Image Threshold Segmentation Research Articles

An efficient image segmentation method for skin cancer imaging using improved golden jackal optimization algorithm

Skin cancer is one of the worst cancers nowadays that poses a severe threat to the health and safety of individuals. Therefore, skin cancer classification and early diagnosis are recommended to preserve human life. Multilevel thresholding image segmentation is well-known and influential technique for extracting regions of interest from skin cancer images to improve the classification process. Therefore, this paper proposes an efficient version of the recently developed golden jackal optimization (GJO) algorithm, the opposition-based golden jackal optimizer (IGJO). The IGJO algorithm is used to solve the multilevel thresholding problem using Otsu’s method as an objective function. The proposed algorithm is compared with seven other meta-heuristic algorithms: whale optimization algorithm, seagull optimization algorithm, salp swarm algorithm, Harris hawks optimization, artificial gorilla troops optimizer, marine predators’ algorithms, and original GJO algorithm. The performance of the proposed algorithm is evaluated using four popular performance measures: peak signal-to-noise ratio, structure similarity index, feature similarity index, and mean square error. Experimental results show that the proposed algorithm outperforms other alternative algorithms in terms of PSNR, SSIM, FSIM, and MSE segmentation metrics and effectively resolves the segmentation problem.

Microstructural and compaction characteristics of tropical black clay soil subgrade modified with lead-zinc mine tailings

Lead–Zinc Mine tailings (LZMT) are wastes generated after the extraction of lead and zinc from mined mineral ore, whose disposal mechanism is gradually becoming environmentally unfriendly. For effective recycling of LZMT, this present study utilized a combination of LZMT and Portland limestone cement (PLC) to improve the compaction and microstructural characteristics of tropical black clay soil (TBCS) for use in pavement design and construction. The LZMT and PLC were added to the expansive soil in varying proportions with mix ratios generated from Taguchi orthogonal array. The result obtained for the compaction characteristics showed that the maximum dry density (MDD) increased significantly when a combination of 20% LMZT and 4% PLC were blended with the expansive soil. The increase in the MDD was attributed to the formation of cementitious compounds. In addition, the optimum mix ratio obtained from the unconfined compressive strength of the TBCS, was used for the evaluation of the pore structure characteristics which included porosity, tortuosity and permeability. The result obtained from the analysis that was implemented with a combination of fractal geometry and Bradley and Roth adaptive thresholding image segmentation technique, indicates the possibility of a slight reduction in the strength properties of the modified soil due to its high level of porosity. Also, the permeability and tortuosity values obtained from the present study suggest a slight increase in the permeability of the modified soil-additive mixtures which may not be unconnected to the occurrence of pozzolanic reaction that resulted in the agglomeration and flocculation of the LZMT-PLC modified TBCS. Furthermore, microstructural analysis was executed on the modified TBCS and LZMT using Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). The result from the FTIR analysis indicates the pozzolanic character of LZMT in the presence of Si–O and Al–O tension bond with the bonds around 1000 cm−1 wavenumber, while the SEM analysis reveals the formation of a cementitious compound in the modified expansive soil-LZMT-PLC mixture.

Research and development of weld tracking system based on laser vision

Aiming at the shortcomings of low real-time, low applicability, and low welding precision of automatic welding system, a seam tracking system based on laser vision is designed. Use the laser vision sensor to collect the weld image and transmits it to the industrial control computer for processing. Using a median filter to eliminate noise impacts such as arc and splash. Then, this paper focuses on the combination of an improved image threshold segmentation algorithm is used to solve the optimal threshold to obtain the binary image. And the information of laser stripe and the background are separated, overcomes the problems that the researchers have encountered before, such as the unrecognized global optimal solution, and the inaccuracy of the segmentation caused by system jitter. Finally, combined with the improved upper and lower average method, least square method, and Hough transform, the weld feature points are identified and more ideal real-time weld tracking is realized. The experimental results show that the method can accurately track the weld feature points, and improve the detection speed.

In vitro assessment of a novel dual-energy cone beam computed tomography device

<h3>Objective</h3> CT attenuation depends on atomic number and physical density of the object. Dual energy systems use two energy spectra and attenuation data from two different exposure conditions to estimate the effect of photoelectric absorption and Compton scatter in generating CT numbers. Based on this principle, a dual energy cone beam CT device (DE-CBCT, RCT-270, Ray Co. Ltd., Korea) was developed to assess jaw bone density and overcome limitations of gray level inhomogeneity in CBCT. <h3>Study Design</h3> We used a human skull phantom and varying concentrations of dipotassium phosphate (K₂HPO₄) to simulate trabecular and cortical bone. 0.2 ml polypropylene tubes with K₂HPO₄ (600mg/ml and 800mg/ml) were placed in empty sockets of the phantom at 3 discrete mandibular sites and scans were acquired. Next, to simulate temporal changes, we used varying concentrations of K₂HPO₄ (400 mg/ml-1200 mg/ml) in the extraction socket of a mandibular second premolar. All scans were made using the Rayscan DE-CBCT unit and repeated thrice. After each scan, the QRM-DE-CBCT phantom was also scanned with the same exposure factors for calibration. Scan calibration and bone density assessments were performed using the vendor's software. <h3>Results</h3> Calculated density of the same K₂HPO₄ placed at different locations (891± 144, 768 ± 29, 541 ± 126) was not statistically different. A similar result was obtained with K₂HPO₄ of higher density (1299 ± 58, 1203 ± 74, 998 ± 125). While simulating changing bone density, increasing concentrations of K₂HPO₄ yielded higher calculated densities, with a linear relationship between K₂HPO₄ concentrations and DE-CBCT calculated density (r² = 0.88). <h3>Conclusion</h3> This study provides an initial assessment of DE-CBCT as a method to better estimate object density which can impact monitoring osseous healing, evaluation of potential implant sites, and augmentation threshold for image segmentation. In ongoing studies, we are characterizing image quality parameters and further assessing the impact of anatomic location. <b>Statement of Ethical Review</b> Ethical Review or exemption was not warranted for this study

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.

Effect of Image Segmentation Thresholding on Droplet Size Measurement

Droplet size spectrum is a key factor in pesticide application because it affects the biological efficacy of a treatment in terms of target coverage, environmental impact in terms of evaporation, drift and run-off, and operator’s safety in terms of inhalation and dermal exposure. Droplet measurement methods based upon image analysis have to face the “binarization” or “segmentation” process, by which the objects of interest (the droplets) are extracted from the background. Segmentation is carried out by choosing appropriate threshold values, mostly based on the operator’s experience. In this study, images of droplets of an air induction nozzle TVI 8002 at four pressures (0.3, 0.5, 1.0, and 1.5 MPa) were obtained using the liquid immersion method. Each image was processed multiple times, firstly by using a “reference” threshold value based on the operator’s experience and then by using 11 different threshold values, chosen in the range of around ±5% of the reference threshold and based upon the average gray level of the image. For each threshold value, the corresponding spray parameters (volumetric diameters, mean diameters, Sauter diameters, and numeric diameters) were analyzed. The results showed that spray parameters had a statistically significant linear trend with respect to the threshold values in most cases. However, in absolute terms, variations were almost always less than 1.0% of reference values. This result allows considering the image acquisition system used in the present study as an automatic tool able to select the threshold according to the gray level of the image, making the whole segmentation process faster, more objective, and less dependent on the operator’s experience.

Observation of Clinical Efficacy of Anisodamine and Chlorpromazine in the Treatment of Intractable Hiccup after Stroke.

Objective This study is aimed at investigating the clinical efficacy of anisodamine combined with chlorpromazine on intractable hiccups after stroke. Methods 150 patients admitted to Affiliated Hospital of the Hebei University of Engineering from 2017 to 2021 were selected as the research objects, all of which received the computed tomography (CT) examination. During CT examination, intelligent algorithms were used to segment the images. An unsupervised multilayer image threshold segmentation algorithm was proposed by using Kullback-Leibler (K-L) divergence and the modified particle swarm optimization (MPSO) algorithm. All patients were divided into three groups, with each group of 50 patients. Patients in the control group (group A) took the calcium tablets, vitamin C tablets, and vitamin B1 tablets orally. Patients in the control group (group B) received the acupoint injection of anisodamine, and those in the observation group (group C) received the acupoint injection of anisodamine combined with chlorpromazine. The therapeutic effect and patient satisfaction of the three groups were compared. Results The two-dimensional (2D) K-L divergence was applied for the multilayer segmentation of images, which was helpful to obtain accurate images. The MPSO algorithm was adopted to reduce the computational complexity. The total efficiency of group C was 98%, that of group B was 56%, and that of group A was 22%. The total efficiency and satisfaction rate of group C were signally better than those of group A and group B (P < 0.05). Conclusion The combination of 2D K-L divergence and MPSO algorithm could improve the accuracy of multilayer image segmentation and CT imaging. Acupoint injection of anisodamine combined with chlorpromazine had better efficacy than the injection of anisodamine alone for the treatment of intractable hiccups after stroke, with high safety and clinical promotion value.

Non-destructive detection of egg qualities based on hyperspectral imaging

Egg quality detection is important to food processing and people consumption. The aim of this study is to detect egg freshness, scattered yolk and eggshell cracks by applying hyperspectral imaging (HSI), multivariate analysis and image process. The transmission visible-near infrared hyperspectral images of egg samples were acquired in the wavelength range of 401–1002 nm. Standard normal variate (SNV) was applied to normalize the spectral data, and iteratively retains informative variable (IRIV) was used to optimize wavelength selection. Based on the feature wavelengths, egg freshness quantitative model was established by using Extreme Gradient Boosting (XGBoost), with coefficient of determination for prediction ( R 2 p ) of 0.91 and root mean square error for prediction (RMSEP) of 4.64. An algorithm including image contrast enhancement, denoising and threshold segmentation was proposed to extract the morphological features of yolk. Based on the morphological feature ratio, the recognition accuracy of scattered yolk eggs reached 97.33%. In addition, a method including crack enhancement, double threshold segmentation was developed to extract the geometric features of cracks. The cracked eggs could be discriminated by XGBoost classification model with identification accuracy of 93.33%. The results indicate that HSI can be useful for the non-destructive detection of egg qualities. • Hyperspectral imaging was applied to detect egg freshness, scattered yolk and cracks. • The feature wavelengths for egg freshness detection were selected by IRIV. • An algorithm for extracting morphological features of yolk was proposed. • A method for extracting geometric features of eggshell cracks was proposed.

Toward Emulating Human Movement: Adopting a Data-Driven Bitmap-Based "Voxel" Multimaterial Workflow to Create a Flexible 3D Printed Neonatal Lower Limb.

It is increasingly common to produce physical anatomical medical models using high-fidelity multiproperty 3D printing to assist doctor-patient communication, presurgical planning, and surgical simulation. Currently, most medical models are created using image thresholding and traditional mesh-based segmentation techniques to produce mono-material boundaries (STL file formats) of anatomical features. Existing medical modeling manufacturing methods restrict shape specification to one material or density, which result in anatomically simple 3D printed medical models with no gradated material qualities. Currently, available high-resolution functionally graded multimaterial 3D printed medical models are rigid and do not represent biomechanical movement. To bypass the identified limitations of current 3D printing medical modeling workflows, we present a bitmap-based "voxel" multimaterial additive manufacturing workflow for the production of highly realistic and flexible anatomical models of the neonatal lower limb using computed tomographic ("CT") data. By interpolating and re-slicing a biomedical volumetric data set at the native 3D printer z resolution of 27 μm and using CT scan attenuation properties (Hounsfield units) to guide material mixing ratios, producing highly realistic models of the neonatal lower limb at a significantly faster rate than other manufacturing methods. The presented medical modeling workflow has considerable potential to improve medical modeling manufacturing methods by translating medical data directly into 3D printing files aiding in anatomical education and surgical simulation practices, especially in neonatal research and clinical training.

Multi-scale creep analysis of river sand and manufactured sand concrete considering the influence of ITZ

This paper investigated the multi-scale prediction of long-term creep in river sand (RS) concrete and manufactured sand (MS) concrete and analyzed the effect of the interfacial transition zone (ITZ) around the aggregate on the creep properties of concrete. Uniaxial compressive creep tests were conducted on the concrete for a duration of 360 d. The creep kinetic properties of the paste matrix and ITZ were obtained by microindentation test. The properties of ITZ were determined by the backscattered electron (BSE) image threshold segmentation technique and the Lu & Torquato model. Finally, the predicted concrete creep modulus was obtained through the calculation of creep homogenization, and the accuracy of multi-scale creep prediction when considering different ITZ cases was discussed. The results show that under the same water-binder ratio, the thickness of ITZ around MS was larger, the creep modulus was smaller and the volume fraction was larger compared with that of ITZ around RS. In the multi-scale homogenization calculation of the concrete long-term creep, the prediction accuracy of the computational model increased with the raised of the considered ITZ types. The prediction accuracy obtained when all ITZs were considered was improved by more than 46.3% compared to that when no ITZs were considered.

(Retracted) Hybrid whale optimization algorithm-Levy flight approach for multilevel thresholding image segmentation

Determining the optimal threshold for multilevel image segmentation is a stimulating task. Image segmentation has many applications, such as content-based image retrieval, medical imaging, object detection, object recognition, machine vision, etc. In multilevel thresholding, multiple thresholds are used to segment complex images, and the images are segmented into multiple levels to extract meaningful information for further image analysis. Where threshold number is small, p-tile, Otsu, moment preserving, and entropy thresholding methods attain good accuracy. These classical thresholding methods are time-consuming, computation expensive, and unable to produce good segmentation accuracy with increased threshold numbers. To overcome these problems, the classical thresholding methods are utilized as objective functions with nature-inspired metaheuristic algorithms such as whale optimization algorithm (WOA), simplified swarm optimization (SSO), sine cosine algorithm (SCA), BAT algorithm, WOA-thresholding, and black widow optimization (BWO) to determine optimal multiple thresholds. Nature-inspired metaheuristic algorithms are widely used to search for an optimal solution for global optimization problems. WOA is widely used to find the optimal solution in search space. WOA suffers from entrapment into local optima due to premature convergence behavior. We introduce hybridization of WOA with Levy flight trajectory and named as hybrid whale optimization algorithm-Levy flight (HWOAL), which is utilized to find optimal multiple thresholds for multilevel image segmentation. Levy flight trajectory is utilized to increase diversity in the swarm population. The efficacy of HWOAL is tested on 23 benchmark optimization functions (F1 to F23) and compared with WOA, SSO, SCA, and BAT algorithms. Experiment arms that HWOAL is efficient and makes WOA faster, enhances the ability of exploitation and exploration phase, and can avoid getting stuck into local optima. The segmentation performance of the proposed HWOAL method has been compared with other algorithms, such as WOA, SSO, SCA, BAT, and other two recent segmentation algorithms, such as WOA-TH and BWO on several benchmark images (BSD 300). The experimental result is carried in 30 trials and analyzed based on objective fitness value, optimal multilevel threshold values, segmentation quality measures, such as mean square error, peak signal-to-noise ratio, structural similarity index, average difference, and computation time to compute optimal multilevel threshold values. The experiment conducted by the authors shows that the proposed HWOAL method is efficient, produces better fitness value, and segmentation metrics for multilevel image segmentation than other algorithms.

Tabu-Adaptive Artificial Bee Colony Metaheuristic for Image Segmentation

This paper proposes to enhance the Artificial Bee Colony (ABC) metaheuristic with a Tabu adaptive memory to optimize the multilevel thresholding for Image Segmentation. This novel method is named Tabu-Adaptive Artificial Bee Colony (TA-ABC). To find the optimal thresholds, two novel versions of the proposed technique named TA-ABC-BCV and TA-ABC-ET are developed using respectively the thresholding functions namely the Between-Class Variance (BCV) and the Entropy Thresholding (ET). To prove the robustness and performance of the proposed methods TA-ABC-BCV and TA-ABC-ET, several benchmark images taken from the USC-SIPI Image Database are used. The experimental results show that TA-ABC-BCV and TA-ABC-ET outperform other existing optimization algorithms in the literature. Besides, compared to TA-ABC-ET and other methods from the literature all experimental results prove the superiority of TA-ABC-BCV.

Correction to: Threshold image segmentation based on improved sparrow search algorithm

Correction to: Threshold image segmentation based on improved sparrow search algorithm

An improved whale optimization algorithm based on multilevel threshold image segmentation using the Otsu method

In this paper, an improved multithreshold image segmentation method based on the whale optimization algorithm (RAV-WOA) is proposed, with the between-class variance (Otsu method) as the objective function. The proposed RAV-WOA is able to select satisfactory optimal thresholds while ensuring high efficiency and quality when performing image segmentation on grayscale and color images In the current work, a reverse learning strategy was introduced into the initialization of RAV-WOA populations to improve the quality of the initial population of whales. An adaptive weighting strategy was introduced into the RAV-WOA algorithm, which is influenced by the fitness value and the number of iterations, to balance the global search capability of the algorithm with the local exploitation capability. The proposed RAV-WOA is then applied to the Otsu method to solve the multilevel thresholding image segmentation problem. To better verify the effectiveness of the proposed method, this paper compares the RAV-WOA with some classical heuristic algorithms and performs image segmentation experiments on a set of benchmark images with low and high thresholds. The experimental results show that the convergence speed and convergence accuracy of RAV-WOA are significantly better than other algorithms, and the segmentation results of RAV-WOA in multithreshold image segmentation have better quality and stability than other algorithms.

Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical multiverse optimization

COVID-19 is currently raging worldwide, with more patients being diagnosed every day. It usually is diagnosed by examining pathological photographs of the patient's lungs. There is a lot of detailed and essential information on chest radiographs, but manual processing is not as efficient or accurate. As a result, how efficiently analyzing and processing chest radiography of COVID-19 patients is an important research direction to promote COVID-19 diagnosis. To improve the processing efficiency of COVID-19 chest films, a multilevel thresholding image segmentation (MTIS) method based on an enhanced multiverse optimizer (CCMVO) is proposed. CCMVO is improved from the original Multi-Verse Optimizer by introducing horizontal and vertical search mechanisms. It has a more assertive global search ability and can jump out of the local optimum in optimization. The CCMVO-based MTIS method can obtain higher quality segmentation results than HHO, SCA, and other forms and is less prone to stagnation during the segmentation process. To verify the performance of the proposed CCMVO algorithm, CCMVO is first compared with DE, MVO, and other algorithms by 30 benchmark functions; then, the proposed CCMVO is applied to image segmentation of COVID-19 chest radiography; finally, this paper verifies that the combination of MTIS and CCMVO is very successful with good segmentation results by using the Feature Similarity Index (FSIM), the Peak Signal to Noise Ratio (PSNR), and the Structural Similarity Index (SSIM). Therefore, this research can provide an effective segmentation method for a medical organization to process COVID-19 chest radiography and then help doctors diagnose coronavirus pneumonia (COVID-19).

Image Multithreshold Segmentation Method Based on Improved Harris Hawk Optimization

In order to improve the accuracy and performance of traditional image threshold segmentation algorithm, this paper proposes a multithreshold segmentation method named improved Harris hawk optimization (IMHHO). Firstly, IMHHO adopts Tent map and elite opposition-based learning to initialize population and enhance the diversity. Secondly, IMHHO uses quadratic interpolation to generate new individuals and enhance the local search ability. Finally, IMHHO adopts improved Gaussian disturbance method to disturb optimal solution, which coordinates the local and global search ability. Then, the performance of IMHHO is tested based on 14 benchmark functions. In image segmentation, different algorithms are tested to compare the comprehensive performance based on Otsu and Renyi entropy. Experiments show that IMHHO performs better in the three kinds of benchmark functions; the segmentation effect is directly proportional to the number of thresholds; compared with other algorithms, IMHHO has better comprehensive performance.

Threshold image segmentation based on improved sparrow search algorithm.

Threshold segmentation based on swarm intelligence optimization algorithm is a research hotspot in image processing, because of its good segmentation effect and easy implementation. This paper proposes an image threshold segmentation method based on an improved sparrow search algorithm and 2-D maximum entropy method. In the proposed algorithm, the nonlinear inertia weight is introduced into the entrants’ update formula to improve the local exploration ability of the algorithm, and Levy flight is introduced into the vigilant sparrows’ update formula to prevent the algorithm from falling into the local optimal solution in the later stage of iteration. In addition, improved sparrow search algorithm is tested on fifteen benchmark functions. The results represent the merit of the proposed algorithm with respect to other algorithms. Finally, the proposed algorithm is applied to entropy based image segmentation. Experiment results on classical images and medical images show that the proposed method improves the segmentation effect in terms of peak signal-to-noise ratio and feature similarity.

Poplar optimization algorithm: A new meta-heuristic optimization technique for numerical optimization and image segmentation

A novel algorithm called Poplar Optimization Algorithm (POA) is developed in this paper to solve continuous optimization problems. The algorithm mimics the sexual and asexual propagation mechanism of poplar, where the basic philosophy of how to execute sexual and asexual propagation for individuals is detail designed in the algorithm. Mutation strategy of backtracking search algorithm is adopted in POA to maintain the diversity in a certain degree. The performance of POA algorithm is tested on 25 functions from the CEC2005 test suite and 30 functions from the CEC2017 test suite with different features. The results of POA are compared with some other population-based algorithms in terms of the quality and efficiency. Finally, the proposed algorithm is used to find the optimal threshold for image segmentation. The results indicate that the poplar optimization algorithm can obtain competitive or superior performance.

Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation

Image segmentation is a key stage in image processing because it simplifies the representation of the image and facilitates subsequent analysis. The multi-level thresholding image segmentation technique is considered one of the most popular methods because it is efficient and straightforward. Many relative works use meta-heuristic algorithms (MAs) to determine threshold values, but they have issues such as poor convergence accuracy and stagnation into local optimal solutions. Therefore, to alleviate these shortcomings, in this paper, we present a modified remora optimization algorithm (MROA) for global optimization and image segmentation tasks. We used Brownian motion to promote the exploration ability of ROA and provide a greater opportunity to find the optimal solution. Second, lens opposition-based learning is introduced to enhance the ability of search agents to jump out of the local optimal solution. To substantiate the performance of MROA, we first used 23 benchmark functions to evaluate the performance. We compared it with seven well-known algorithms regarding optimization accuracy, convergence speed, and significant difference. Subsequently, we tested the segmentation quality of MORA on eight grayscale images with cross-entropy as the objective function. The experimental metrics include peak signal-to-noise ratio (PSNR), structure similarity (SSIM), and feature similarity (FSIM). A series of experimental results have proved that the MROA has significant advantages among the compared algorithms. Consequently, the proposed MROA is a promising method for global optimization problems and image segmentation.

Boosting Marine Predators Algorithm by Salp Swarm Algorithm for Multilevel Thresholding Image Segmentation.

Pixel rating is considered one of the commonly used critical factors in digital image processing that depends on intensity. It is used to determine the optimal image segmentation threshold. In recent years, the optimum threshold has been selected with great interest due to its many applications. Several methods have been used to find the optimum threshold, including the Otsu and Kapur methods. These methods are appropriate and easy to implement to define a single or bi-level threshold. However, when they are extended to multiple levels, they will cause some problems, such as long time-consuming, the high computational cost, and the needed improvement in their accuracy. To avoid these problems and determine the optimal multilevel image segmentation threshold, we proposed a hybrid Marine Predators Algorithm (MPA) with Salp Swarm Algorithm (SSA) to determine the optimal multilevel threshold image segmentation MPASSA. The obtained solutions of the proposed method are represented using the image histogram. Several standard evaluation measures, such as (the fitness function, time consumer, Peak Signal-to-Noise Ratio, Structural Similarity Index, etc.…) are employed to evaluate the proposed segmentation method’s effectiveness. Several benchmark images are used to validate the proposed algorithm’s performance (MPASSA). The results showed that the proposed MPASSA got better results than other well-known optimization algorithms published in the literature.