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Journal articles on the topic 'Otsu's algorithm'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Li, Shenghan, and Linlin Ye. "Multi-level thresholding image segmentation for rubber tree secant using improved Otsu's method and snake optimizer." Mathematical Biosciences and Engineering 20, no. 6 (2023): 9645–69. http://dx.doi.org/10.3934/mbe.2023423.

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<abstract><p>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

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Wang, Yanmei, Mingyu Lu, and Pengfei Feng. "Research on Road Extraction Algorithm Based on Improved Otsu’s Thresholding Method." Journal of Physics: Conference Series 2364, no. 1 (2022): 012064. http://dx.doi.org/10.1088/1742-6596/2364/1/012064.

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Abstract In this paper, the widely used method of Otsu's thresholding method is studied. On the basis of the traditional Otsu's thresholding method, according to the distribution characteristics of the target and background in the actual image, a new method based on traditional Otsu's thresholding method is presented.and the road extraction experiment is carried out with GF-2 remote sensing image.The experimental results show that the improved method has the advantages of high segmentation accuracy and strong anti noise ability compared with the traditional Otsu's thresholding method.

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Lee, Youngwoo, and Jin Heon Kim. "A Computational Improvement of Otsu's Algorithm by Estimating Approximate Threshold." Journal of Korea Multimedia Society 20, no. 2 (2017): 163–69. http://dx.doi.org/10.9717/kmms.2017.20.2.163.

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Soundarya, C., A. Kalaiselvi, and J. Surya. "Brain Tumor Detection Using Transfer Learning." Journal of Signal Processing 9, no. 1 (2023): 33–42. http://dx.doi.org/10.46610/josp.2023.v09i01.004.

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The main objective of the proposed work is to encounter the most serious condition of brain tumors. However, if caught early enough, a brain tumor can be cured. MRI scans and CT scans are used to diagnose brain tumors in most cases. It is far too difficult to accurately detect a tumor’s location and size. It is often difficult for doctors and patients to comprehend the outcomes. This paper targets to frame automated segmentation and classification of brain tumors. In this work, around 3000 MRI images (both tumors and non-tumors) are collected. To identify the images with tumors, Otsu's segment

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Chakraborty, Falguni, Provas Kumar Roy, and Debashis Nandi. "Symbiotic Organisms Search Optimization for Multilevel Image Thresholding." International Journal of Swarm Intelligence Research 11, no. 2 (2020): 31–61. http://dx.doi.org/10.4018/ijsir.2020040103.

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Determination of optimum thresholds is the prime concern of any multilevel image thresholding technique. The traditional methods for multilevel thresholding are computationally expensive, time-consuming, and also suffer from lack of accuracy and stability. To address this issue, the authors propose a new methodology for multilevel image thresholding based on a recently developed meta-heuristic algorithm, Symbiotic Organisms Search (SOS). The SOS algorithm has been inspired by the symbiotic relationship among the organism in nature. This article has utilized the concept of the symbiotic relatio

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Khairuzzaman, Abdul Kayom Md, and Saurabh Chaudhury. "Moth-Flame Optimization Algorithm Based Multilevel Thresholding for Image Segmentation." International Journal of Applied Metaheuristic Computing 8, no. 4 (2017): 58–83. http://dx.doi.org/10.4018/ijamc.2017100104.

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Multilevel thresholding is a popular image segmentation technique. However, computational complexity of multilevel thresholding increases very rapidly with increasing number of thresholds. Metaheuristic algorithms are applied to reduce computational complexity of multilevel thresholding. A new method of multilevel thresholding based on Moth-Flame Optimization (MFO) algorithm is proposed in this paper. The goodness of the thresholds is evaluated using Kapur's entropy or Otsu's between class variance function. The proposed method is tested on a set of benchmark test images and the performance is

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Sheng, Dong-Bo, Sang-Bong Kim, Trong-Hai Nguyen, Dae-Hwan Kim, Tian-Shui Gao, and Hak-Kyeong Kim. "Fish Injured Rate Measurement Using Color Image Segmentation Method Based on K-Means Clustering Algorithm and Otsu's Threshold Algorithm." Journal of the Korea Society For Power System Engineering 20, no. 4 (2016): 32–37. http://dx.doi.org/10.9726/kspse.2016.20.4.032.

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Miledi, Mariem, and Souhail Dhouib. "VNS Metaheuristic Based on Thresholding Functions for Brain MRI Segmentation." International Journal of Applied Metaheuristic Computing 12, no. 1 (2021): 94–110. http://dx.doi.org/10.4018/ijamc.2021010106.

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Image segmentation is a very crucial step in medical image analysis which is the first and the most important task in many clinical interventions. The authors propose in this paper to apply the variable neighborhood search (VNS) metaheuristic on the problem of brain magnetic resonance images (MRI) segmentation. In fact, by reviewing the literature, they notice that when the number of classes increases the computational time of the exhaustive methods grows exponentially with the number of required classes. That's why they exploit the VNS algorithm to optimize two maximizing thresholding functio

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Brahmaiah, Naik J., B. Rajasree, Panchakshari P. Durga, V. Sai, G. Sunny, and J. Raghavendra. "Multilevel thresholding image segmentation using mixed strategy improved convergence based whale optimization algorithm." i-manager’s Journal on Electronics Engineering 15, no. 2 (2025): 1. https://doi.org/10.26634/jele.15.2.21575.

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This paper presents a novel multilevel image segmentation method that leverages an enhanced Whale Optimization Algorithm (WOA). While WOA has shown promise in solving various optimization problems, its performance can be limited by susceptibility to local optima. To address this challenge, a Mixed-Strategy Improved Convergence WOA (MSICWOA) is proposed, which enhances the algorithm's optimization efficiency by incorporating a nonlinear convergence factor, an adaptive weight coefficient, and a k-point initialization technique. The MSICWOA is then applied alongside Otsu's crossvariance and Kapur

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Suma K. V. and Bheemsain Rao. "Detection of Rarefaction of Capillaries and Avascular Region in Nailfold Capillary Images." International Journal of Biomedical and Clinical Engineering 5, no. 2 (2016): 73–86. http://dx.doi.org/10.4018/ijbce.2016070106.

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Reduction in the capillary density in the nailfold region is frequently observed in patients suffering from Hypertension (Feng J, 2010). Loss of capillaries results in avascular regions which have been well characterized in many diseases (Mariusz, 2009). Nailfold capillary images need to be pre-processed so that noise can be removed, background can be separated and the useful parameters may be computed using image processing algorithms. Smoothing filters such as Gaussian, Median and Adaptive Median filters are compared using Mean Squared Error and Peak Signal-to-Noise Ratio. Otsu's thresholdin

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Bohush, Rykhard, Sergey Ablameyko, Tatiana Kalganova, and Pavel Yarashevich. "Extraction of image parking spaces in intelligent video surveillance systems." Machine Graphics and Vision 27, no. 1/4 (2019): 47–62. http://dx.doi.org/10.22630/mgv.2018.27.1.3.

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This paper discusses the algorithmic framework for image parking lot localization and classification for the video intelligent parking system. Perspective transformation, adaptive Otsu's binarization, mathematical morphology operations, representation of horizontal lines as vectors, creating and filtering vertical lines, and parking space coordinates determination are used for the localization of parking spaces in a~video frame. The algorithm for classification of parking spaces is based on the Histogram of Oriented Descriptors (HOG) and the Support Vector Machine (SVM) classifier. Parking lot

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Surmayanti, Surmayanti, Sumijan Sumijan, and Saiful Bukhori. "Development of watershed algorithm for identification of diabetic retinopathy based on fundus images." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 3 (2025): 2845. https://doi.org/10.11591/ijece.v15i3.pp2845-2856.

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Diabetic retinopathy (DR) is a serious complication of diabetes that can lead to blindness if not detected early. This research presents a novel method for the identification of DR using fundus images, employing the Watershed Algorithm for accurate image segmentation and the gray level co-occurrence matrix (GLCM) for texture feature extraction. The image processing pipeline involves several stages, including grayscale conversion, noise reduction through Gaussian and median filters, and Otsu's thresholding to isolate key features such as retinal lesions. The watershed algorithm is applied to de

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Alshami, Abdallah M. M., Hairol Nizam Mohd Shah, Mohd Fairus Abdollah, Mohd Zamzuri Ab Rashid, and Mohd Ali Arshad. "Pothole Boundary Detection Algorithm using Image Segmentation Technique in Urban Road." Journal of Advanced Research Design 132, no. 1 (2025): 66–77. https://doi.org/10.37934/ard.132.1.6677.

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Urban road safety is critically undermined by the presence of potholes, which necessitate accurate detection and assessment for effective maintenance. This study introduces a specialized algorithm designed to precisely detect and delineate the boundaries of potholes using advanced image segmentation techniques. Employing Otsu's method and Canny edge detection, the algorithm seeks to accurately identify and contour the edges of potholes in urban road imagery. The research extends beyond mere detection, focusing on the accurate characterization of potholes, determining their parameters and defin

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Hosseini, Hamed Shah. "Intelligent water drops algorithm for automatic multilevel thresholding of grey-level images using a modified Otsu's criterion." International Journal of Modelling, Identification and Control 15, no. 4 (2012): 241. http://dx.doi.org/10.1504/ijmic.2012.046402.

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Smalyuk, A. F., M. S. Dzeshka, and I. D. Kupchykava. "Segmentation of dermatoscopic images of skin lesions. Comparison of methods." «System analysis and applied information science», no. 1 (May 8, 2024): 50–58. http://dx.doi.org/10.21122/2309-4923-2024-1-50-58.

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The work discusses a number of techniques for segmenting dermoscopic images of skin lesions to identify the areas occupied by these lesions. Segmentation is necessary as the first stage of most methods of computer diagnostics of malignancy of neoplasms. A number of techniques, such as ABCDE, use the shape of the tumor as one of the criteria for making a diagnosis; for others, such as the use of convolutional neural networks, identifying the tumor allows one to increase the accuracy of the results obtained. The work discusses three methods of segmentation: thresholding using Otsu's method to ca

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Rajesh, Babu G., Lakshmi Himaja Palla, Vidya Sri Yalamanchili, Naga Karthik Bandaru, and Yaswanth Naga Sai Kiran Puppala. "Multilevel thresholding using K-point strategy improved convergence based whale optimization algorithm for image segmentation." i-manager’s Journal on Image Processing 12, no. 1 (2025): 22. https://doi.org/10.26634/jip.12.1.21733.

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The current study presents an innovative multilevel image segmentation method utilizing an improved Whale Optimization Algorithm (WOA). While WOA has shown promise in various optimization tasks, its performance can be limited by a tendency to be trapped in local optima. To address this challenge, the K-point Strategy Improved Convergence WOA (KSICWOA), which enhances optimization efficiency by incorporating a nonlinear convergence factor, an adaptive weight coefficient, and a k-point initialization strategy. The proposed KSICWOA is then applied alongside Otsu's cross variance and Kapur's entro

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Liu, Chang Qing, Bing Qi Chen, Yang Liu, and Tao Zha. "Quantity Detection of Kernels in an Ear Corn Based on Machine Vision." Applied Mechanics and Materials 246-247 (December 2012): 279–85. http://dx.doi.org/10.4028/www.scientific.net/amm.246-247.279.

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A high efficient method is provided to count the number of kernels in an ear corn. PC camera captured a sequence of images around an ear corn using a simple device. The Otsu's algorithm was applied for background segmentation. An object processing area was obtained after contour extraction. The x-direction cumulative histogram was used to extract every row of the ear corn. The y-direction cumulative histogram was used to detect the number of corn kernels in a row. The number of rows was got by matching the edge of the current ear row with the first one. The time of detecting kernels for an ear

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T., Lakshmi Narayana, Md. Ayesha S., Mahesh Nayak B., and Rajeswari G. "DETECTION AND CLASSIFICATION OF BRAIN TUMOUR IN MRI IMAGES USING PARTICLE SWARM OPTIMIZATION AND MACHINE LEARNING ALGORITHM." International Journal of Engineering Sciences & Emerging Technologies 11, no. 2 (2023): 79–89. https://doi.org/10.5281/zenodo.10435294.

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<em>Today, people of all ages suffer from headaches and pain in the internal areas of the brain caused by a tumour. Radiologists face a difficult problem when it comes to the detection and categorization of tumours from magnetic resonance image modalities because this organ is critical to the decision-making process regarding the operation of the entire human body system, and any incorrect identification could seriously harm someone's health. Therefore, a multilevel thresholding-based particle swarm optimization and support vector machine classifier are presented here to decrease the false-fin

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Lamphar, Héctor. "Spatio-temporal association of light pollution and urban sprawl using remote sensing imagery and GIS: A simple method based in Otsu's algorithm." Journal of Quantitative Spectroscopy and Radiative Transfer 251 (August 2020): 107060. http://dx.doi.org/10.1016/j.jqsrt.2020.107060.

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20

Tawfeeq, Yahya Jirjees, and Jalal A. Al-Sudani. "Digital Rock Samples Porosity Analysis by OTSU Thresholding Technique Using MATLAB." Iraqi Journal of Chemical and Petroleum Engineering 21, no. 3 (2020): 57–66. http://dx.doi.org/10.31699/ijcpe.2020.3.8.

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Porosity plays an essential role in petroleum engineering. It controls fluid storage in aquifers, connectivity of the pore structure control fluid flow through reservoir formations. To quantify the relationships between porosity, storage, transport and rock properties, however, the pore structure must be measured and quantitatively described. Porosity estimation of digital image utilizing image processing essential for the reservoir rock analysis since the sample 2D porosity briefly described. The regular procedure utilizes the binarization process, which uses the pixel value threshold to conv

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Nirmal Jothi Jerome, Sivasankari Jothiraj, Saranya Kandasamy, Divya Ramachandran, Dineshkumar Selvaraj, and Poonguzhali Ilango. "An Effective approach for Plant Disease Detection Using Assessment-Based Convolutional Neural Networks (A-CNN)." Journal of Advanced Research in Applied Sciences and Engineering Technology 31, no. 3 (2023): 155–72. http://dx.doi.org/10.37934/araset.31.3.155172.

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Agriculture is crucial in determining land availability and economic productivity in developing countries, where most of the population relies on this sector. Even though plant diseases are common, they are primarily identified in agriculture. Automated disease detection technology is necessary for the timely identification of plant diseases. Detecting plant diseases is crucial in avoiding loss of production and enhancing agricultural produce quality. Lack of proper attention in this area can cause severe damage to plants, resulting in loss of product quality, quantity, or productivity. Diagno

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Baydaulet, Urmashev, Buribayev Zholdas, Amirgaliyeva Zhazira, Ataniyazova Aisulu, Zhassuzak Mukhtar, and Turegali Amir. "Development of a weed detection system using machine learning and neural network algorithms." Eastern-European Journal of Enterprise Technologies 6, no. 2 (114) (2021): 70–85. https://doi.org/10.15587/1729-4061.2021.246706.

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The detection of weeds at the stages of cultivation is very important for detecting and preventing plant diseases and eliminating significant crop losses, and traditional methods of performing this process require large costs and human resources, in addition to exposing workers to the risk of contamination with harmful chemicals. To solve the above tasks, also in order to save herbicides and pesticides, to obtain environmentally friendly products, a program for detecting agricultural pests using the classical K-Nearest Neighbors, Random Forest and Decision Tree algorithms, as well as YOLOv5 ne

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Li, Hong, Zhong An Lai, and Jun Wei Lei. "Image Threshold Segmentation Algorithm Based on Histogram Statistical Property." Applied Mechanics and Materials 644-650 (September 2014): 4027–30. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.4027.

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The traditional Otsu threshold algorithm is not a good method for processing the real images because of complex shape and unbalanced distribution. To solve this problem, the paper uses the thinking of Otsu’s method for reference, introduces a threshold segmentation algorithm based on histogram statistical property. In addition, the paper draws a comparison between the new algorithm and Otsu’s algorithm. Experimental results show that the new algorithm can get better segmentation effect than that of Otsu’s method when the gray-level distribution of the background follows normal distribution app

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He, Li Fang, Xiong Tong, and Song Wei Huang. "Mineral Belt Image Segmentation Using Firefly Algorithm." Advanced Materials Research 989-994 (July 2014): 4074–77. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4074.

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Otsu is one of better image segmentation algorithm, which obtains optimum threshold by maximizing the between-class variance of an image. However, computation time will sharply increase as the threshold increase. In order to greatly improve the problem, a new segmentation algorithm called firefly algorithm based on Otsu’s method is presented in this paper, and applied the algorithm to segmentation of mineral belt image of shaking table, which lay the foundation for automation of shaking table. Experimental results show that the new algorithm can accurately segment different mineral belts and c

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Zahraa, Shihab Al Hakeem, Ismael Shahadi Haider, and Ismael Shahadi Haider. "An automatic flame detection system for outdoor areas." TELKOMNIKA 21, no. 04 (2023): 864–71. https://doi.org/10.12928/telkomnika.v21i4.24381.

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Traditional fire detection depends on smoke sensors. This strategy, however, is unsuited for big and open buildings, as well as outdoor regions. As a result, based on computer vision systems, this research proposes an effective method for recognizing flames in open areas. To minimize data size without losing important information, integer Haar lifting wavelet transform is used to frame and analyze the input video. Then, three color spaces (binary, hue, saturation, value (HSV), and YCbC) are used in simultaneous color detection. In binary space, Otsu’s approach is utilized to determine au

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Mendez, Enrico, Javier Piña Camacho, Jesús Arturo Escobedo Cabello, and Alfonso Gómez-Espinosa. "Autonomous Navigation and Crop Row Detection in Vineyards Using Machine Vision with 2D Camera." Automation 4, no. 4 (2023): 309–26. http://dx.doi.org/10.3390/automation4040018.

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In order to improve agriculture productivity, autonomous navigation algorithms are being developed so that robots can navigate along agricultural environments to automatize tasks that are currently performed by hand. This work uses machine vision techniques such as the Otsu’s method, blob detection, and pixel counting to detect the center of the row. Additionally, a commutable control is implemented to autonomously navigate a vineyard. Experimental trials were conducted in an actual vineyard to validate the algorithm. In these trials show that the algorithm can successfully guide the robot thr

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Chai, Ruishuai. "Otsu’s Image Segmentation Algorithm with Memory-Based Fruit Fly Optimization Algorithm." Complexity 2021 (March 25, 2021): 1–11. http://dx.doi.org/10.1155/2021/5564690.

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In this paper, the most common pepper noise in grayscale image noise is investigated in depth in the median filtering algorithm, and the improved median filtering algorithm, adaptive switching median filtering algorithm, and adaptive polar median filtering algorithm are applied to the OTSU algorithm. Two improved OTSU algorithms such as the adaptive switched median filter-based OTSU algorithm and the polar adaptive median filter-based OTSU algorithm are obtained. The experimental results show that the algorithm can better cope with grayscale images contaminated by pretzel noise, and the segmen

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Geng, Guoqing, Peining Wang, Liqin Sun, and Han Wen. "Enhanced Isolation Forest-Based Algorithm for Unsupervised Anomaly Detection in Lidar SLAM Localization." World Electric Vehicle Journal 16, no. 4 (2025): 209. https://doi.org/10.3390/wevj16040209.

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Lidar SLAM (simultaneous localization and mapping) systems provide vehicles with high-precision maps and localization for environmental perception. However, sensor noise and dynamic changes can lead to the localization drift or localization failure of the SLAM system. Identifying such anomalies currently relies on post-trajectory analysis with subjective parameter thresholds. To address this issue, we propose an unsupervised real-time localization anomaly detection model based on the isolation forest algorithm. We first determined the necessity of variable research through variable correlation

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Liu, Yuanyuan, Jiahui Sun, Haiye Yu, Yueyong Wang, and Xiaokang Zhou. "An Improved Grey Wolf Optimizer Based on Differential Evolution and OTSU Algorithm." Applied Sciences 10, no. 18 (2020): 6343. http://dx.doi.org/10.3390/app10186343.

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Aimed at solving the problems of poor stability and easily falling into the local optimal solution in the grey wolf optimizer (GWO) algorithm, an improved GWO algorithm based on the differential evolution (DE) algorithm and the OTSU algorithm is proposed (DE-OTSU-GWO). The multithreshold OTSU, Tsallis entropy, and DE algorithm are combined with the GWO algorithm. The multithreshold OTSU algorithm is used to calculate the fitness of the initial population. The population is updated using the GWO algorithm and the DE algorithm through the Tsallis entropy algorithm for crossover steps. Multithres

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Xing, Jiangwa, Pei Yang, and Letu Qingge. "Robust 2D Otsu’s Algorithm for Uneven Illumination Image Segmentation." Computational Intelligence and Neuroscience 2020 (August 11, 2020): 1–14. http://dx.doi.org/10.1155/2020/5047976.

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Otsu’s algorithm is one of the most well-known methods for automatic image thresholding. 2D Otsu’s method is more robust compared to 1D Otsu’s method. However, it still has limitations on salt-and-pepper noise corrupted images and uneven illumination images. To alleviate these limitations and improve the overall performance, here we propose an improved 2D Otsu’s algorithm to increase the robustness to salt-and-pepper noise together with an adaptive energy based image partition technology for uneven illumination image segmentation. Based on the partition method, two schemes for automatic thresh

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Guo, Laigong, and Sitong Wu. "FPGA Implementation of a Real-Time Edge Detection System Based on an Improved Canny Algorithm." Applied Sciences 13, no. 2 (2023): 870. http://dx.doi.org/10.3390/app13020870.

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Canny edge detection is one of the most widely used edge detection algorithms due to its superior performance. However, it is a complex, time-consuming process and has a high hardware cost. To overcome these issues, an improved Canny algorithm is proposed in this paper. It uses the Sobel operator and approximation methods to calculate the gradient magnitude and direction for replacing complex operations with reduced hardware costs. Otsu’s algorithm is introduced to adaptively determine the image threshold. However, Otsu’s algorithm has division operations, and the division operation is complex

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Lv, Chongshan, Ting Zhang, and Chengyuan Liu. "An Improved Otsu’s Thresholding Algorithm on Gesture Segmentation." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 2 (2017): 247–50. http://dx.doi.org/10.20965/jaciii.2017.p0247.

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In gesture recognition systems, segmenting gestures from complex background is the hardest and the most critical part. Gesture segmentation is the prerequisite of following image processing, and the result of segmentation has a direct influence on the result of gesture recognition. This paper proposed an algorithm of adaptive threshold gesture segmentation based on skin color. First of all, the image should be transformed from RGB color space to YCbCr color space. After eliminating luminance component Y, similarity graph of skin color will be obtained from the Gaussian model. Then Otsu adaptiv

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Jumiawi, Walaa Ali H., and Ali El-Zaart. "Improvement in the Between-Class Variance Based on Lognormal Distribution for Accurate Image Segmentation." Entropy 24, no. 9 (2022): 1204. http://dx.doi.org/10.3390/e24091204.

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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

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Charansiriphaisan, Kanjana, Sirapat Chiewchanwattana, and Khamron Sunat. "A Global Multilevel Thresholding Using Differential Evolution Approach." Mathematical Problems in Engineering 2014 (2014): 1–23. http://dx.doi.org/10.1155/2014/974024.

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Otsu’s function measures the properness of threshold values in multilevel image thresholding. Optimal threshold values are necessary for some applications and a global search algorithm is required. Differential evolution (DE) is an algorithm that has been used successfully for solving this problem. Because the difficulty of a problem grows exponentially when the number of thresholds increases, the ordinary DE fails when the number of thresholds is greater than 12. An improved DE, using a new mutation strategy, is proposed to overcome this problem. Experiments were conducted on 20 real images a

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Oliva, Diego, Erik Cuevas, Gonzalo Pajares, Daniel Zaldivar, and Marco Perez-Cisneros. "Multilevel Thresholding Segmentation Based on Harmony Search Optimization." Journal of Applied Mathematics 2013 (2013): 1–24. http://dx.doi.org/10.1155/2013/575414.

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In this paper, a multilevel thresholding (MT) algorithm based on the harmony search algorithm (HSA) is introduced. HSA is an evolutionary method which is inspired in musicians improvising new harmonies while playing. Different to other evolutionary algorithms, HSA exhibits interesting search capabilities still keeping a low computational overhead. The proposed algorithm encodes random samples from a feasible search space inside the image histogram as candidate solutions, whereas their quality is evaluated considering the objective functions that are employed by the Otsu’s or Kapur’s methods. G

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Seyyedabbasi, Amir. "A Hybrid Multi-Strategy Optimization Metaheuristic Algorithm for Multi-Level Thresholding Color Image Segmentation." Applied Sciences 15, no. 13 (2025): 7255. https://doi.org/10.3390/app15137255.

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Hybrid metaheuristic algorithms have been widely used to solve global optimization problems, making the concept of hybridization increasingly important. This study proposes a new hybrid multi-strategy metaheuristic algorithm named COSGO, which combines the strengths of grey wolf optimization (GWO) and Sand Cat Swarm Optimization (SCSO) to effectively address global optimization tasks. Additionally, a chaotic opposition-based learning strategy is incorporated to enhance the efficiency and global search capability of the algorithm. One of the main challenges in metaheuristic algorithms is premat

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Sri Madhava Raja, N., V. Rajinikanth, and K. Latha. "Otsu Based Optimal Multilevel Image Thresholding Using Firefly Algorithm." Modelling and Simulation in Engineering 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/794574.

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Histogram based multilevel thresholding approach is proposed using Brownian distribution (BD) guided firefly algorithm (FA). A bounded search technique is also presented to improve the optimization accuracy with lesser search iterations. Otsu’s between-class variance function is maximized to obtain optimal threshold level for gray scale images. The performances of the proposed algorithm are demonstrated by considering twelve benchmark images and are compared with the existing FA algorithms such as Lévy flight (LF) guided FA and random operator guided FA. The performance assessment comparison b

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Dong, Yan Xue. "An Improved Otsu Image Segmentation Algorithm." Advanced Materials Research 989-994 (July 2014): 3751–54. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3751.

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Otsu segmentation algorithm is one of the most successful algorithms in the field of image processing,However, it has drawbacks such as it is poor in dividing small object in images. This paper propose a improved Otsu algorithm based on weighted smooth histogram which can ensure the location of threshold point is more near to the valley point of the image and at the same time ensure the ensure between-class variance maximum.The results show that the improved Otsu algorithm can effectively accomplish segmentation for multimodal image, and get better segmentation results for images added Gaussia

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Pan, Feifei, Xiaohuan Xi, and Cheng Wang. "A Comparative Study of Water Indices and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery." Remote Sensing 12, no. 10 (2020): 1611. http://dx.doi.org/10.3390/rs12101611.

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A comparative study of water indices and image classification algorithms for mapping inland water bodies using Landsat imagery was carried out through obtaining 24 high-resolution (≤5 m) and cloud-free images archived in Google Earth with the same (or ±1 day) acquisition dates as the Landsat-8 OLI images over 24 selected lakes across the globe, and developing a method to generate the alternate ground truth data from the Google Earth images for properly evaluating the Landsat image classification results. In addition to the commonly used green band-based water indices, Landsat-8 OLI’s ultra-blu

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Zheng, Jianfeng, Yinchong Gao, Han Zhang, Yu Lei, and Ji Zhang. "OTSU Multi-Threshold Image Segmentation Based on Improved Particle Swarm Algorithm." Applied Sciences 12, no. 22 (2022): 11514. http://dx.doi.org/10.3390/app122211514.

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In view of the slow convergence speed of traditional particle swarm optimization algorithms, which makes it easy to fall into local optimum, this paper proposes an OTSU multi-threshold image segmentation based on an improved particle swarm optimization algorithm. After the particle swarm completes the iterative update speed and position, the method of calculating particle contribution degree is used to obtain the approximate position and direction, which reduces the scope of particle search. At the same time, the asynchronous monotone increasing social learning factor and the asynchronous mono

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Liu, Tianfu, and Chunqiu Tang. "Local Dimming Algorithm of Automotive LCD Instrument Based on Otsu and Maximum Entropy." Journal of Nanomaterials 2022 (May 25, 2022): 1–9. http://dx.doi.org/10.1155/2022/5244088.

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In order to reasonably reduce the energy consumption of automotive LCD instrument and improve the display quality, a new local dimming algorithm is proposed in this paper. Firstly, the gray image of the input image is obtained by using the maximum principle, and then, the LED backlight brightness value is obtained by using Otsu method and maximum entropy method. The BMA backlight smoothing algorithm is improved by combining bilinear interpolation algorithm, and the dimming image is obtained by using pixel compensation algorithm based on logarithm. Then, for low brightness images, high brightne

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Alexanderani, Mohammad K., Mohamed Omar, Matthew Greenblatt, et al. "Abstract 2584: Decoding colon cancer recurrence: Unveiling accurate predictions with attention-guided deep neural networks on histopathological whole slide images." Cancer Research 84, no. 6_Supplement (2024): 2584. http://dx.doi.org/10.1158/1538-7445.am2024-2584.

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Abstract Background: Up to 40% of colon cancer patients are at high risk of cancer recurrence, yet accurate and timely prediction tools are lacking. Leveraging whole slide images (WSIs) and deep learning models, we aimed to develop precise algorithm for prediction of colon cancer recurrence. Thus, enables risk stratification for optimized therapeutic interventions and improved health outcomes. Design: We designed an attention-based deep learning model for predicting colon cancer recurrence on paraffin-embedded, hematoxylin and eosin-stained colon tissue biopsies of digital slides. The WSIs wer

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Dong, Yuxue, Mengxia Li, and Mengxiang Zhou. "Multi-Threshold Image Segmentation Based on the Improved Dragonfly Algorithm." Mathematics 12, no. 6 (2024): 854. http://dx.doi.org/10.3390/math12060854.

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In view of the problems that the dragonfly algorithm has, such as that it easily falls into the local optimal solution and the optimization accuracy is low, an improved Dragonfly Algorithm (IDA) is proposed and applied to Otsu multi-threshold image segmentation. Firstly, an elite-opposition-based learning optimization is utilized to enhance the diversity of the initial population of dragonflies, laying the foundation for subsequent algorithm iterations. Secondly, an enhanced sine cosine strategy is introduced to prevent the algorithm from falling into local optima, thereby improving its abilit

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Raheem, Khamael Raqim, and Hafedh Ali Shabat. "An Otsu thresholding for images based on a nature-inspired optimization algorithm." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 2 (2023): 933. http://dx.doi.org/10.11591/ijeecs.v31.i2.pp933-944.

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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

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Khamael, Raqim Raheem, and Ali Shabat Hafedh. "An Otsu thresholding for images based on a nature-inspired optimization algorithm." An Otsu thresholding for images based on a nature-inspired optimization algorithm 31, no. 2 (2023): 933–44. https://doi.org/10.11591/ijeecs.v31.i2.pp933-944.

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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

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Karakoyun, Murat, Nurdan Akhan Baykan, and Mehmet Hacibeyoglu. "Multi-Level Thresholding for Image Segmentation With Swarm Optimization Algorithms." International Research Journal of Electronics and Computer Engineering 3, no. 3 (2017): 1. http://dx.doi.org/10.24178/irjece.2017.3.3.01.

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Image segmentation is an important problem for image processing. The image processing applications are generally affectedfromthe segmentation success. There is noany image segmentation method which gives good results for all sorts of images. That’s why there are many approaches and methods forimage segmentationin the literature. And one of the most used is the thresholding technique. Thresholding techniques can be categorized into two topics: bi-level and multi-level thresholding. Bi-level thresholding technique has one threshold value which separates the image into two groups. However, multi-

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Neeraj Bhargava, Dr, Anchal kumawat, and Dr Ritu Bhargava. "Threshold and binarization for document image analysis using otsu’s Algorithm." International Journal of Computer Trends and Technology 17, no. 5 (2014): 272–75. http://dx.doi.org/10.14445/22312803/ijctt-v17p150.

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Shen, Lingli. "Implementation of CT Image Segmentation Based on an Image Segmentation Algorithm." Applied Bionics and Biomechanics 2022 (October 12, 2022): 1–11. http://dx.doi.org/10.1155/2022/2047537.

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With the increasingly important role of image segmentation in the field of computed tomography (CT) image segmentation, the requirements for image segmentation technology in related industries are constantly improving. When the hardware resources can fully meet the needs of the fast and high-precision image segmentation program system, the main means of how to improve the image segmentation effect is to improve the related algorithms. Therefore, this study has proposed a combination of genetic algorithm (GA) and Great Law (OTSU) algorithm to form an image segmentation algorithm-immune genetic

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Işıker, Hakan, and Caner Özdemir. "A Multi-Thresholding Method Based on Otsu’s Algorithm for the Detection of Concealed Threats in Passive Millimeter-Wave Images." Frequenz 73, no. 5-6 (2019): 179–87. http://dx.doi.org/10.1515/freq-2018-0255.

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Abstract In this study, an algorithm to the detection and imaging of hidden arms for passive millimeter-wave (PMMW) imaging systems is proposed. This technique is; in fact, an improved version of our previously developed auto-classification algorithm by extending it by exploiting the Otsu’s multi-level thresholding method. The detailed derivation and the brief steps of the proposed algorithm are given. The proposed algorithm is tested and validated by real PMMW images obtained by a real radiometric imaging system. Resultant measured images are obtained with the employment of signal and image p

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Anger, Philipp M., Leonhard Prechtl, Martin Elsner, Reinhard Niessner, and Natalia P. Ivleva. "Implementation of an open source algorithm for particle recognition and morphological characterisation for microplastic analysis by means of Raman microspectroscopy." Analytical Methods 11, no. 27 (2019): 3483–89. http://dx.doi.org/10.1039/c9ay01245a.

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