Bi-level Thresholding Research Articles

Multi-Level Thresholding Color Image Segmentation Using Modified Gray Wolf Optimizer.

The success of image segmentation is mainly dependent on the optimal choice of thresholds. Compared to bi-level thresholding, multi-level thresholding is a more time-consuming process, so this paper utilizes the gray wolf optimizer (GWO) algorithm to address this issue and enhance accuracy. To acquire the optimal thresholds at different levels, we modify the GWO (MGWO) in terms of leader selection, position update, and mutation. We also use the Otsu method and Kapur entropy as objective functions. The performance of MGWO is compared with other color image segmentation algorithms on ten images from the BSD500 dataset in terms of objective values, variance, signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and feature similarity index (FSIM). Experimental and non-parametric statistical analyses demonstrate that MGWO performs excellently in the multi-level thresholding segmentation of color images.

Leveraging local neighborhood features in 3D unstructured point cloud data for geometry-based automated damage delineation

In a post-earthquake environment, manual inspection is resource-intensive and subjective. This article presents a real-world damage assessment study of the Improved Iterative Bi-Level Thresholding (IIBLT) algorithm. Damaged regions are delineated using IIBLT based on the local point curvature. The methodology is tested using a unique dataset: Mono Temporal Disaster City 3D Dataset (C3DO). C3DO is a point cloud data generated by a 3D reconstruction of a controlled post-earthquake environment, which presents challenges for concrete façade damage assessment algorithms. Results from IIBLT demonstrated a 16% increase in Matthews Correlation Coefficient (MCC) and Intersection over Union (mGIoU), compared to the current state-of-the-art methods. This corresponds to improving accuracy and completeness in defect region identification without the dependence on knowledge of damaged regions. Results from this study highlight the novel contributions of the IIBLT algorithm including the elimination of parameter tuning, and demonstrate the applicability of the novel iterative framework for concrete surface defect delineation at large.

Optimal planning of safety improvements on road sites belonging to different categories within large networks: An integrated multi-layer framework

Planning road safety interventions on large road networks implies several layers of complexity in the decision-making process. In fact, the following simultaneous problems should be addressed: estimating safety performances on the different road elements of the network, identifying sites showing high potential for improvement with respect to reference values, defining the possible types of safety measures to be implemented and their anticipated effect on traffic safety, limiting the number of interventions given fixed budget constraints.This study proposes an integrated multi-layer framework which takes into account the above-defined problems into a single optimization procedure which provides the number and type of safety interventions to be implemented over a wide road network composed of different categories of road elements. The proposed framework is based on the following peculiar aspects: the potential for safety improvement is quantitatively assessed based on the estimation of safety performances for each road category, a bi-level thresholding process integrated in the optimization process is used to highlight sites for interventions, the anticipated outcome of safety measures is quantitatively assessed as well through available crash reduction factors.The proposed methodology is applied to a case study which analyzes a sample of real roads belonging to a province-wide road network composed of various road elements (i.e., different categories of segments and intersections), under different budget constraints. Results demonstrate the applicability and flexibility of the proposed approach, which could be used for planning purposes, independently of the particular geographic location. Clearly, the approach is valid at the planning stage, given that several details of the different layers of analysis are necessarily simplified, while they should be studied in detail at the single intervention project stage.

Multilevel Thresholding of Color Image Segmentation Using Memory-based Grey Wolf Optimizer With Otsu Method, Kapur, and M.Masi Entropy

Determining the optimal threshold value for image segmentation has become more attention in recent years because of its varied uses. Otsu-based thresholding methods, minimum cross entropy, and Kapur entropy are efficient for solving bi-level thresholding image segmentation problems (BL-ISP), but not with multi-level thresholding image segmentation problems (ML-ISP). The main problem is exponentially increasing computational complexity. This study uses the memory-based Gray Wolf Optimizer (mGWO) to determine the optimal threshold value for solving ML-ISP on RGB images. The mGWO method is a variant of the standard grey wolf optimizer (GWO) that utilizes the best track record of each individual grey wolf for the global exploration and local exploitation phases of the problem solution space. The solution candidates are represented by each grey wolf using the image intensity values and optimized according to mGWO characteristics. Three objective functions, namely the Otsu method, Kapur Entropy, and M.Masi Entropy are used to evaluate the solutions generated in the optimization process. The GridSearch method is used to determine the optimal parameter combination of each method based on 10 training images. Evaluation of the performance of the mGWO method was measured using several benchmark images and compared with five standard swarm intelligence (SI) methods as benchmarks. Analysis of the results was carried out qualitatively and quantitatively based on the average PSNR, RMSE, SSIM, UQI, fitness value, and CPU processing time from 30 tests. The results were analyzed further with the Wilcoxon signed-rank test. The experimental results show that the performance of the mGWO method outperforms the benchmark method in most experiments and metrics. The mGWO variant also proved to be superior to the standard GWO in resolving multi-level color image segmentation problems. The mGWO performance results are also compared with other state-of-the-art SI methods in solving ML-ISP on grayscale images and was able to outperform those methods in most experiments.

An Otsu thresholding for images based on a nature-inspired optimization algorithm

Thresholding is a type of image segmentation, where the pixels change to make the image easier to analyze. In bi-level thresholding, the image in grayscale format is transformed into a binary format. The traditional methods for image thresholding may be inefficient in finding the best threshold and take longer computation time. Recently, metaheuristic swarm-based algorithms were applied for optimization in different applications to find optimal solutions with minimum computational time. The proposed work aims to optimize the fitness function obtained by the Otsu algorithm using a metaheuristic swarm-based algorithm called the bat algorithm. As a result, the optimal threshold value for bi-level images in cloud detection was obtained. Also, one of the trajectory-based algorithms called hill climbing was applied to optimize the fitness function taken from the Otsu algorithm. The HYTA dataset was used to evaluate the work, which was later confirmed through testing. The findings of experiments indicated that the developed algorithm is promising and the performance of the metaheuristic population-based algorithm is better than the trajectory-based algorithm in terms of efficiency and computational time for image thresholding.

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.

An Improved Search and Rescue Algorithm for Global Optimization and Blood Cell Image Segmentation

Image segmentation has been one of the most active research areas in the last decade. The traditional multi-level thresholding techniques are effective for bi-level thresholding because of their resilience, simplicity, accuracy, and low convergence time, but these traditional techniques are not effective in determining the optimal multi-level thresholding for image segmentation. Therefore, an efficient version of the search and rescue optimization algorithm (SAR) based on opposition-based learning (OBL) is proposed in this paper to segment blood-cell images and solve problems of multi-level thresholding. The SAR algorithm is one of the most popular meta-heuristic algorithms (MHs) that mimics humans' exploration behavior during search and rescue operations. The SAR algorithm, which utilizes the OBL technique to enhance the algorithm's ability to jump out of the local optimum and enhance its search efficiency, is termed mSAR. A set of experiments is applied to evaluate the performance of mSAR, solve the problem of multi-level thresholding for image segmentation, and demonstrate the impact of combining the OBL technique with the original SAR for improving solution quality and accelerating convergence speed. The effectiveness of the proposed mSAR is evaluated against other competing algorithms, including the L'evy flight distribution (LFD), Harris hawks optimization (HHO), sine cosine algorithm (SCA), equilibrium optimizer (EO), gravitational search algorithm (GSA), arithmetic optimization algorithm (AOA), and the original SAR. Furthermore, a set of experiments for multi-level thresholding image segmentation is performed to prove the superiority of the proposed mSAR using fuzzy entropy and the Otsu method as two objective functions over a set of benchmark images with different numbers of thresholds based on a set of evaluation matrices. Finally, analysis of the experiments' outcomes indicates that the mSAR algorithm is highly efficient in terms of the quality of the segmented image and feature conservation, compared with the other competing algorithms.

QUANTUM INSPIRED GENETIC ALGORITHM FOR BI-LEVEL THRESHOLDING OF GRAY-SCALE IMAGES

Abstract. Thresholding is the primitive step in the process of image segmentation. Finding the optimal threshold for satellite images with reduced computation time and resources is still a challenging task. In this paper, we propose a Grey-Level Co-occurrence Matrix based Quantum Inspired Genetic Algorithm (QGA-GLCM) for bi-level thresholding of gray-scale images (natural and satellite). In this paper, QGA was used to find the optimal threshold. The results are compared with four different variants of Differential Evolution (DE) meta-heuristic algorithms, namely- DE-Otsu, DE-Kapur, DE-Tsali’s, DE-GLCM, and three different variants of QGA, namely- QGA-Otsu, QGA-Kapur, QGA-Tsali’s. Intensity value from image pixel is the only information used by Otsu, Tsali’s and Kapur for thresholding and are highly affected by noise. The main objective of this paper was a) To have a binary threshold for images corrupted with noise by bringing in spatial context b) To reduce the computational complexity and time for generating a threshold. Performance evaluators viz., CPU time, Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index Measure (SSIM) were used for quantitative assessment of partitioned images. From this study we observed that our proposed technique, QGA-GLCM is a) very good at producing a diverse population b) ten times faster than its classical counterparts c) generates better threshold for images corrupted by noise. In general, the threshold values generated by QGA and its variants are better than its classical counterparts. The results clearly show that exploration and exploitation capability of QGA is superior to DE for all variants. QGA-GLCM can be an effective technique to generate thresholds both in terms of computational speed and time.

Bilevel Thresholding–Based Iterative Analysis for Building-Surface Damage Detection in a Postearthquake Environment

Bilevel Thresholding–Based Iterative Analysis for Building-Surface Damage Detection in a Postearthquake Environment

Image thresholding segmentation based on weighted Parzen-window and linear programming techniques

Image segmentation by thresholding is an important and fundamental task in image processing and computer vision. In this paper, a new bi-level thresholding approach based on weighted Parzen-window and linear programming techniques is proposed to use in image thresholding segmentation. First, by proposing a weighted Parzen-window to describe the gray level distribution status, we obtain the boundaries for the foreground and background of the image. Then the image thresholding problem can be transformed into the problem of solving a linear programming problem for computing the coefficient values of weighted Parzen-window. The results of testing on synthetic, NDT and a set of benchmark images indicate that the proposed method can achieve a higher segmentation accuracy and robustness in comparison to some classical thresholding methods, such as inter class variance method (OTSU), Kapur’s entropy-based method (KSW), and some state-of-art methods that consider spatial information, such as CHPSO, GLLV histogram method and GABOR histogram method.

Multilevel Thresholding for Image Segmentation Using Mean Gradient

Image binarization and segmentation have been one of the most important operations in digital image processing and related fields. In spite of the enormous number of research studies in this field over the years, huge challenges still exist hampering the usability of some existing algorithms. Some of these challenges include high computational cost, insufficient performance, lack of generalization and flexibility, lack of capacity to capture various image degradations, and many more. These challenges present difficulties in the choice of the algorithm to use, and sometimes, it is practically impossible to implement these algorithms in a low-capacity hardware application where computational power and memory utilization are of great concern. In this study, a simple yet effective and noniterative global and bilevel thresholding technique is proposed. It uses the concept of image gradient vector to binarize or segment the image into three clusters. In addition, a parametric preprocessing approach is also proposed that can be used in image restoration applications. Evidences from the experiments from both visual and standard evaluation metrics show that the proposed methods perform exceptionally well. The proposed global thresholding outperforms the formidable Otsu thresholding technique.

HWOA: A hybrid whale optimization algorithm with a novel local minima avoidance method for multi-level thresholding color image segmentation

Traditional methods to address color image segmentation work efficiently for bi-level thresholding. However, for multi-level thresholding, traditional methods suffer from time complexity that increases exponentially with the increasing number of threshold levels. To overcome this problem, in this paper, a new approach is proposed to tackle multi-threshold color image segmentation by employing the Otsu method as an objective function. This approach is based on a hybrid of the whale optimization algorithm (WOA) with a novel method called the local minima avoidance method (LMAM), abbreviated as HWOA. LMAM avoids local minima by updating the whale either within the search space of the problem or between two whales selected randomly from the population-based on a certain probability. HWOA is validated on ten color images taken from the Berkeley University Dataset by measuring the objective values, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), features similarity index (FSIM), and CPU time, and compared with a number of the well-known robust meta-heuristic algorithms: the sine–cosine algorithm (SCA), WOA, modified salp swarm algorithm (MSSA), improved marine predators algorithm (IMPA), modified Cuckoo Search (CS) using McCulloch’s algorithm (CSMC), and equilibrium optimizer (EO). The experimental results show that HWOA is superior to all the other algorithms in terms of PSNR, FSIM, and objective values, and is competitive in terms of SSIM.

Binarization of Stone Inscription Images by Modified Bi-level Entropy Thresholding

India is rich in its heritage and culture. It has many historical monuments and temples where the walls are made of inscribed stones and rocks. The stone inscriptions play a vital role in portraying about the ancient incidents. Hence, the digitization of these stone inscriptions is necessary and contributes much for the epigraphers. Recently, the digitizing of these inscriptions began with the binarization process of stone inscriptions. This process mainly depends on the thresholding technique. In this paper, the binarization of terrestrial and underwater stone inscription images is preceded by a contrast enhancement and succeeded by edge-based filtering that minimizes noise and fine points the edges. A new method called modified bi-level thresholding (MBET) algorithm is proposed and compared with various existing thresholding algorithms namely Otsu method, Niblack method, Sauvola method, Bernsen method and Fuzzy C means method. The obtained results are evaluated with the performance metrics such as peak signal-to-noise ratio (PSNR) and standard deviation (SD). It is observed that the proposed method has an improvement of 49% and 39%, respectively, on an average by the metrics considered.

Krill Herd Algorithm for Color Image Segmentation with Kapur, Otsu and Minimum Cross Entropy Fitness Functions

Color image segmentation is essential to analyze information from the desired image with RGB color space. Generally, visual information can be easily retrieved through the simple, effective technique called thresholding. Segmentation of complex images is accurately achieved, through MultiLevel Thresholding (MLT) with most optimistic objective functions such as Kapur, Otsu and Minimum Cross Entropy (MCE), than Bilevel Thresholding (BLT). However, the complexity in exploring the optimal threshold increases with the increase in levels of threshold. The key to breach this barrier is by the most computationally effective, flexible Krill Herd Algorithm (KHA). The behavior of the krill movement, the foraging activity and the diffusion methods are utilized for global and local searches. KHA incorporates the swarm intelligence and with crossover, mutation operators improve the convergence rate. The performance of the KHA is compared with Teaching-Learning Based Optimization (TLBO) and cuckoo search algorithm (CSA). Experimental results disclose that the Otsu based MLT outperforms the Kapur and the MCE fitness functions. Quantitative and qualitative validations by metrics such as computational time, Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) confirm that Kapur, Otsu and MCE based KHA outperform the existing techniques for real life applications.

An efficient multilevel color image thresholding based on modified whale optimization algorithm

Color image segmentation is a vital preprocessing stage in various image processing applications. In threshold-based segmentation, the success of the image segmentation depends mainly on the optimal selection of thresholds. The selection of threshold values for multilevel thresholding is indeed a time-consuming process compared to bi-level thresholding. The issue of optimal threshold selection is formulated as an optimization problem in the case of color image segmentation using multilevel thresholding. To optimize the threshold selection for a multilevel color image thresholding, a modified whale optimization algorithm (MWOA) is proposed in this paper. The Otsu’s and Kapur’s functions have been used in the proposed strategy as a fitness function that can be maximized by MWOA. In the MWOA, the position of the whales is controlled by adapting the cosine function during optimization process. Further, the movements of search agents are regulated during the search process by introducing the correction factors in position updation. These changes incorporated in the MWOA provide a proper balance between the phases of exploration and exploitation and also avoid local optima problem. The performance of the MWOA is evaluated quantitatively and qualitatively based on the best fitness values in terms of PSNR, SSIM, and FSIM, further CPU computing time and Wilcoxon test. The experimental outcomes show that the proposed multilevel optimal color image thresholding using MWOA algorithm yields better performance results in terms of image quality, feature conservation, and convergence rate with less CPU computing time than other state-of-the-art algorithms.

Color image segmentation using Kapur, Otsu and Minimum Cross Entropy functions based on Exchange Market Algorithm

Color image segmentation is the primary step to elicit detailed information of the image with RGB color space. The key to acquire the region of interest is the straightforward, instinctual technique called thresholding. Bi-level thresholding can be assessed concretely for simple images, whereas revealing various classes from a complex image is achieved only through multiple threshold values. Hence, multilevel thresholding with the most propitious maximizing objective functions such as Kapur, Otsu and Minimum Cross Entropy (MCE) to extract the precise threshold are employed in this paper. As the segmentation level increases, execution of non-parametric objective functions leads to exponential increase in computational time. Several researches to enhance the speed of objective functions along with various metaheuristics such as Krill Herd Algorithm (KHA), Teaching-Learning Based Optimization (TLBO) and Cuckoo Search Algorithm (CSA) are performed. The need to unlock the computational complexity drifted our view towards the most powerful, robust, recently introduced metaheuristic Exchange Market Algorithm (EMA). EMA involves exchange of shares among investors to gain profit. The strategic approach of share members in stable and unstable modes of the market results in precise exploration and exploitation. Furthermore, low fitness shareholders gaining experience from their high and moderate counterparts, stands out as an exceptional step to reduce the time consumption. This concept is utilized in our paper for the first time to obtain the exact details from the image without any delay. Empirical outcome of the results indicates that exceptional image segmentation is achieved by EMA in less time compared to extensive search techniques such as KHA, TLBO and CSA. Quantitative and qualitative validation furnished by metrics such as Structural Similarity Index (SSIM), computational time, Peak Signal to Noise Ratio (PSNR) and statistical Wilcoxon test affirm that Otsu, Kapur and MCE based EMA outperforms the existing techniques to analyze the real-world images.

Multilevel thresholding using an improved cuckoo search algorithm for image segmentation

Multilevel thresholding image segmentation is an important technique, which has attracted much attention in recent years. The conventional exhaustive search method for image segmentation is efficient for bilevel thresholding. However, they are time expensive when dealing with multilevel thresholding image segmentation. To better tackle this problem, an improved cuckoo search algorithm (ICS) is proposed to search for the optimal multilevel thresholding in this paper, and Otsu is considered as its objective function. In the ICS, two modifications are used to improve the standard cuckoo search algorithm. First, a parameter adaptation strategy is utilized to improve exploration performance. Second, a dynamic weighted random-walk method is adopted to enhance the local search efficiency. A total of six benchmark test images are used to perform the experiments, and seven state-of-the-art metaheuristic algorithms are introduced to compare with the ICS. A series of measure indexes such as objective function value and standard deviation, PSNR, FSIM, and SSIM as well as the Wilcoxon rank sum and convergence performance are performed in the experiments; the experimental results show that the proposed algorithm is superior to other seven well-known heuristic algorithms.

Image compression based on adaptive image thresholding by maximising Shannon or fuzzy entropy using teaching learning based optimisation

In this paper, teaching leaning based optimisation (TLBO) is used for maximising Shannon entropy or fuzzy entropy for effective image thresholding which leads to better image compression with higher peak signal to noise ratio (PSNR). The conventional multilevel thresholding methods are efficient when bi-level thresholding. However, they are computationally expensive extending to multilevel thresholding since they exhaustively search the optimal thresholds to optimise the objective functions. To overcome this drawback, a TLBO based multilevel image thresholding is proposed by maximising Shannon entropy or fuzzy entropy and results are compared with differential evolution, particle swarm optimisation and bat algorithm and proved better in standard deviation, PSNR, weighted PSNR and reconstructed image quality. The performance of the proposed algorithm is found better with fuzzy entropy compared to Shannon entropy.

Efficient Image Segmentation of Natural Images with Noise using Energy Curved Based on Electromagnetism Optimization Algorithm

The method of digital image processing is an important step for extraction features from images, especially details from the medical image by dividing the image into various regions. In the last two decades, many significant image segmentation schemes modeled for different applications like medical to weather forecasting by images from the satellite. From available methods, multilevel thresholding of pixel values using the histogram of an image is an exceedingly important method developed for vide range of applications. Otsu’s method is the primer multilevel thresholding method based on the maximizing the inter-class variance based on the histogram. In Otsu’s method, the pixels of an image are grouped into different regions based on threshold levels calculated on the histogram of the image. All the pixels are segmented into two groups based on threshold level in the case of bi-level thresholding. The effectiveness of segmentation is depends on threshold levels, efficiency can be enhanced by computing optimized threshold level, Electromagnetism Optimization (EMO) technique used for this purpose in this paper. The techniques based on histograms of image are not holding the spatial particulars of contextual information for computing the optimized threshold levels, to nullify this shortcoming Energy Curve is used as an alternative of histogram, which is restore the special relation of pixels with its neighbor pixels. To examine the results several benchmark images considered, the final results shows that the method with Otsu’s technique with EMO on energy curve is giving better results as compared with methods based on the image histogram.

A hybrid firefly and particle swarm optimization algorithm applied to multilevel image thresholding

There are many techniques for conducting image analysis and pattern recognition. This papers explores a way to optimize one of these techniques—image segmentation—with the help of a novel hybrid optimization algorithm. Image segmentation is mostly used for a semantic segmentation of images, and thresholding is one the most common techniques for performing this segmentation. Otsu’s and Kapur’s thresholding methods are two well-known approaches, both of which maximize the between-class variance and the entropy measure, respectively, in a gray image histogram. Both techniques were developed for bi-level thresholding. However, these techniques can be extended to multilevel image thresholding. For this to occur, a large number of iterations are required to account for exact threshold values. However, various optimization techniques have been used to overcome this drawback. In this study, a hybrid firefly and particle swarm optimization algorithm has been applied to yield optimum threshold values in multilevel image thresholding. The proposed method has been assessed by comparing it with four well-known optimization algorithms. The comprehensive experiments reveal that the proposed method achieves better results in term of fitness value, PSNR, SSIM, FSIM, and SD.