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In this paper, an enhanced version of the Exponential Distribution Optimizer (EDO) called mEDO is introduced to tackle global optimization and multi-level image segmentation problems. EDO is a math-inspired optimizer that has many limitations in handling complex multi-modal problems. mEDO tries to solve these drawbacks using 2 operators: phasor operator for diversity enhancement and an adaptive p-best mutation strategy for preventing it converging to local optima. To validate the effectiveness of the suggested optimizer, a comprehensive set of comparative experiments using the CEC'2020 test suite was conducted. The experimental results consistently prove that the suggested technique outperforms its counterparts in terms of both convergence speed and accuracy. Moreover, the suggested mEDO algorithm was applied for image segmentation using the multi-threshold image segmentation method with Otsu's entropy, providing further evidence of its enhanced performance. The algorithm was evaluated by comparing its results with those of existing well-known algorithms at various threshold levels. The experimental results validate that the proposed mEDO algorithm attains exceptional segmentation results for various threshold levels.

In this paper, an enhanced version of the Exponential Distribution Optimizer (EDO) called mEDO is introduced to tackle global optimization and multi-level image segmentation problems. EDO is a math-inspired optimizer that has many limitations in handling complex multi-modal problems. mEDO tries to solve these drawbacks using 2 operators: phasor operator for diversity enhancement and an adaptive p-best mutation strategy for preventing it converging to local optima. To validate the effectiveness of the suggested optimizer, a comprehensive set of comparative experiments using the CEC'2020 test suite was conducted. The experimental results consistently prove that the suggested technique outperforms its counterparts in terms of both convergence speed and accuracy. Moreover, the suggested mEDO algorithm was applied for image segmentation using the multi-threshold image segmentation method with Otsu's entropy, providing further evidence of its enhanced performance. The algorithm was evaluated by comparing its results with those of existing well-known algorithms at various threshold levels. The experimental results validate that the proposed mEDO algorithm attains exceptional segmentation results for various threshold levels. [ABSTRACT FROM AUTHOR]

The detection of Martian polar ice cap change patterns is important for understanding their effects on driving Mars's global water cycle and for regulating atmospheric circulation. However, current Martian ice cap identification using optical remote sensing data mainly relies on visual interpretation, which makes it difficult to quickly extract ice caps from multiple images and analyze their fine-scale spatiotemporal variation characteristics. Therefore, this study proposes an automatic Martian polar ice cap extraction algorithm for remote sensing data and analyzes the dynamic change characteristics of the Martian North Pole ice cap using time-series data. First, the automatic Martian ice cap segmentation algorithm was developed based on the ice cap features of high reflectance in the blue band and low saturation in the RGB band. Second, the Martian North Pole ice cap was extracted for the three Martian years MY25, 26, and 28 using Mars Orbiter Camera (MOC) Mars Daily Global Maps (MDGMs) data, which had better spatiotemporal continuity to analyze its variation characteristics. Lastly, the spatiotemporal variation characteristics of the ice cap and the driving factors of ice cap ablation were explored for the three aforementioned Martian years. The results indicated that the proposed automatic ice cap extraction algorithm had good performance, and the classification accuracy exceeded 93%. The ice cap ablation boundary retreat rates and spatiotemporal distributions were similar for the three years, with approximately 105 km2 of ice cap ablation for every one degree of areocentric longitude of the Sun (Ls). The main driving factor of ice cap ablation was solar radiation, which was mainly related to Ls. In addition, elevation had a different effect on ice cap ablation at different Ls in the same latitude area near the ablation boundary of the ice cap. [ABSTRACT FROM AUTHOR]

The efficient management of commercial orchards strongly requires accurate information on plant growing status for the implementation of necessary farming activities such as irrigation, fertilization, and pest control. Crown planar area and plant number are two very important parameters directly relating to fruit growth conditions and the final productivity of an orchard. In this study, in order to propose a novel and effective method to extract the crown planar area and number of mature and young papayas based on visible light images obtained from a DJ Phantom 4 RTK, we compared different vegetation indices (NGRDI, RGBVI, and VDVI), filter types (high- and low-pass filters), and filter convolution kernel sizes (3–51 pixels). Then, Otsu's method was used to segment the crown planar area of the papayas, and the mean–standard deviation threshold (MSDT) method was used to identify the number of plants. Finally, the extraction accuracy of the crown planar area and number of mature and young papayas was validated. The results show that VDVI had the highest capability to separate the papayas from other ground objects. The best filter convolution kernel size was 23 pixels for the low-pass filter extraction of crown planar areas in mature and young plants. As to the plant number identification, segmentation could be set to the threshold with the highest F-score, i.e., the deviation coefficient n = 0 for single young papaya plants, n = 1 for single mature ones, and n = 1.4 for crown-connecting mature ones. Verification indicated that the average accuracy of crown planar area extraction was 93.71% for both young and mature papaya orchards and 95.54% for extracting the number of papaya plants. This set of methods can provide a reference for information extraction regarding papaya and other fruit trees with a similar crown morphology. [ABSTRACT FROM AUTHOR]

In the context of complex structures and blurred cell boundaries present in breast cancer histopathological tissue images under a microscope, traditional thresholding methods struggle to accurately separate lesion areas in breast cancer image segmentation. To address this challenge, we propose a multi-threshold segmentation method for breast cancer images based on an improved Dandelion Optimization algorithm. This approach incorporates the concept of opposite-based learning and utilizes the improved Dandelion Optimization algorithm to calculate the maximum between-class variance as the optimization objective. Moreover, the method establishes fallback strategies and incorporates a memory matrix, while leveraging the golden jackal energy judgment mechanism to identify optimal thresholds. The experimental results show that compared with the Crow search algorithm, Harris Hawks optimization algorithm, artificial gorilla troop optimization algorithm, dandelion optimization algorithm, ocean predator algorithm, whale optimization algorithm, sparrow search algorithm, and sine cosine algorithm, and the improved Dandelion optimization algorithm achieves the highest fitness value and converges at the fastest speed when using the same threshold number, it also occupies an advantageous position in terms of peak signal-to-noise ratio, structural similarity index, feature similarity index, and mean square error. [ABSTRACT FROM AUTHOR]

The proposed methodology employs a variety of image processing and analysis techniques to achieve accurate detection results. To begin, the acquired lung cancer images are preprocessed with a multidimensional filter and histogram equalization in order to improve their quality and subsequent analysis. Histogram equalization optimizes an image's dynamic range, enhancing visibility of structures and abnormalities. This technique proves invaluable in medical imaging, revealing subtle features for accurate anomaly detection. Meanwhile, Multidimensional Filtering refines image analysis with intelligent filtering methods. Pre-processing, segmentation, and feature extraction from lung cancer images are all part of the method. For accurate lung cancer detection, a deep neural network is trained and tested. The proposed method achieves 99.1251% specificity, 99.1121% sensitivity, and 99.269% accuracy. MATLAB is used to run the entire simulation. The architectural representation distinctly illustrates the method's superior ability to discern true negatives and true positives in lung cancer detection. The research advances lung cancer diagnosis and has the potential for early detection and improved patient care. [ABSTRACT FROM AUTHOR]

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

The main disease that decreases the manufacturing of natural rubber is tapping panel dryness (TPD). To solve this problem faced by a large number of rubber trees, it is recommended to observe TPD images and make early diagnosis. Multi-level thresholding image segmentation can extract regions of interest from TPD images for improving the diagnosis process and increasing the efficiency. In this study, we investigate TPD image properties and enhance Otsu's approach. For a multi-level thresholding problem, we combine the snake optimizer with the improved Otsu's method and propose SO-Otsu. SO-Otsu is compared with five other methods: fruit fly optimization algorithm, sparrow search algorithm, grey wolf optimizer, whale optimization algorithm, Harris hawks optimization and the original Otsu's method. The performance of the SO-Otsu is measured using detail review and indicator reviews. According to experimental findings, SO-Otsu performs better than the competition in terms of running duration, detail effect and degree of fidelity. SO-Otsu is an efficient image segmentation method for TPD images.

Abstract Background Image processing technologies have been developed in the past two decades to help clinicians diagnose tumors using medical images. Computer-aided diagnosis systems (CADs) have proven their ability to increase clinicians' detection rate of positive cases by 10% and have become integrated with many medical imaging systems and technologies. The study aimed to develop a hybrid algorithm to help doctors detect brain tumors from magnetic resonance imaging images. Results We were able to reach a detection accuracy of 96.6% and design a computer application that allows the user to enter the image and identify the location of the tumor in it if it exists with many additional features. Conclusions This approach can be improved by using different segmentation techniques, extracting additional features, or using other classifiers.

The detection of Martian polar ice cap change patterns is important for understanding their effects on driving Mars’s global water cycle and for regulating atmospheric circulation. However, current Martian ice cap identification using optical remote sensing data mainly relies on visual interpretation, which makes it difficult to quickly extract ice caps from multiple images and analyze their fine-scale spatiotemporal variation characteristics. Therefore, this study proposes an automatic Martian polar ice cap extraction algorithm for remote sensing data and analyzes the dynamic change characteristics of the Martian North Pole ice cap using time-series data. First, the automatic Martian ice cap segmentation algorithm was developed based on the ice cap features of high reflectance in the blue band and low saturation in the RGB band. Second, the Martian North Pole ice cap was extracted for the three Martian years MY25, 26, and 28 using Mars Orbiter Camera (MOC) Mars Daily Global Maps (MDGMs) data, which had better spatiotemporal continuity to analyze its variation characteristics. Lastly, the spatiotemporal variation characteristics of the ice cap and the driving factors of ice cap ablation were explored for the three aforementioned Martian years. The results indicated that the proposed automatic ice cap extraction algorithm had good performance, and the classification accuracy exceeded 93%. The ice cap ablation boundary retreat rates and spatiotemporal distributions were similar for the three years, with approximately 105 km2 of ice cap ablation for every one degree of areocentric longitude of the Sun (Ls). The main driving factor of ice cap ablation was solar radiation, which was mainly related to Ls. In addition, elevation had a different effect on ice cap ablation at different Ls in the same latitude area near the ablation boundary of the ice cap.

The efficient management of commercial orchards strongly requires accurate information on plant growing status for the implementation of necessary farming activities such as irrigation, fertilization, and pest control. Crown planar area and plant number are two very important parameters directly relating to fruit growth conditions and the final productivity of an orchard. In this study, in order to propose a novel and effective method to extract the crown planar area and number of mature and young papayas based on visible light images obtained from a DJ Phantom 4 RTK, we compared different vegetation indices (NGRDI, RGBVI, and VDVI), filter types (high- and low-pass filters), and filter convolution kernel sizes (3–51 pixels). Then, Otsu’s method was used to segment the crown planar area of the papayas, and the mean–standard deviation threshold (MSDT) method was used to identify the number of plants. Finally, the extraction accuracy of the crown planar area and number of mature and young papayas was validated. The results show that VDVI had the highest capability to separate the papayas from other ground objects. The best filter convolution kernel size was 23 pixels for the low-pass filter extraction of crown planar areas in mature and young plants. As to the plant number identification, segmentation could be set to the threshold with the highest F-score, i.e., the deviation coefficient n = 0 for single young papaya plants, n = 1 for single mature ones, and n = 1.4 for crown-connecting mature ones. Verification indicated that the average accuracy of crown planar area extraction was 93.71% for both young and mature papaya orchards and 95.54% for extracting the number of papaya plants. This set of methods can provide a reference for information extraction regarding papaya and other fruit trees with a similar crown morphology.

Extensive floating macroalgae have drifted from the East China Sea to Japan's offshore area, and field observation cannot sufficiently grasp their extensive spatial and temporal changes. High-spatial-resolution satellite data, which contain multiple spectral bands, have advanced remote sensing analysis. Several indexes for recognizing vegetation in satellite images, namely, the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and floating algae index (FAI), are useful for detecting floating macroalgae. Thresholds are defined to separate macroalgae-containing image pixels from other pixels, and adaptive thresholding increases the reliability of image segmentation. This study proposes adaptive thresholding using Sentinel-2 satellite data with a 10 m spatial resolution. We compare the abilities of Otsu's, exclusion, and standard deviation methods to define the floating macroalgae detection thresholds of NDVI, NDWI, and FAI images. This comparison determines the most advantageous method for the automatic detection of floating macroalgae. Finally, the spatial coverage of floating macroalgae and the reproducible combination needed for the automatic detection of floating macroalgae in Kagoshima, Japan, are examined. [ABSTRACT FROM AUTHOR]

OCR olarak da bilinen optik karakter tanıma, taranan görüntülerdeki bir kelimeyi ya da bir cümleyi tanımak için kullanılan bir yöntemdir. Uzun yıllara dayanan araştırmalarla geliştirilmiştir. Taranan görüntüler üzerindeki metni tespit etmede büyük başarı sağlamıştır. Ancak doğal görüntüler üzerinde istenilen sonucu vermemektedir. Bu nedenle, doğal görüntülerdeki metinleri tespit edebilmek için özel yaklaşımların geliştirilmesi gerekliliği doğmuştur. Bu çalışmada, doğal görüntüler üzerinde metin olan bölgeleri algılamak için Otsu ve maksimum kararlı ekstrem bölgeler (MSER) görüntü bölütleme yöntemleri kullanılmıştır. Görüntü bölütleme, bir görüntüyü daha iyi analiz edebilmek için görüntüyü anlamlı bölgelere ayırma işlemidir. Otsu modelinde görüntü için en uygun eşik değeri belirlenerek, görüntü bu eşik değerine göre ön plan ve arka plan olmak üzere iki sınıfa ayrılmaktadır. MSER yöntemi ise metin olmayan bölgeleri engelleyerek, metin olduğu düşünülen bölgeleri sınırlayıcı kutu içerisine almaktadır. Gerçekleştirilen çalışmada, Otsu metodu ve MSER yöntemi ile ICDAR 2013 veri setinden seçilen 20 doğal görüntü üzerinde metin olan bölgelerinin tespit edilmesi amaçlanmıştır. Doğal görüntü üzerinde bölütleme işlemleri yapıldıktan sonra görüntülere OCR uygulanarak doğal görüntüler üzerindeki metnin tespit edilmesi sağlanmış ve doğruluk oranları karşılaştırılmıştır.

Background: Image processing technologies have been developed in the past two decades to help clinicians diagnose tumors using medical images. Computer-aided diagnosis systems (CADs) have proven their ability to increase clinicians' detection rate of positive cases by 10% and have become integrated with many medical imaging systems and technologies. The study aimed to develop a hybrid algorithm to help doctors detect brain tumors from magnetic resonance imaging images. Results: We were able to reach a detection accuracy of 96.6% and design a computer application that allows the user to enter the image and identify the location of the tumor in it if it exists with many additional features. Conclusions: This approach can be improved by using different segmentation techniques, extracting additional features, or using other classifiers. [ABSTRACT FROM AUTHOR]

Summary: In the diagnosis of COVID‐19, investigation, analysis, and automatic counting of blood cell clusters are the most essential steps. Currently employed methods for cell segmentation, identification, and counting are time‐consuming and sometimes performed manually from sampled blood smears, which is hard and needs the support of an expert laboratory technician. The conventional method for the blood‐count‐test is by automatic hematology analyzer which is quite expensive and slow. Moreover, most of the unsupervised learning techniques currently available presume the medical practitioner to have a prior knowledge regarding the number and action of possible segments within the image before applying recognition. This assumption fails most often as the severity of the disease gets increased like the advanced stages of COVID‐19, lung cancer etc. In this manuscript, a simplified automatic histopathological image analysis technique and its hardware architecture suited for blind segmentation, cell counting, and retrieving the cell parameters like radii, area, and perimeter has been identified not only to speed up but also to ease the process of diagnosis as well as prognosis of COVID‐19. This is achieved by combining three algorithms: the K‐means algorithm, a novel statistical analysis technique‐HIST (histogram separation technique), and an islanding method an improved version of CCA algorithm/blob detection technique. The proposed method is applied to 15 chronic respiratory disease cases of COVID‐19 taken from high profile hospital databases. The output in terms of quantitative parameters like PSNR, SSIM, and qualitative analysis clearly reveals the usefulness of this technique in quick cytological evaluation. The proposed high‐speed and low‐cost architecture gives promising results in terms of performance of 190 MHz clock frequency, which is two times faster than its software implementation. [ABSTRACT FROM AUTHOR]

Clothing genres analysis, is a notorious topic in the field of computer vision and in multimedia. The major challenges faced in the segmentation of clothing images includes, numerous clothing variations, clothing deformation, view-invariant problems, skin ambiguities, and colour consistency degradation. To accomplish, the view-invariant problem, new Iterative Triclass Thresholding technique is implemented, which segments the cloths from human images. The thresholding segmentation algorithm has been designed, extensively for medical and natural images, not for segmenting the clothing images. In this research the potential of thresholding segmentation is analyzed in clothing and accomplishment of this framework is estimated by some samples of men's wear. From the Multi-View Clothing (MVC) dataset, 200 shirts with multi view face images have been selected. A test, on this dataset implies that, Iterative method can outperform the standard Otsu method. The experimental verification of this technique has been carried out, using MATLAB stimulation, which proved that the new Iterative Triclass Thresholding technique leads to effective results in the process of segmenting the clothing image while incurring low computational cost. [ABSTRACT FROM AUTHOR]

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There are various distributions of image histograms where regions form symmetrically or asymmetrically based on the frequency of the intensity levels inside the image. In pure image processing, the process of optimal thresholding tends to accurately separate each region in the image histogram to obtain the segmented image. Otsu's method is the most used technique in image segmentation. Otsu algorithm performs automatic image thresholding and returns the optimal threshold by maximizing between-class variance using the sum of Gaussian distribution for the intensity level in the histogram. There are various types of images where an intensity level has right-skewed histograms and does not fit with the between-class variance of the original Otsu algorithm. In this paper, we proposed an improvement of the between-class variance based on lognormal distribution, using the mean and the variance of the lognormal. The proposed model aims to handle the drawbacks of asymmetric distribution, especially for images with right-skewed intensity levels. Several images were tested for segmentation in the proposed model in parallel with the original Otsu method and the relevant work, including simulated images and Medical Resonance Imaging (MRI) of brain tumors. Two types of evaluation measures were used in this work based on unsupervised and supervised metrics. The proposed model showed superior results, and the segmented images indicated better threshold estimation against the original Otsu method and the related improvement. [ABSTRACT FROM AUTHOR]

Currently, biopolymers based on polylysine acids are widely used in various fields of science and technology, in particular medicine. Such biodegradable polymers are also used in the food industry as packaging material. With an increase in their prevalence, there is a need to study such polymers and the processes occurring in them by noninvasive methods, one of which is X-ray microtomography. In this work, a number of studies of the model object are carried out at various degrees of radiation monochromatization using a monochromator crystal and aluminum filters of various thicknesses to cut off the low-energy part of the polychromatic spectrum of an X-ray tube and at various exposure times. As a result of the experiments, a series of tomograms is obtained and software is created for calculating the morphological parameters of the objects under study. The data-processing program is based on the Otsu binarization algorithm for separating the sample and the background in the image, the Hough method for highlighting the boundaries of the sample, and the median filtering method to reduce the influence of impulse noise. Optimized parameters for microtomographic measurements are proposed to reduce the time and improve the quality of the study of the porous structure of biodegradable polymer samples. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate automatic image analysis for processing marks in femtosecond laser micromachining using concave and convex (unevenness) coefficient. To realize laser processing without thermal deformation and cracks, it's important to search optimum processing conditions such as scanning speed and pulse energy. Therefore, we analyzed a set of microscope image during laser microprocessing of glass, and evaluated the morphology such as depth and straightness of processing marks. For the laser microprocessing, an ultrashort laser system (Fianium, FP1060S-PP-D) with a wavelength of 1.06 µm, a pulse duration of 250 fs, and a repetition rate of 1 MHz was used. The sample to be processed was white glass substrates. The sample, which was mounted on the threedimensional stage, was scanned 10 mm in the x-axis direction with a scan speed of 0.1, 0.5, 1, and 2 mm/s. Also, for the laser microprocessing, the pulse energy was changed from 0.9 to 1.3 µJ. After laser microprocessing, processed mark was analyzed with concave and convex coefficient and Otsu's method. By making use of the concave and convex coefficient, luminance unevenness was reduced. We will introduce relationship between "scanning speed and/or pulse energy" and "gray scale and/or straightness of processing marks". [ABSTRACT FROM AUTHOR]

Extensive floating macroalgae have drifted from the East China Sea to Japan’s offshore area, and field observation cannot sufficiently grasp their extensive spatial and temporal changes. High-spatial-resolution satellite data, which contain multiple spectral bands, have advanced remote sensing analysis. Several indexes for recognizing vegetation in satellite images, namely, the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and floating algae index (FAI), are useful for detecting floating macroalgae. Thresholds are defined to separate macroalgae-containing image pixels from other pixels, and adaptive thresholding increases the reliability of image segmentation. This study proposes adaptive thresholding using Sentinel-2 satellite data with a 10 m spatial resolution. We compare the abilities of Otsu’s, exclusion, and standard deviation methods to define the floating macroalgae detection thresholds of NDVI, NDWI, and FAI images. This comparison determines the most advantageous method for the automatic detection of floating macroalgae. Finally, the spatial coverage of floating macroalgae and the reproducible combination needed for the automatic detection of floating macroalgae in Kagoshima, Japan, are examined.

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. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is pervasive worldwide, posing a high risk to people's safety and health. Many algorithms were developed to identify COVID-19. One way of identifying COVID-19 is by computed tomography (CT) images. Some segmentation methods are proposed to extract regions of interest from COVID-19 CT images to improve the classification. In this paper, an efficient version of the recent manta ray foraging optimization (MRFO) algorithm is proposed based on the oppositionbased learning called the MRFO-OBL algorithm. The original MRFO algorithm can stagnate in local optima and requires further exploration with adequate exploitation. Thus, to improve the population variety in the search space, we applied Opposition-based learning (OBL) in the MRFO's initialization step. MRFO-OBL algorithm can solve the image segmentation problem using multilevel thresholding. The proposed MRFO-OBL is evaluated using Otsu's method over the COVID-19 CT images and compared with six meta-heuristic algorithms: sine-cosine algorithm, moth flame optimization, equilibrium optimization, whale optimization algorithm, slap swarm algorithm, and original MRFO algorithm. MRFO-OBL obtained useful and accurate results in quality, consistency, and evaluation matrices, such as peak signal-to-noise ratio and structural similarity index. Eventually, MRFO-OBL obtained more robustness for the segmentation than all other algorithms compared. The experimental results demonstrate that the proposed method outperforms the original MRFO and the other compared algorithms under Otsu's method for all the used metrics. [ABSTRACT FROM AUTHOR]

Background. An analysis of the processes of image segmentation is being carried out. An original method of image segmentation using a convolutional neural network is proposed. Materials and methods. A comparative assessment of existing segmentation methods such as threshold segmentation methods: Otsu, Niblack, Bernsen, Savola, as well as the method of image segmentation using a convolutional neural network is carried out. Their advantages and disadvantages are evaluated. Examples of image segmentation by various methods are given. Algorithmic descriptions of segmentation methods are presented. Experiments were carried out to study the segmentation of frames (images) from a given video sequence. Results and conclusions. The results of the experiment, showing the operation of one or another segmentation method, are presented.

There are various distributions of image histograms where regions form symmetrically or asymmetrically based on the frequency of the intensity levels inside the image. In pure image processing, the process of optimal thresholding tends to accurately separate each region in the image histogram to obtain the segmented image. Otsu’s method is the most used technique in image segmentation. Otsu algorithm performs automatic image thresholding and returns the optimal threshold by maximizing between-class variance using the sum of Gaussian distribution for the intensity level in the histogram. There are various types of images where an intensity level has right-skewed histograms and does not fit with the between-class variance of the original Otsu algorithm. In this paper, we proposed an improvement of the between-class variance based on lognormal distribution, using the mean and the variance of the lognormal. The proposed model aims to handle the drawbacks of asymmetric distribution, especially for images with right-skewed intensity levels. Several images were tested for segmentation in the proposed model in parallel with the original Otsu method and the relevant work, including simulated images and Medical Resonance Imaging (MRI) of brain tumors. Two types of evaluation measures were used in this work based on unsupervised and supervised metrics. The proposed model showed superior results, and the segmented images indicated better threshold estimation against the original Otsu method and the related improvement.

The main disease that decreases the manufacturing of natural rubber is tapping panel dryness (TPD). To solve this problem faced by a large number of rubber trees, it is recommended to observe TPD images and make early diagnosis. Multi-level thresholding image segmentation can extract regions of interest from TPD images for improving the diagnosis process and increasing the efficiency. In this study, we investigate TPD image properties and enhance Otsu's approach. For a multi-level thresholding problem, we combine the snake optimizer with the improved Otsu's method and propose SO-Otsu. SO-Otsu is compared with five other methods: fruit fly optimization algorithm, sparrow search algorithm, grey wolf optimizer, whale optimization algorithm, Harris hawks optimization and the original Otsu's method. The performance of the SO-Otsu is measured using detail review and indicator reviews. According to experimental findings, SO-Otsu performs better than the competition in terms of running duration, detail effect and degree of fidelity. SO-Otsu is an efficient image segmentation method for TPD images.

Bougie location is the crucial step of information extraction in biochips. Many researchers have used algorithms, such as image brightness information or autocovariance, to grid the biochip images. However, these algorithms cannot resolve problems such as the bougie lattice being tilted or there being some bougies with insufficient hybridization. To resolve such problems, we propose a grid location algorithm based on the output of the fifth version of You Only Look Once (YOLOv5) for bougies in lattice biochip images. First, we use the YOLOv5 algorithm to detect and locate the bougies with sufficient hybridization. Second, we build a linear model for bougie location based on the least squares method and use the depth-first search algorithm to detect and locate the bougies with insufficient hybridization. Finally, we use Otsu's method to extract the gray values of grid areas. The algorithm was tested on a dataset consisting of 100 lattice biochip images. The experimental results show that the average accuracy is 99.76%, and the average detection time is 5.04 s. The algorithm has good robustness and high accuracy and can accurately locate the bougies. [ABSTRACT FROM AUTHOR]

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)