asperity of CT scan images of COVID-19 positive cases: A Machine Learning perspective

Owing to the profoundly irresistible nature of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, an enormous number of individuals halt in the line for Computed Tomography (CT) scan assessment, which overburdens the medical practitioners, radiologists, and adversely influences the patient's remedy, diagnosis, as well as restraint the epidemic. Medical facilities like intensive care systems and mechanical ventilators are restrained due to highly infectious diseases. It turns out to be very imperative to characterize the patients as per their asperity levels. This article exhibited a novel execution of a threshold-based image segmentation technique and random forest classifier for COVID-19 contamination asperity identification. With the help of the image segmentation model and machine learning classifier, we can identify and classify COVID-19 individuals into three asperity classes such as early, progressive, and advanced, with an accuracy of 95.5% using chest CT scan image database. Experimental outcomes on an adequately enormous number of CT scan images exhibit the adequacy of the machine learning mechanism developed and recommended to identify coronavirus severity.

Big Data Analytics with Optimal Deep Learning Model for Medical Image Classification

In recent years, huge volumes of healthcare data are getting generated in various forms. The advancements made in medical imaging are tremendous owing to which biomedical image acquisition has become easier and quicker. Due to such massive generation of big data, the utilization of new methods based on Big Data Analytics (BDA), Machine Learning (ML), and Artificial Intelligence (AI) have become essential. In this aspect, the current research work develops a new Big Data Analytics with Cat Swarm Optimization based deep Learning (BDA-CSODL) technique for medical image classification on Apache Spark environment. The aim of the proposed BDA-CSODL technique is to classify the medical images and diagnose the disease accurately. BDA-CSODL technique involves different stages of operations such as preprocessing, segmentation, feature extraction, and classification. In addition, BDA-CSODL technique also follows multi-level thresholding-based image segmentation approach for the detection of infected regions in medical image. Moreover, a deep convolutional neural network-based Inception v3 method is utilized in this study as feature extractor. Stochastic Gradient Descent (SGD) model is used for parameter tuning process. Furthermore, CSO with Long Short-Term Memory (CSO-LSTM) model is employed as a classification model to determine the appropriate class labels to it. Both SGD and CSO design approaches help in improving the overall image classification performance of the proposed BDA-CSODL technique. A wide range of simulations was conducted on benchmark medical image datasets and the comprehensive comparative results demonstrate the supremacy of the proposed BDA-CSODL technique under different measures.

Improved Seismic Residual Diffracted Multiple Suppression Method Based on Object Detection and Image Segmentation

Seismic multiple is one of the most common noises in marine seismic data, which heavily affects subsequent processing and interpretation. To eliminate the influence of seismic multiples, many methods have been developed, while surface-related multiple elimination (SRME) is one of the most widely deployed methods. However, results of SRME always contain a few strong residual diffracted multiples (RDMs) in practice because of the unprecise prediction of diffracted multiples compared to reflection multiples. If we try to apply further multiple suppression methods to SRME results, it not only tends to damage the signals, but also spends lots of unnecessary computations where there is no RDM. In this article, we propose an improved RDM suppression method based on object detection and image segmentation. First, we employ an object detection network to locate bounding boxes containing RDMs in the SRME results. Then a threshold-based image segmentation method is utilized to identify regions of strong RDMs in the detected boxes. According to the segmentation results, parameters for weak multiples and strong multiples are provided for the adaptive multiple subtraction (AMS) in different regions to generate different results. At last, we combine the suppression results of strong RDMs and weak RDMs as the final results. Application on field data demonstrates that our method is able to suppress RDMs with little loss of signal.

A fast and reliable method for semi-automated planimetric quantification of dental plaque in clinical trials.

To develop a simple and reproducible method for semi-automated planimetric quantification of dental plaque. Plaque from 20 healthy volunteers was disclosed using erythrosine, and fluorescence images of the first incisors, first premolars, and first molars were recorded after 1, 7, and 14 days of de novo plaque formation. The planimetric plaque index (PPI) was determined using a semi-automated threshold-based image segmentation algorithm and compared with manually determined PPI and the Turesky modification of the Quigley-Hein plaque index (TM-QHPI). The decrease of tooth autofluorescence in plaque-covered areas was quantified as an index of plaque thickness (TI). Data were analysed by analysis of variance (ANOVA) and Pearson correlations. The high contrast between teeth, disclosed plaque, and soft tissues in fluorescence images allowed for a fast threshold-based image segmentation. Semi-automated PPI is strongly correlated with manual planimetry (r=0.92; p < .001) and TM-QHPI recordings (r=0.88; p < .001), and may exhibit a higher discriminatory power than TM-QHPI due to its continuous scale. TI values corresponded to optically perceived plaque thickness, and no differences were observed over time (p > .05, ANOVA). The proposed semi-automated planimetric analysis based on fluorescence images is a simple and efficient method for dental plaque quantification in multiple images with reduced human input.

Spatial Heterogeneity of Excess Lung Fluid in Cystic Fibrosis: Generalized, Localized Diffuse, and Localized Presentations

Magnetic resonance (MR) imaging has demonstrated that CF subjects have a significantly higher lung density (e.g., fluid content) when compared with healthy control subjects, but, at present, there are no techniques to quantify the spatial presentation of these lung abnormalities. The excess fluid in MR lung images for CF subjects with mild (n = 4), moderate (n = 5), and severe (n = 4) disease and age- and sex-matched healthy controls (n = 13) in both the right and left lungs was identified and quantified using a thresholding-based image segmentation technique using healthy controls as a baseline. MR lung images were categorized into one of three spatial presentation groups based on their regional and global percent area of the lung covered by excess fluid (i.e., spatial distribution): (i) generalized for sparse, (ii) localized diffuse for a moderate focality, and (iii) localized for a strong focality. A total of 96% of the controls presented as generalized. CF subjects populated all three presentation groups and an individual’s right and left lungs did not always categorize identically. The developed metrics for categorization provide a quantification method to describe the spatial presentation of CF disease and suggests the heterogeneous nature of the disease.

Image Segmentation under the Optimization Algorithm of Krill Swarm and Machine Learning.

This study aims to improve the efficiency and accuracy of image segmentation, and to compare and study traditional threshold-based image segmentation methods and machine learning model-based image segmentation methods. The krill herb optimization algorithm is combined with the traditional maximum between-class variance function to form a new graph segmentation algorithm. The pet dataset is used to train the algorithm model and build an image semantic segmentation system. The results show that when the traditional Ostu algorithm performs image single-threshold segmentation, the number of iterations is about 256. When double-threshold segmentation is performed, the number of iterations increases exponentially, and the execution time is about 2 s. The number of iterations of the improved Krill Herd algorithm in single-threshold segmentation is 6.95 times, respectively. The execution time for double-threshold segmentation is about 0.24 s. The number of iterations is only improved by a factor of 0.19. The average classification accuracy of the Unet network model and the SegNet network model is 86.3% and 91.9%, respectively. The average classification accuracy of the DC-Unet network model reaches 93.1%. This shows that the proposed fusion algorithm has high optimization efficiency and stronger practicability in multithreshold image segmentation. The DC-Unet network model can improve the image detail segmentation effect. The research provides a new idea for finding an efficient and accurate image segmentation method.

Method for quantifying the reaction degree of slag in alkali-activated cements using deep learning-based electron microscopy image analysis.

In this paper, we present a methodology for measuring the reaction degree of ground granulated blast furnace slag (GGBFS) in alkali-activated cements using neural network based image analysis. The new methodology consists of an image analysis routine in which the segmentation of the back scattered electron (BSE) (SEM) images is based on a deep learning U-net. This methodology was applied to and developed for NaOH-activated slag cements and validated against independently measured XRD results. In a next step the developed method was applied to NaOH-Na2 SO4 -activated systems, to check the broader applicability. The neural networks based image analysis results were shown to correlate well with the XRD results. Once the model was trained, it segmented images fast and accurately. Furthermore, the model trained on the NaOH-activated systems was readily applicable on NaOH-Na2 SO4 -activated system indicating that the model generalises well. As such, the developed methodology and models can be more performant and robust than conventional threshold-based image segmentation. The method's accuracy, replicability and transferability make it a promising tool for material analysis and characterisation.

A novel image processing technique for detection of pseudo occluded bubbles and identification of flow regimes in a bubble column reactor

Pseudo bubble occlusion is a challenging situation for an image analysis tool as it involves a 2D projection of a 3D object. In this work, a robust algorithm has been developed to detect the bubbles from pseudo occlusion. Images of the flow are captured using a high-speed camera. The algorithm employed for bubble detection comprises Adaptive Threshold-based Image Segmentation which comprises segmentation, edge detection, segment grouping, and ellipse fitting. The bubble size is detected and the bubble size distributions for different air flow rates are obtained. The regime identification has been done based on the cell averaged projected void fraction, bubble dimensions, and the distribution of the bubbles. As the superficial air velocity increases in the range of 0–2 cm/s, bubble break up becomes prominent, and hence, the number of smaller size bubbles increases.

An Introspective Performance Analysis of Threshold-based Segmentation Techniques on Digital Mammograms

Image segmentation, as a pre-processing step, plays a vital role in medical image analysis. The variants of threshold-based image segmentation methods are proved to offer feasible and optimal solutions to extract the region of interest (RoI), from medical images. Digital mammograms are used as a reliable source of breast cancer prognosis and diagnosis. Thresholding is a simple and effective strategy that finds applications in image processing and analysis. This research aimed to analyze the performance behaviour of a few threshold-based segmentation methods with respect to the intensity distribution of the input mammograms. For this analytical research, six automated thresholding segmentation techniques were chosen: Kapur, Otsu’s, Isoentropic, Ridler &amp; Calvard’s, Kittler &amp; Illingworth's, and Yen. The performance and behaviour of those methods were validated on the digital mammogram images of mini-MIAS database featured with Fatty (F), Fatty-Glandular (G), and Dense-Glandular (D) breast tissues. Those methods were analyzed on two metrics viz., Region Non-Uniformity (RNU) and computation time. The results of this research confirm that Ridler &amp; Calvard’s method gives the best segmentation results for Dense-Glandular, Isoentropic method gives better segmentation results for Fatty and Yen method works well on the Fatty-Glandular normal mammogram images.

Multi-level Image Segmentation using Randomized Spiral-based Whale Optimization Algorithm

Background: Segmenting an image into multiple regions is a pre-processing phase of computer vision. For the same, determining an optimal set of thresholds is a challenging problem. Objective: This paper introduces a novel multi-level thresholding based image segmentation method. Methods: The presented method uses a novel variant of whale optimization algorithm to determine the optimal thresholds. For experimental validation of the proposed variant, twenty-three benchmark functions are considered. To analyze the efficacy of new multi-level image segmentation method, images from Berkeley Segmentation Dataset and Benchmark (BSDS300) have been considered and tested against recent multi-level image segmentation methods. Conclusion: Experiments arm that the presented method is efficient and competitive than the existing multi-level image segmentation methods.

A novel chaotic symbiotic organisms search optimization in multilevel image segmentation

Multilevel thresholding-based image segmentation plays a vital role in image processing. It significantly impacts many applications, such as remote sensing, pattern recognition, and medical image diagnosis. Premature convergence due to stuck into the local optima is the main challenge of any evolutionary algorithm-based multilevel image thresholding. Most of the evolutionary algorithms use their stochastic property to comprehensively utilize the search space, which strongly influences premature convergence. This paper presents a novel chaotic symbiotic organisms search (CSOS) optimization for multilevel image segmentation that maintains a strategic distance from premature convergence and improves the performance of conventional symbiotic organisms search (SOS) optimization in multilevel image segmentation. We have analyzed the performance of the proposed CSOS using state-of-the-art entropies such as Kapur’s, Tsallis’, Renyi’s, and Masi’s entropy as objective functions. The experiments on standard used color images are presented to establish the practicality of the proposed algorithm. The results show that the CSOS algorithm with Masi’s entropy is more effective and has wide adaptability to the high-dimensional optimization problems than the other recently proposed algorithms considered in this paper.

OABC: An Otsu-Artificial Bee Colony Multilevel Image Threshold Optimization for Liver Tumor Segmentation

Grey scale or color image conversion involves thresholding technique which can be either at single or multiple threshold levels. Resultant images have reduced pixel level classes. As level of thresholding required increases, the process becomes complex involving exhaustive search and thereby increasing computational resources and time complexity. Meta-heuristic optimization algorithms show promise to reduce time-complexity, especially the Artificial Bee Colony (ABC) algorithm mimicking honeybee foraging activities. It provides optimal results when sampling a large solution space. Since threshold-based image segmentation is a multidimensional discreet optimization problem, for fitness evaluation of threshold level candidates, Otsu’s thresholding method has been widely employed. In this paper, proposed is an Otsu-ABC multilevel image thresholding model for CT images using MATLAB for Liver tumor segmentation. The developed model is evaluated for increased dimensionality and computation time. It is also benchmarked against other commonly used algorithms. Results demonstrate that the proposed model is immune to dimensionality increase and outperforms all other models with regards to quality of images segmented and computation time proving that the model is ideal for optimizing multilevel image thresholding problems involving large thresholds for complex computer vision problems in medical diagnostics, biometrics, surveillance, satellite imagery and gaming.

Optimization and assessment of MWCNT dispersion in polypropylene nanocomposite films

Functionalized carbon nanotubes dispersion in polypropylene is optimized and investigated using thresholding based image segmentation. Carbon nanotubes polypropylene films are developed using solution casting method and MWCNT dispersion is optimized upto 1% MWCNT concentration. Dispersion state is investigated by thresholding based image segmentation and agglomeration estimation method applied on scanning electron microscopy (SEM) images. K-means clustering algorithm is applied further to validate the thresholding algorithm by calculating the MWCNT weight % in processed SEM images. It is found that the proposed clustering algorithm can detect MWCNT concentration significantly upto 1%. To further validate the dispersion studies, developed nanocomposite films are used to fabricate the dragonfly inspired artificial flapping wings. The natural frequency was found maximum for 1% MWCNT-COOH-PP wing at 52.27 Hz.

Multilevel thresholding based image segmentation using new multistage hybrid optimization algorithm

Thresholding is one of the highly accepted methods for image segmentation because of its simplicity in nature. The selection of optimal threshold values in threshold-based image segmentation is a tricky job. In this work, Kapur’s entropy is used to solve the optimal threshold selection problem and a multistage hybrid nature-inspired optimization algorithm is used to get the best possible parameters for this objective function. The proposed method has three stages namely: primary stage, booster stage and final stage. Particle swarm optimization (PSO), artificial bee colony optimization (ABC) and ant colony optimization (ACO) used at these stages. In this proposed work various benchmarked images have been used for experimentation purpose. The proposed method has been assessed and performance is compared with well-known metaheuristic optimization like PSO, ABC, ACO, classical Otsu thresholding method and modified bacterial foraging optimization qualitatively and quantitatively. Peak signal to noise ratio and Structure Similarity Index are used for qualitative assessment. Wilcoxon p value test, ANOVA test and box plots are used for statistical analysis. The experimental results showed that the proposed method performed better in terms of quality and consistency.