Multilevel Thresholding Method Research Articles

Multilevel thresholding with divergence measure and improved particle swarm optimization algorithm for crack image segmentation

Crack formation is a common phenomenon in engineering structures, which can cause serious damage to the safety and health of these structures. An important method of ensuring the safety and health of engineered structures is the prompt detection of cracks. Image threshold segmentation based on machine vision is a crucial technology for crack detection. Threshold segmentation can separate the crack area from the background, providing convenience for more accurate measurement and evaluation of the crack condition and location. The segmentation of cracks in complex scenes is a challenging task, and this goal can be achieved by means of multilevel thresholding. The arithmetic-geometric divergence combines the advantages of the arithmetic mean and the geometric mean in probability measures, enabling a more precise capture of the local features of an image in image processing. In this paper, a multilevel thresholding method for crack image segmentation based on the minimum arithmetic-geometric divergence is proposed. To address the issue of time complexity in multilevel thresholding, an enhanced particle swarm optimization algorithm with local stochastic perturbation is proposed. In crack detection, the thresholding criterion function based on the minimum arithmetic-geometric divergence can adaptively determine the thresholds according to the distribution characteristics of pixel values in the image. The proposed enhanced particle swarm optimization algorithm can increase the diversity of candidate solutions and enhance the global convergence performance of the algorithm. The proposed method for crack image segmentation is compared with seven state-of-the-art multilevel thresholding methods based on several metrics, including RMSE, PSNR, SSIM, FSIM, and computation time. The experimental results show that the proposed method outperforms several competing methods in terms of these metrics.

Comparison of the suitability of the otsu method thresholding and multilevel thresholding for flower image segmentation

The digital representation of flowers, characterized by their vivid chromatic attributes, establishes them as viable candidates for deployment as input imagery within the object recognition paradigm. Within the context of object recognition, the imperative of a proficient image segmentation process is underscored, serving to effectively discern the object from its background and, consequently, optimizing the efficacy of the object recognition process. This research unfolds through a methodologically structured tripartite framework, encompassing the initial stage involving input imagery, the subsequent intermediate phase dedicated to image segmentation, and a conclusive stage centered on the quantitative evaluation of methodological outcomes. The second stage, focusing on image segmentation, employs the Otsu thresholding and multilevel thresholding methods. The subsequent third stage involves a thorough assessment of segmentation outcomes through the application of quantitative metrics, including Peak signal-to-oise ratio (PSNR) and Root Mean Square Error (RMSE). Empirical investigations, incorporating a diverse array of floral input images, reveal a conspicuous inclination towards a specific segmentation methodology. Specifically, the Otsu Thresholding method emerges as the more judicious choice relative to multilevel Thresholding, demonstrating superior performance with a diminished RMSE value and an augmented PSNR value, substantiated by an average RMSE value. This research is propelled by the overarching objective of discerning the most optimal method for the segmentation of flower images, particularly in the face of diverse input images. Its significant contribution lies in providing nuanced insights into the discerning selection of segmentation methodologies, attuned to the variability inherent in diverse forms of input imagery, thereby culminating in optimized outcomes within the domain of flower image recognition. Where did these results come from? please show it in the sub-discussion.

An efficient multilevel image thresholding method based on improved heap-based optimizer

Image segmentation is the process of separating pixels of an image into multiple classes, enabling the analysis of objects in the image. Multilevel thresholding (MTH) is a method used to perform this task, and the problem is to obtain an optimal threshold that properly segments each image. Methods such as the Kapur entropy or the Otsu method, which can be used as objective functions to determine the optimal threshold, are efficient in determining the best threshold for bi-level thresholding; however, they are not effective for MTH due to their high computational cost. This paper integrates an efficient method for MTH image segmentation called the heap-based optimizer (HBO) with opposition-based learning termed improved heap-based optimizer (IHBO) to solve the problem of high computational cost for MTH and overcome the weaknesses of the original HBO. The IHBO was proposed to improve the convergence rate and local search efficiency of search agents of the basic HBO, the IHBO is applied to solve the problem of MTH using the Otsu and Kapur methods as objective functions. The performance of the IHBO-based method was evaluated on the CEC’2020 test suite and compared against seven well-known metaheuristic algorithms including the basic HBO, salp swarm algorithm, moth flame optimization, gray wolf optimization, sine cosine algorithm, harmony search optimization, and electromagnetism optimization. The experimental results revealed that the proposed IHBO algorithm outperformed the counterparts in terms of the fitness values as well as other performance indicators, such as the structural similarity index (SSIM), feature similarity index (FSIM), peak signal-to-noise ratio. Therefore, the IHBO algorithm was found to be superior to other segmentation methods for MTH image segmentation.

Insulator fault diagnosis based on multi-objectives multilevel thresholding method and boost particle swarm optimization

Insulator fault diagnosis based on multi-objectives multilevel thresholding method and boost particle swarm optimization

The variance entropy multi-level thresholding method

The variance entropy multi-level thresholding method

Menghitung Jumlah Sel Goblet Usus Ayam Secara Otomatis Dengan Metode Multilevel Thresholding

Goblet cell was an indicator the health of chicken intestine that was strongly influence of chicken productivity. So far, the determination of goblet cell was conducted by visual used of microscope. These methods required a long time and also the result was subjective value. The aim of this study was image processed for calculating the number of goblet cells in the chicken intestine and assessed the method applied performance. The material used was 30 images of broiler intestine preparations with AB-PAS staining. The research was carried out used multilevel thresholding and labeling methods for calculating the number of goblet cells in intestine of chicken. Chicken intestinal tissue that had been prepared with AB-PAS staining was acquired used a microscope equipped with a camera and connected to a PC. The result of the acquisition was an RGB scale image, so needed to be converted into an HSV scale image. Next, conducted the extraction process the hue component to convert the image to a gray scale. The gray scale image can be used in the process of identifying goblet cells using the multilevel thresholding method. Objects in the form of goblet cells that have been separated from other tissues were then repaired using morphological operations. The results of morphological operations were then calculated using the labeling method. The results showed that the method applied was successful in automatically calculated the number of goblet cells. The results of calculations using the multilevel thresholding and labeling method had accuracy rate of 90% compared with the results of calculated of visually directly by analyst.

Hybrid Multilevel Thresholding Image Segmentation Approach for Brain MRI.

A brain tumor is an abnormal growth of tissues inside the skull that can interfere with the normal functioning of the neurological system and the body, and it is responsible for the deaths of many individuals every year. Magnetic Resonance Imaging (MRI) techniques are widely used for detection of brain cancers. Segmentation of brain MRI is a foundational process with numerous clinical applications in neurology, including quantitative analysis, operational planning, and functional imaging. The segmentation process classifies the pixel values of the image into different groups based on the intensity levels of the pixels and a selected threshold value. The quality of the medical image segmentation extensively depends on the method which selects the threshold values of the image for the segmentation process. The traditional multilevel thresholding methods are computationally expensive since these methods thoroughly search for the best threshold values to maximize the accuracy of the segmentation process. Metaheuristic optimization algorithms are widely used for solving such problems. However, these algorithms suffer from the problem of local optima stagnation and slow convergence speed. In this work, the original Bald Eagle Search (BES) algorithm problems are resolved in the proposed Dynamic Opposite Bald Eagle Search (DOBES) algorithm by employing Dynamic Opposition Learning (DOL) at the initial, as well as exploitation, phases. Using the DOBES algorithm, a hybrid multilevel thresholding image segmentation approach has been developed for MRI image segmentation. The hybrid approach is divided into two phases. In the first phase, the proposed DOBES optimization algorithm is used for the multilevel thresholding. After the selection of the thresholds for the image segmentation, the morphological operations have been utilized in the second phase to remove the unwanted area present in the segmented image. The performance efficiency of the proposed DOBES based multilevel thresholding algorithm with respect to BES has been verified using the five benchmark images. The proposed DOBES based multilevel thresholding algorithm attains higher Peak Signal-to-Noise ratio (PSNR) and Structured Similarity Index Measure (SSIM) value in comparison to the BES algorithm for the benchmark images. Additionally, the proposed hybrid multilevel thresholding segmentation approach has been compared with the existing segmentation algorithms to validate its significance. The results show that the proposed algorithm performs better for tumor segmentation in MRI images as the SSIM value attained using the proposed hybrid segmentation approach is nearer to 1 when compared with ground truth images.

Two-Stage Feature Selection Method Created for 20 Neurons Artificial Neural Networks for Automatic Breast Cancer Detection

Breast cancer is a common deadly diseases in women. Initial recognition of breast cancer using mammogram images is a challenging task. Hence, this paper proposed a unique automatic diagnosis model for breast cancer. Initially, the mammogram images are preprocessed with a median filter and contrast limited adaptive histogram equalization (CLAHE). The pre-processed image is automatically segmented using the multilevel threshold method. Subsequently, statistical, texture, shape, and geometric features are extracted from the segmented image. So, the length of the feature vector is high, and it is important to identify optimum features. In this paper, the dimension of the feature vector is reduced by 2-stage feature selection methods. Initially, the feature vector is applied to the best first search method information gain (IG) with rank feature method, and then secondly, apply the Pearson correlation method (PCM). Artificial neural networks (ANNs) are used to increase the classification accuracy of a breast cancer diagnosis. In this model, the selection of appropriate neurons in a single hidden layer is used to avoid overfitting problems in an ANN model. Based on optimum feature selection, the appropriate number of neurons chosen in the hidden layer is 20, which was applied for the proposed IG+PCM+Boosted-ANN model. The proposed model is applied on 2 regular datasets mini-Mammographic Image Analysis Society (mini-MIAS) and Digital Database for Screening Mammography (DDSM). The proposed model was superior to other exiting models and the model in this study achieves the accuracy of 99 and 98.80 % for mini-MIAS and DDSM datasets, respectively.

Harris hawks optimization for COVID-19 diagnosis based on multi-threshold image segmentation.

Digital image processing techniques and algorithms have become a great tool to support medical experts in identifying, studying, diagnosing certain diseases. Image segmentation methods are of the most widely used techniques in this area simplifying image representation and analysis. During the last few decades, many approaches have been proposed for image segmentation, among which multilevel thresholding methods have shown better results than most other methods. Traditional statistical approaches such as the Otsu and the Kapur methods are the standard benchmark algorithms for automatic image thresholding. Such algorithms provide optimal results, yet they suffer from high computational costs when multilevel thresholding is required, which is considered as an optimization matter. In this work, the Harris hawks optimization technique is combined with Otsu's method to effectively reduce the required computational cost while maintaining optimal outcomes. The proposed approach is tested on a publicly available imaging datasets, including chest images with clinical and genomic correlates, and represents a rural COVID-19-positive (COVID-19-AR) population. According to various performance measures, the proposed approach can achieve a substantial decrease in the computational cost and the time to converge while maintaining a level of quality highly competitive with the Otsu method for the same threshold values.

Analysis of Multilevel Otsu Thresholding for Corrosion Rate of Anodised AZ91D Magnesium Alloy ​

Magnesium and its alloy are renowned for their high strength-to-weight ratio, excellent machinability and good castability, making it a favourable material for manufacturing various automotive parts. But when exposed to the environment, magnesium and its alloy have a weak ability to resist corrosion. AZ91D magnesium alloy was anodised and immersed in a salt solution to accelerate deterioration. The image of the corroded surface resulting from the immersion test was analysed by quantising the corroded regions. This paper studies the feasibility of an image processing approach using the multilevel Otsu thresholding method by quantising the corroded region on the AZ91D magnesium alloy. This method converts the original colour image to a greyscale image and then converts it to a binary image and RGB image for the multilevel thresholding. The pixel distributions from the multilevel Otsu thresholding display a trend that, with a higher corrosion rate, the pixel’s frequency of occurrence shifted more to the darker regions. This conforms with the Otsu thresholding, where the darker region is identified as the corroded area of the AZ91D magnesium alloy. By implementing the negative image, the binary image is segmented into non-corroded and corroded areas and labelled as 0 (black) and 1 (white,) respectively. As for multilevel Otsu thresholding, the RGB image is assigned in three colours representing different corrosion severity regions. The regions of the corrosion severity are categorised into severe corrosion, moderate corrosion and low corrosion, coloured in dark blue, light blue, and yellow, respectively. Thus, the trend of the corrosion activity can be seen by implementing the multilevel Otsu thresholding and correlating it with the corrosion rate value. Furthermore, obtaining data on corrosion attack severity can provide helpful information in predicting a component’s failure and future maintenance planning.

Novel Square Error Minimization-Based Multilevel Thresholding Method for COVID-19 X-Ray Image Analysis Using Fast Cuckoo Search

Coronavirus outbreaks in 2019 (COVID-19) have been a huge disaster in the fields of health, economics, education, and tourism in the last two years. For diagnosis, a quick interpretation of the COVID-19 chest X-ray image is required. There is also a strong need to find an efficient multiclass segmentation technique for the analysis of COVID-19 X-ray images. Most of the threshold selection techniques are entropy-based. Nevertheless, these techniques suffer from their dependencies on the spatial distribution of grey values. To tackle these issues, a novel non-entropic threshold selection method is proposed, which is the primary key contribution having found a new source of information to the biomedical image processing field. The firsthand Square Error (SE)-based objective function is suggested. The second key contribution is the new optimizer called Fast Cuckoo Search (FCS), which is useful and brings novel ideas into the subject, used to optimize the suggested objective functions for computing the optimal thresholds. To ensure a faster convergence with a quality optimal solution, we include extra exploitation together with a chance factor. The FCS is validated using the well-known classical and CEC 2014 benchmark test functions, which shows a significant improvement over its predecessors—Adaptive Cuckoo Search (ACS) and other state-of-the-art optimizers. Further, the SE minimization-based optimal multilevel thresholding method using the FCS, coined as SE-FCS, is proposed. To experiment, images are considered from the Kaggle Radiography database. We have compared its performances with Tsallis, Kapur’s, and Masi entropy-based techniques using well-known segmentation metrics and achieved a performance increase of 2.95%, 5.51% and 10.50%, respectively. The proposed method shows superiority using Friedman’s mean rank statistical test and ranked first.

An adaptive multilevel thresholding method with chaotically-enhanced Rao algorithm.

Multilevel image thresholding is a well-known technique for image segmentation. Recently, various metaheuristic methods have been proposed for the determination of the thresholds for multilevel image segmentation. These methods are mainly based on metaphors and they have high complexity and their convergences are comparably slow. In this paper, a multilevel image thresholding approach is proposed that simplifies the thresholding problem by using a simple optimization technique instead of metaphor-based algorithms. More specifically, in this paper, Chaotic enhanced Rao (CER) algorithms are developed where eight chaotic maps namely Logistic, Sine, Sinusoidal, Gauss, Circle, Chebyshev, Singer, and Tent are used. Besides, in the developed CER algorithm, the number of thresholds is determined automatically, instead of manual determination. The performances of the developed CER algorithms are evaluated based on different statistical analysis metrics namely BDE, PRI, VOI, GCE, SSIM, FSIM, RMSE, PSNR, NK, AD, SC, MD, and NAE. The experimental works and the related evaluations are carried out on the BSDS300 dataset. The obtained experimental results demonstrate that the proposed CER algorithm outperforms the compared methods based on PRI, SSIM, FSIM, PSNR, RMSE, AD, and NAE metrics. In addition, the proposed method provides better convergence regarding speed and accuracy.

Selection of Optimal Thresholds in Multi-Level Thresholding Using Multi-Objective Emperor Penguin Optimization for Precise Segmentation of Mammogram Images

In medical image examination, image segmentation is the broadly used method. Currently, the efficient segmentation of mammogram images is the main challenge. Many methods were presented for segmenting the mammogram images, but the results are not satisfactory. In this paper, an efficient segmentation of mammogram images-based Multilevel Thresholding (MLT) method is proposed. Initially, the preprocessing step is executed for eliminating the unnecessary noises. For gaining the useful features from the mammogram images, mammogram image segmentation is carried out using multilevel thresholding method. In this paper, a novel Multi-Objective Emperor Penguin Optimization (MOEPO) algorithm is proposed for searching the multilevel greatest thresholds that segment the images into background and objects. The objective functions of the MLT are Otsu’s method, Kapur and Tsallis entropy. The effectiveness of the proposed method is analyzed using several performances evaluating metrics, like PSNR, FSIM and SSIM. The experimental outcomes show that the performance of the proposed technique is superior to other state-of-the-art methods. The proposed technique is likened to three existing models, viz. ScPSO-MT, Double Threshold and IWO-SUSAN. The SSIM of the proposed technique is 24.99%, 27.83% and 26.95% better than ScPSO-MT, Double Threshold and IWO-SUSAN existing approaches. The PSNR of the proposed technique is 25.27%, 40% and 50.74% better than ScPSO-MT, Double Threshold and IWO-SUSAN approaches. The FSIM of the proposed technique is 28.57%, 34.12% and 34.12% better than ScPSO-MT, Double Threshold and IWO-SUSAN methods.

Analisis Karakterisasi Penyakit pada Tanaman Pisang Menggunakan Kamera Termal dengan Metode Tresholding

Abstract — Banana is a fruit plant that is widely produced in Indonesia. Unfortunately, this plant is verysusceptible to diseases which can reduce the quality and quantity of the crop. This paper proposes diseasedetection in banana plants using a thermal camera. The detection is carried out using image processingtechniques with multilevel thresholding methods. The image is captured using a thermal camera, then theimage is preprocessed to suit what is desired. After that, so that the position is the same as the image takenusing a digital camera, the image produced by the thermal camera is carried out by an image registrationprocess. The image processing result is compared with the ground truth image obtained from a digitalcamera to determine the effectiveness of the proposed method. The effectiveness of the proposed method ismeasured using the parameters Recall, Precision, F-measure, and Accuracy. The effectiveness of theproposed method is effective enough to produce a recall value of 76.65%, Precision 58.38%, F-measure64.68%, and Accuracy 62.87%. Keywords— Banana Plant Disease, Thermal Image, RGB Image, Multi Thresholding, EffectivenessMeasurement.

Differential exponential entropy-based multilevel threshold selection methodology for colour satellite images using equilibrium-cuckoo search optimizer

Recently, the entropic based multilevel threshold selection methods use 2D histogram, which is constructed using the local averages, leading to a loss of edges. Further, the computation of the entropy using the diagonal pixel values only leads to a loss of information. Nevertheless, traditional 2D histogram based multilevel thresholding methods suffer from efficiently retaining the spatial correlation information. In addition, the conventional entropy uses logarithmic function, which has inherent problems, thereby, reducing the accuracy at some situations. To solve these issues, a differential exponential entropy (DEE) -based multilevel threshold selection methodology is proposed. To suppress the high magnitude peaks in the 2D histogram, the normalized local variance is used while the construction. A novel objective function is suggested to compute the DEE. A new Equilibrium-Cuckoo Search Optimizer (ECSO) is suggested to maximize the DEE. For testing, standard benchmark functions are used. The results are compared with the physics-based Equilibrium Optimizer (EO) and the nature-inspired Cuckoo Search Algorithm (CSA). Different benchmark colour satellite images are acquired from the Landsat Image Gallery database for the experiment. The performances are compared with the state-of-the-art methods. Different metrics such as PSNR, SSIM and FSIM are used for the image quality assessment. A statistical analysis is presented in terms of the Box plots. Our proposed DEE-ECSO outperforms the other techniques. The suggested algorithm would be useful for segmentation of the brain MR images for biomedical engineering applications.

Analysis Of Banana Plant Disease Characterization Using Thermal Camera With Tressolding Method

Banana is a fruit plant that is widely produced in Indonesia. Unfortunately, this plant is very susceptible to diseases which can reduce the quality and quantity of the crop. This paper proposes disease detection in banana plants using a thermal camera. The detection is carried out using image processing techniques with multilevel thresholding methods. The image is captured using a thermal camera, then the image is preprocessed to suit what is desired. After that, so that the position is the same as the image taken using a digital camera, the image produced by the thermal camera is carried out by an image registration process. The image processing result is compared with the ground truth image obtained from a digital camera to determine the effectiveness of the proposed method. The effectiveness of the proposed method is measured using the parameters Recall, Precision, F-measure, and Accuracy. The effectiveness of the proposed method is quite effective because it produces parameter values above 80%, namely the recall value of 86,59%, the Precision of 99,1%, the F-measure of 92%, and the accuracy of 89,78%.

An Interval Iteration Based Multilevel Thresholding Algorithm for Brain MR Image Segmentation.

In this paper, we propose an interval iteration multilevel thresholding method (IIMT). This approach is based on the Otsu method but iteratively searches for sub-regions of the image to achieve segmentation, rather than processing the full image as a whole region. Then, a novel multilevel thresholding framework based on IIMT for brain MR image segmentation is proposed. In this framework, the original image is first decomposed using a hybrid L1 − L0 layer decomposition method to obtain the base layer. Second, we use IIMT to segment both the original image and its base layer. Finally, the two segmentation results are integrated by a fusion scheme to obtain a more refined and accurate segmentation result. Experimental results showed that our proposed algorithm is effective, and outperforms the standard Otsu-based and other optimization-based segmentation methods.

Modified Artificial Ecosystem-Based Optimization for Multilevel Thresholding Image Segmentation

Multilevel thresholding is one of the most effective image segmentation methods, due to its efficiency and easy implementation. This study presents a new multilevel thresholding method based on a modified artificial ecosystem-based optimization (AEO). The differential evolution (DE) is applied to overcome the shortcomings of the original AEO. The main idea of the proposed method, artificial ecosystem-based optimization differential evolution (AEODE), is to employ the operators of the DE as a local search of the AEO to improve the ecosystem of solutions. We used benchmark images to test the performance of the AEODE, and we compared it to several existing approaches. The proposed AEODE achieved a high performance when evaluated by the structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and fitness values. Moreover, the AEODE outperformed the basic version of the AEO concerning SSIM and PSNR by 78% and 82%, respectively, which reserves the best features for each of AEO and DE.

A Novel Evolutionary Arithmetic Optimization Algorithm for Multilevel Thresholding Segmentation of COVID-19 CT Images

One of the most crucial aspects of image segmentation is multilevel thresholding. However, multilevel thresholding becomes increasingly more computationally complex as the number of thresholds grows. In order to address this defect, this paper proposes a new multilevel thresholding approach based on the Evolutionary Arithmetic Optimization Algorithm (AOA). The arithmetic operators in science were the inspiration for AOA. DAOA is the proposed approach, which employs the Differential Evolution technique to enhance the AOA local research. The proposed algorithm is applied to the multilevel thresholding problem, using Kapur’s measure between class variance functions. The suggested DAOA is used to evaluate images, using eight standard test images from two different groups: nature and CT COVID-19 images. Peak signal-to-noise ratio (PSNR) and structural similarity index test (SSIM) are standard evaluation measures used to determine the accuracy of segmented images. The proposed DAOA method’s efficiency is evaluated and compared to other multilevel thresholding methods. The findings are presented with a number of different threshold values (i.e., 2, 3, 4, 5, and 6). According to the experimental results, the proposed DAOA process is better and produces higher-quality solutions than other comparative approaches. Moreover, it achieved better-segmented images, PSNR, and SSIM values. In addition, the proposed DAOA is ranked the first method in all test cases.

An opposition equilibrium optimizer for context-sensitive entropy dependency based multilevel thresholding of remote sensing images

Earlier 1D histogram-based entropic methods for multilevel image thresholding suffer from the lack of contextual information. Subsequently, the idea was extended for 2D histogram-based methods, where neighbourhood pixels were considered to retain the contextual information. Nevertheless, 2D histogram-based entropic methods are computationally intensive. Moreover, these methods are based on the maximization of entropic functions using an optimizer, leading to less accuracy. To address these issues, we propose a context-sensitive entropy dependency (CSED) based multilevel thresholding method. A new optimizer called opposition equilibrium optimizer (OEO) is introduced. The opposition based learning and escaping strategy are incorporated to enhance exploration capability. Here, 31 test functions including 8 from standard testbed IEEE CEC 2014 are used for validation. The merits are – i) reduced complexity, ii) improved accuracy, iii) better stability and scalability, iv) enhanced exploration capability, v) well suited to random problems with changing dimensions, etc. The search history, trajectory, and average fitness history of the OEO are explicitly discussed. The Box plots and the convergence curves are presented to confirm its stability and faster convergence. Friedman's mean rank test, Bonferroni-Dunn test, and Holm's test are also carried out to ensure OEO superiority over others. Encouraging thresholding results on high dimensional colour satellite images from the Landsat image gallery are shown based on the suggested method (CSED-OEO). The quantitative measures (Peak Signal to Noise Ratio, Feature Similarity Index, and Structure Similarity Index) are used for validation. The CSED-OEO is compared with state-of-the-art methods and found better. It means, realistically, the method may be useful for segmentation-based analysis of colour images.