Multilevel Thresholding Approach Research Articles

Efficient Approach to Color Image Segmentation Based on Multilevel Thresholding Using EMO Algorithm by Considering Spatial Contextual Information.

The process of image segmentation is partitioning an image into its constituent parts and is a significant approach for extracting interesting features from images. Over a couple of decades, many efficient image segmentation approaches have been formulated for various applications. Still, it is a challenging and complex issue, especially for color image segmentation. To moderate this difficulty, a novel multilevel thresholding approach is proposed in this paper based on the electromagnetism optimization (EMO) technique with an energy curve, named multilevel thresholding based on EMO and energy curve (MTEMOE). To compute the optimized threshold values, Otsu's variance and Kapur's entropy are deployed as fitness functions; both values should be maximized to locate optimal threshold values. In both Kapur's and Otsu's methods, the pixels of an image are classified into different classes based on the threshold level selected on the histogram. Optimal threshold levels give higher efficiency of segmentation; the EMO technique is used to find optimal thresholds in this research. The methods based on an image's histograms do not possess the spatial contextual information for finding the optimal threshold levels. To abolish this deficiency an energy curve is used instead of the histogram and this curve can establish the spatial relationship of pixels with their neighbor pixels. To study the experimental results of the proposed scheme, several color benchmark images are considered at various threshold levels and compared with other meta-heuristic algorithms: multi-verse optimization, whale optimization algorithm, and so on. The investigational results are illustrated in terms of mean square error, peak signal-to-noise ratio, the mean value of fitness reach, feature similarity, structural similarity, variation of information, and probability rand index. The results reveal that the proposed MTEMOE approach overtops other state-of-the-art algorithms to solve engineering problems in various fields.

Adaptive Color Quantization Method with Multi-level Thresholding

In this study, a novel color quantization approach which automatically estimates the number of colors by multi-level thresholding based on the histogram is proposed. The method consists of three stages. First, red–green–blue is clustered by threshold values. Thus, the pixels are positioned in a cluster or sub-prism. Second, the color palette is produced by determining the centroids of the clusters. Finally, the pixels are reassigned to clusters based on their distance from each centroid. The average of the pixels included in each cluster also represents the color of that cluster. While conventional methods are user-dependent, the proposed algorithm automatically generates the number of colors by considering the pixels assigned to the clusters. Additionally, the multi-level thresholding approach is also a solution to the initialization problem, which is another important issue for quantization. Consequently, the experimental results of the method tested with various images show better performance than many frequently used quantization techniques.

A novel minimum generalized cross entropy-based multilevel segmentation technique for the brain MRI/dermoscopic images

BackgroundOne of the challenging and the primary stages of medical image examination is the identification of the source of any disease, which may be the aberrant damage or change in tissue or organ caused by infections, injury, and a variety of other factors. Any such condition related to skin or brain sometimes advances in cancer and becomes a life-threatening disease. So, an efficient automatic image segmentation approach is required at the initial stage of medical image analysis. PurposeTo make a segmentation process efficient and reliable, it is essential to use an appropriate objective function and an efficient optimization algorithm to produce optimal results. MethodThe above problem is resolved in this paper by introducing a new minimum generalized cross entropy (MGCE) as an objective function, with the inclusion of the degree of divergence. Another key contribution is the development of a new optimizer called opposition African vulture optimization algorithm (OAVOA). The proposed optimizer boosted the exploration, skill by inheriting the opposition-based learning. The resultsThe experimental work in this study starts with a performance evaluation of the optimizer over a set of standards (23 numbers) and IEEE CEC14 (8 numbers) Benchmark functions. The comparative analysis of test results shows that the OAVOA outperforms different state-of-the-art optimizers. The suggested OAVOA-MGCE based multilevel thresholding approach is carried out on two different types of medical images – Brain MRI Images (AANLIB dataset), and dermoscopic images (ISIC 2016 dataset) and found superior than other entropy-based thresholding methods.

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.

M. Masi Entropy- and Grey Wolf Optimizer-Based Multilevel Thresholding Approach for Image Segmentation

M. Masi Entropy- and Grey Wolf Optimizer-Based Multilevel Thresholding Approach for Image Segmentation

Restoration of deteriorated text sections in ancient document images using a tri-level semi-adaptive thresholding technique

The proposed research aims to restore deteriorated text sections that are affected by stain markings, ink seepages and document ageing in ancient document photographs, as these challenges confront document enhancement. A tri-level semi-adaptive thresholding technique is developed in this paper to overcome the issues. The primary focus, however, is on removing deteriorations that obscure text sections. The proposed algorithm includes three levels of degradation removal as well as pre- and post-enhancement processes. In level-wise degradation removal, a global thresholding approach is used, whereas, pseudo-colouring uses local thresholding procedures. Experiments on palm leaf and DIBCO document photos reveal a decent performance in removing ink/oil stains whilst retaining obscured text sections. In DIBCO and palm leaf datasets, our system also showed its efficacy in removing common deteriorations such as uneven illumination, show throughs, discolouration and writing marks. The proposed technique directly correlates to other thresholding-based benchmark techniques producing average F-measure and precision of 65.73 and 93% towards DIBCO datasets and 55.24 and 94% towards palm leaf datasets. Subjective analysis shows the robustness of proposed model towards the removal of stains degradations with a qualitative score of 3 towards 45% of samples indicating degradation removal with fairly readable text. Highlights This work presents a semi-adaptive binarization technique for ancient image enhancement. Main focus of this work is to restore obscured text sections. Multi-level thresholding approach is used for the removal of degradations. Gradient of the original image is used in the computation of reference image to detect deteriorated text sections. Pseudo-colouring and post-enhancement process finally transform to the enhanced image. DIBCO and palm leaf document samples are used for experimentations.

A novel threshold score based multiclass segmentation technique for brain magnetic resonance images using adaptive opposition slime mold algorithm

AbstractClinicians can detect diseases early, thanks to the digital image processing methodologies, which improve health together with the treatment experience. The technology of magnetic resonance imaging (MRI) is frequently employed in the brain, research for any kind of related illness. The brain MR image requires precise automated thresholding for a meaningful representation to aid doctors, because of its different modalities and complexity. The majority of the threshold selection strategies are based on entropy. However, these strategies are limited by their reliance on the spatial distribution of gray values. There is also a pressing need to develop a thresholding technique that is independent of the spatial distribution, making it more suitable for a variety of modalities and complexity, such as the brain MR images. A novel non‐entropic maximizing objective function for the multilevel thresholding approach using a threshold score (TS) is presented in this paper, to address these concerns. An evolutionary TS‐AOSMA approach, using the optimizer called adaptive opposition slime mold algorithm (AOSMA), is suggested to lower the computational cost of TS‐based multiclass segmentation, which is a novel idea. The proposed approach is evaluated on T2‐weighted brain MR imaging slices from Harvard Medical School's whole brain atlas dataset. When compared to the state‐of‐the‐art Kapur's, Tsallis, and Masi entropy‐based technologies, the proposed scheme offered better quantitative and qualitative outcomes. The recommended strategies may be useful in medical image analysis.

A fuzzy entropy, Contourlet based automatic fault detection

Seismic fault interpretation has great importance in characterising subsurface geology. However, it is in general a manual and time-consuming task, thus, adaptive methods to achieve good fault detection results would be valuable. In this paper, we present a new method using combination of Contourlet transform and fuzzy entropy definition to adaptively detect and extract faults from seismic data. Contourlet is a multiscale and multidirectional filter that has high directionality which decomposes an image to various subscales. Our proposed scheme has 3 phases: first, employing Contourlet to pull out fault information using multidirectional and multiscale property of contourlet (feature extraction), second, using differentiation to boost fault information and calculating the correlation coefficient between the input image and subscales, fault information can be isolated from reflectors adaptively (feature selection) and third, applying a multi-level thresholding approach built on fuzzy partition of the histogram and entropy theory to classify image pixels into fault and non-fault (classification). The adaptive hybrid technique was applied to one synthetic and two real datasets containing fault data, reflectors and random noise. According to the results and their assessments, the proposed scheme has desirably located fault features in the data. We also examined the effect of random noise (Signal to Noise Ratio (SNR) = 2) on our adaptive algorithm which showed the success of our designed technique in the presence of noise.

Synthesis and Comparison of Improved Edge Detection Technique Based on Metaheuristic and Intelligent Algorithm Optimization

The edge is the most significant information in image processing applications. Moreover, and the accurate and continue edge commonly leads to accurate related steps like object tracking and region clustering. In fact, it is the first step of image analysis and understanding. The accuracy edge detection results have an impact on the comprehension machine system. In this paper we present various improved edge detection techniques by our research team, of similar color and grey level images, using the information theory approach based on other energy information inspired from Shannon entropy and utilizing as well the metaheuristic and intelligent method combined with multilevel thresholding approach in various color spaces, and like the ant colony optimization with the graph cut approach for indexing images before the segmentation step. In addition, particular swarm optimization is done, and finally the fuzzy technique is used. The effectiveness and accuracy of these approaches are evaluated by many metric measurements and compared with the common operators. The PR metric, has a significant mean value (about 20) than PR of Canny operator (about 9). And also, we can denote that all improved techniques achieve significant results with ameliorated running time.

An improved symbiotic organisms search algorithm for higher dimensional optimization problems

The symbiotic organisms search (SOS) algorithm was introduced by considering the relationships among the creatures in a natural ecosystem. Despite the superior efficiency of SOS, it has been observed that fixing benefit factors of mutualism phase at 1 or 2; the algorithm obstructs itself from an extensive and diverse search of the search region. Moreover, alteration of random dimensions in the parasitism phase increases the computational burden of the algorithm. Considering these limitations, a modified SOS algorithm, namely nwSOS, has been proposed in this study to solve higher dimensional optimization problems. In the suggested nwSOS, the benefit factors are calculated by a non-linear approach. The mutual vector is modified, and weights of both benefit factors are utilized to effectively explore and exploit the search region. Moreover, the parasitism phase is tailored to lessen the computational overhead. The modified method is then used to evaluate twenty basic benchmark functions using 100 and 500 dimensions. Results are compared with six state-of-the-art algorithms and with SOS and its five modified variants. Four designing issues from both unconstrained and constrained classifications are solved utilizing nwSOS. Complexity analysis, statistical analysis, and convergence analysis are executed to measure the algorithm’s effectiveness from different aspects. Moreover, the proposed algorithm has been used for segmenting COVID-19 chest X-ray images with the help of multi-level thresholding approach using different thresholds. All the results confirmed the enhancement of the proposed algorithm.

COVID-19 X-ray image segmentation by modified whale optimization algorithm with population reduction

Coronavirus disease 2019 (COVID-19) has caused a massive disaster in every human life field, including health, education, economics, and tourism, over the last year and a half. Rapid interpretation of COVID-19 patients' X-ray images is critical for diagnosis and, consequently, treatment of the disease. The major goal of this research is to develop a computational tool that can quickly and accurately determine the severity of an illness using COVID-19 chest X-ray pictures and improve the degree of diagnosis using a modified whale optimization method (WOA). To improve the WOA, a random initialization of the population is integrated during the global search phase. The parameters, coefficient vector (A) and constant value (b), are changed so that the algorithm can explore in the early stages while also exploiting the search space extensively in the latter stages. The efficiency of the proposed modified whale optimization algorithm with population reduction (mWOAPR) method is assessed by using it to segment six benchmark images using multilevel thresholding approach and Kapur's entropy-based fitness function calculated from the 2D histogram of greyscale images. By gathering three distinct COVID-19 chest X-ray images, the projected algorithm (mWOAPR) is utilized to segment the COVID-19 chest X-ray images. In both benchmark pictures and COVID-19 chest X-ray images, comparisons of the evaluated findings with basic and modified forms of metaheuristic algorithms supported the suggested mWOAPR's improved performance.

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 efficient method to minimize cross-entropy for selecting multi-level threshold values using an improved human mental search algorithm

The minimum cross-entropy (MCIT) is introduced as a multi-level image thresholding approach, but it suffers from time complexity, in particular, when the number of thresholds is high. To address this issue, this paper proposes a novel MCIT-based image thresholding based on improved human mental search (HMS) algorithm, a recently proposed population-based metaheuristic algorithm to tackle complex optimisation problems. To further enhance the efficacy, we improve HMS algorithm, IHMSMLIT, with four improvements, including, adaptively selection of the number of mental searches instead of randomly selection, proposing one-step k-means clustering for region clustering, updating based on global and personal experiences, and proposing a random clustering strategy. To assess our proposed algorithm, we conduct an extensive set of experiments with several state-of-the-art and the most recent approaches on a benchmark set of images and in terms of several criteria including objective function, peak signal to noise ratio (PSNR), feature similarity index (FSIM), structural similarity index (SSIM), and stability analysis. The obtained results apparently demonstrate the competitive performance of our proposed algorithm.

Improving the segmentation of magnetic resonance brain images using the LSHADE optimization algorithm

Segmentation is an essential preprocessing step in techniques for image analysis. The automatic segmentation of brain magnetic resonance imaging has been exhaustively investigated since the accurate use of this kind of methods permits the diagnosis and identification of several diseases. Thresholding is a straightforward and efficient technique for image segmentation. Nonetheless, thresholding based approaches tend to increase the computational cost based on the number of thresholds used for the segmentation. Therefore, metaheuristic algorithms are an important tool that helps to find the optimal values in multilevel thresholding. The adaptive differential evolution, based in numerous successes through history, with linear population size reduction (LSHADE) is a robust metaheuristic algorithm that efficiently solves numerical optimization problems. The main advantage of LSHADE is its capability to adapt its internal parameters according to prior knowledge acquired along the evolutionary process. Meanwhile, the continuous reduction of the population improves the exploitation process. This article presents a multilevel thresholding approach based on the LSHADE method for the segmentation of magnetic resonance brain imaging. The proposed method has been tested using three groups of reference images— the first group consists of grayscale standard benchmark images, the second group consists of magnetic resonance T2-weighted brain images, and the third group is formed by images of unhealthy brains affected by tumors. In turn, the performance of the intended approach was compared with distinct metaheuristic algorithms and machine learning methods. The statistically verified results demonstrate that the suggested approach improves consistency and segmentation quality.

Multilevel thresholding and Morphological Relationship Approach for Automatic Detection of Anterior and Posterior Commissure in Mid-sagittal Brain MRI

Multilevel thresholding and Morphological Relationship Approach for Automatic Detection of Anterior and Posterior Commissure in Mid-sagittal Brain MRI

Cauchy with whale optimizer based eagle strategy for multi-level color hematology image segmentation

Pathological color image segmentation is an exigent procedure due to the existence of imperceptibly correlated, and indistinct multiple regions of concern. Multi-level thresholding has been introduced as one of the most significant image segmentation procedures for pathological analysis. However, finding an optimal set of threshold values is an extremely time-consuming task, and crucially depends on the objective function criterion. In order to solve these problems, this paper presents a multi-level hematology color image thresholding approach with the assistance of a two-stage strategy called Eagle Strategy coupled with Whale Optimization Algorithm (ES-WOA), analyzing the performance over five well-known objective functions, namely; Kapur’s entropy, Fuzzy entropy, Tsallis’ entropy, Otsu’s method, and Cross entropy. A rigorous comparative study is performed among classical WOA and existing eagle strategy based optimization algorithms, considering a set of hematology color images, and common performance indexes evaluated by each objective function tested. Experimental results indicate that proposed ES-WOA in combination with Tsallis’ entropy outperforms the rest of tested algorithms in terms of computational effort, image segmentation quality, and robustness. For example, ES-WOA with Tsallis’ produces segmented images with average Peak Signal-to-Noise Ratio (PSNR) values 16.0371, 17.9975, 21.1353, and 23.0759 for threshold values 2, 3, 4, and 5, respectively, which are superior to other tested methods. Additionally, the numerical results are statistically validated using a nonparametric approach to eliminate the random effect in the obtained results.

An Improved Marine Predators Algorithm With Fuzzy Entropy for Multi-Level Thresholding: Real World Example of COVID-19 CT Image Segmentation

Medical imaging techniques play a critical role in diagnosing diseases and patient healthcare. They help in treatment, diagnosis, and early detection. Image segmentation is one of the most important steps in processing medical images, and it has been widely used in many applications. Multi-level thresholding (MLT) is considered as one of the simplest and most effective image segmentation techniques. Traditional approaches apply histogram methods; however, these methods face some challenges. In recent years, swarm intelligence methods have been leveraged in MLT, which is considered an NP-hard problem. One of the main drawbacks of the SI methods is when searching for optimum solutions, and some may get stuck in local optima. This because during the run of SI methods, they create random sequences among different operators. In this study, we propose a hybrid SI based approach that combines the features of two SI methods, marine predators algorithm (MPA) and moth-?ame optimization (MFO). The proposed approach is called MPAMFO, in which, the MFO is utilized as a local search method for MPA to avoid trapping at local optima. The MPAMFO is proposed as an MLT approach for image segmentation, which showed excellent performance in all experiments. To test the performance of MPAMFO, two experiments were carried out. The first one is to segment ten natural gray-scale images. The second experiment tested the MPAMFO for a real-world application, such as CT images of COVID-19. Therefore, thirteen CT images were used to test the performance of MPAMFO. Furthermore, extensive comparisons with several SI methods have been implemented to examine the quality and the performance of the MPAMFO. Overall experimental results confirm that the MPAMFO is an efficient MLT approach that approved its superiority over other existing methods.

A context sensitive multilevel thresholding using swarm based algorithms

In this paper, a comprehensive energy function is used to formulate the three most popular objective functions: Kapur&#x02BC s, Otsu and Tsalli&#x02BC s functions for performing effective multilevel color image thresholding. These new energy based objective criterions are further combined with the proficient search capability of swarm based algorithms to improve the efficiency and robustness. The proposed multilevel thresholding approach accurately determines the optimal threshold values by using generated energy curve, and acutely distinguishes different objects within the multi-channel complex images. The performance evaluation indices and experiments on different test images illustrate that Kapur&#x02BC s entropy aided with differential evolution and bacterial foraging optimization algorithm generates the most accurate and visually pleasing segmented images.

A multi-level image thresholding approach using Otsu based on the improved invasive weed optimization algorithm

Multi-level thresholding is one of the most popular techniques in image segmentation. However, selecting the optimal thresholds with high accuracy and efficiency is still challenging. In this paper, a novel multi-level thresholding method using between-class variance (Otsu) based on an improved invasive weed optimization algorithm (FIWO) is proposed. In the FIWO algorithm, the forking technique of the lightning search algorithm is introduced to guarantee the quality of the initial population and to enhance the exploration of the algorithm. In addition, the current best solution swing operation is used to obtain the optimal thresholds with a fast convergence rate. Comparative experiments are carried out to test the performance of FIWO. The results show that the proposed FIWO algorithm is able to achieve better segmented images with fewer iterations than those of the simulated annealing algorithm, gravitational search algorithm, whale optimization algorithm and traditional invasive weed optimization algorithm.

Clusters identification and meso-scale structures in a circulating fluidized bed based on image processing

To understand the behaviors of particle clusters in the circulating fluidized bed (CFB), experiments were conducted with glass beads to acquire the image sequences of gas-solid flow on a CFB riser with a 100 mm × 25 mm cross-section and 3.2 m in length by adopting high-speed photography. An image multilevel thresholding approach using k-means algorithm was applied to perform image segmentation to identify clusters as well as core clusters in the riser, automatically. Cluster characteristics, such as the density and the number of clusters were obtained subsequently. The results show the image segmentation method based on k-means algorithm has made some improvement in terms of precision and systematicness for cluster identification. In addition, the internal structure of the cluster was analysed. Collectively, the cluster always consists of a dense core with highest solids holdup surrounded by a relatively dilute cloud with no clear boundary. High solids holdup enhances the cluster formation. On the contrary, the core cluster disappears at low solids holdup condition, indicating the cluster is only composed of cluster cloud in this case. Furthermore, based on the present experimental data, the correlations between the cluster density and the local time-mean solids holdup are presented.