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Journal articles on the topic 'Otsu thresholding'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 June 2025

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1

Fajarianto, Gama Wisnu, Ahmad Hifdhul Abror, and Nur Hayatin. "Adaptif Range-Constrained Otsu Untuk Pemilihan Threshold Secara Otomatis Pada Histogram Citra Dengan Variansi Kelas Yang Tidak Seimbang." Register: Jurnal Ilmiah Teknologi Sistem Informasi 2, no. 1 (2016): 6. http://dx.doi.org/10.26594/r.v2i1.439.

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Abstrak Image Thresholding merupakan proses segmentasi untuk pemisahkan foreground dan background pada citra dengan cara membagi histogram citra menjadi dua kelas. Beberapa metode thresholding seperti Otsu dan Range-constrained Otsu menggunakan nilai variansi dari histogram untuk mendapatkan titik threshold, namun ketika menangani citra yang memiliki nilai variansi kelas foreground dan background tidak seimbang titik threshold yang dihasilkan kurang tepat. Paper ini mengusulkan metode Adaptif Range-constrained Otsu untuk mengatasi permasalahan variansi kelas yang tidak seimbang dengan cara mencari kelas yang memiliki nilai variansi lebih besar, untuk mendapatkan titik threshold yang lebih tepat. Pengujian menggunakan 22 NDT image dengan evaluasi misclassification error rate dan metode perankingan menunjukkan metode ini menghasilkan rerata ME 0.1153. Sedangkan Otsu sebesar 0.1746. Nilai rerata ranking 3.55, selisih 0.05 dibanding Kittler III. Hasil ini menunjukkan metode yang diusulkan kompetitif, terutama untuk segmentasi citra yang memiliki variansi kelas tidak sama. Kata kunci: segmentasi, thresholding, histogram, Otsu, Range-constrained. Abstract Image thresholding is segmentation process for separating foreground and background of an image by dividing image histogram into two classes. Several thresholding methods like Otsu and Rangeconstrained Otsu using the variance value of the histogram to get the threshold point, but when handling images that have unbalance class variance of the foreground and background produce less accurate threshold point. This paper proposes a method Adaptive Range-constrained Otsu to solve unbalance class variance problem by finding a class that has greater variance value to obtain more accurate threshold point. NDT testing using 22 images with misclassification error rate evaluation and ranking methods shows that this method results ME average of 0.1153, while Otsu method results 0.1746. The rankings mean value is 3.55, which has the difference of 0.05 when compared with Kittler III. These results show that the proposed method is competitive, especially for image segmentation with different class variance. Key word: segmentasi, thresholding, histogram, Otsu, Range-constrained.

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A.Jeevitha, A. Jeevitha, and P. Narendran P.Narendran. "BTS (Brain Tumor Segmentation)Based on Otsu Thresholding." Paripex - Indian Journal Of Research 2, no. 2 (2012): 53–55. http://dx.doi.org/10.15373/22501991/feb2013/17.

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3

XU, Chang-xin, and Guo-hua PENG. "Fast algorithm for 2D Otsu thresholding algorithm." Journal of Computer Applications 32, no. 5 (2013): 1258–60. http://dx.doi.org/10.3724/sp.j.1087.2012.01258.

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4

Goh, Ta Yang, Shafriza Nisha Basah, Haniza Yazid, Muhammad Juhairi Aziz Safar, and Fathinul Syahir Ahmad Saad. "Performance analysis of image thresholding: Otsu technique." Measurement 114 (January 2018): 298–307. http://dx.doi.org/10.1016/j.measurement.2017.09.052.

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5

Dong, Yan Xue. "Review of Otsu Segmentation Algorithm." Advanced Materials Research 989-994 (July 2014): 1959–61. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1959.

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Image segmentation is the key step in the process from image processing to image analysis. Otsu method is one of the most successful methods for image thresholding because of its simple calculation. Otsu method can select threshold automatically and divide the object from the background in the image. In this paper, various Otsu algorithm are studied.

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Yang, Guifeng, Jiulun Fan, and Dong Wang. "Recursive Algorithms of Maximum Entropy Thresholding on Circular Histogram." Mathematical Problems in Engineering 2021 (March 24, 2021): 1–13. http://dx.doi.org/10.1155/2021/6653031.

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Circular histogram thresholding is a novel color image segmentation method, which makes full use of the hue component color information of the image, so that the desired target can be better separated from the background. Maximum entropy thresholding on circular histogram is one of the exist circular histogram thresholding methods. However, this method needs to search for a pair of optimal thresholds on the circular histogram of two-class thresholding in an exhaustive way, and its running time is even longer than that of the existing circular histogram thresholding based on the Otsu criteria, so the segmentation efficiency is extremely low, and the real-time application cannot be realized. In order to solve this problem, a recursive algorithm of maximum entropy thresholding on circular histogram is proposed. Moreover, the recursive algorithm is extended to the case of multiclass thresholding. A large number of experimental results show that the proposed recursive algorithms are more efficient than brute force and the existing circular histogram thresholding based on the Otsu criteria.

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7

Saddami, Khairun, Khairul Munadi, Yuwaldi Away, and Fitri Arnia. "Improvement of binarization performance using local otsu thresholding." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (2019): 264. http://dx.doi.org/10.11591/ijece.v9i1.pp264-272.

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<p><span>Ancient document usually contains multiple noises such as uneven-background, show-through, water-spilling, spots, and blur text. The noise will affect the binarization process. Binarization is an extremely important process in image processing, especially for character recognition. This paper presents an improvement to Nina binarization technique. Improvements were achieved by reducing processing steps and replacing median filtering by Wiener filtering. First, the document background was approximated by using Wiener filter, and then image subtraction was applied. Furthermore, the manuscript contrast was adjusted by mapping intensity of image value using intensity transformation method. Next, the local Otsu thresholding was applied. For removing spotting noise, we applied labeled connected component. The proposed method had been testing on H-DIBCO 2014 and degraded Jawi handwritten ancient documents. It performed better regarding recall and precision values, as compared to Otsu, Niblack, Sauvola, Lu, Su, and Nina, especially in the documents with show-through, water-spilling and combination noises.</span></p>

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8

Alamsyah, Muslim. "Segmentasi Citra Iris Mata Menggunakan Metode Otsu Thresholding." JOINTECS (Journal of Information Technology and Computer Science) 4, no. 1 (2019): 23. http://dx.doi.org/10.31328/jointecs.v4i1.1001.

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Iridologi atau yang biasa disebut sebagai diagnosis iris adalah suatu metode kedokteran yang menyatakan bahwa tiap bagian pada tubuh manusia dapat direpresentasikan dengan wilayah yang terdapat pada iris mata (bagian yang berwarna pada pupil) dan dapat mengetahui seseorang yang telah mengalami gejala penyakit. Untuk menentukan gejala suatu penyakit yang diderita pasien pada umumnya akan dilakukan tes laboratorium, dimana tes ini cukup mahal dan terkadang menimbulkan luka serta hasilnya pun terkadang lama untuk diketahui, metode sebelumnya mampu melakukan segmentasi citra iris mata tapi sulit untuk pengambilan fokus iris mata yang akurat dan nilai tingkat pada akurasi karena antara sklera, iris mata dan pupil masih jadi satu. Oleh karena itu, pada penelitian ini mengimplementasikan segmentasi tiga kelas menggunakan metode otsu thresholding, metode otsu merupakan metode pencarian ambang batas atau treshold otomatis yang baik kemudian menggunakan metode tiga kelas dengan menentukan foreground, TBD (To Be Determinand) dan background. Metode ini mampu melakukan segmentasi citra iris mata dengan baik yaitu dengan akurasi 99.07 % dan nilai Area Under Curve (AUC) pada kurva relaive operating characteristic (ROC) sebesar 99.07 %.

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9

Yang, Xiaolu, Xuanjing Shen, Jianwu Long, and Haipeng Chen. "An Improved Median-based Otsu Image Thresholding Algorithm." AASRI Procedia 3 (2012): 468–73. http://dx.doi.org/10.1016/j.aasri.2012.11.074.

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10

ElBayoumi Harb, Suheir M., Nor Ashidi Mat Isa, and Samy A. Salamah. "Improved image magnification algorithm based on Otsu thresholding." Computers & Electrical Engineering 46 (August 2015): 338–55. http://dx.doi.org/10.1016/j.compeleceng.2015.03.025.

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11

Baihaqi, Wiga Maulana, Chyntia Raras Ajeng Widiawati, and Tegar Insani. "K-Means Clustering Based on Otsu Thresholding For Nucleus of White Blood Cells Segmentation." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, no. 5 (2020): 907–14. http://dx.doi.org/10.29207/resti.v4i5.2309.

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White blood cells function as the human immune system, and help defend the body against viruses. In clinical practice, identification and counting of white blood cells in blood smears is often used to diagnose many diseases such as infection, inflammation, malignancy, leukemia. In the past, examination of blood smears was very complex, manual tasks were tedious and time-consuming. This research proposes the k-means clustering algorithm to separate white blood cells from other parts. However, k-means clustering has a weakness that is when determining the initial prototype values, so the otsu thresholding method is used to determine the threshold by utilizing global values, then proceed with morphological operations to refine the segmentation image. The results of segmentation are measured by the Positive Predeictive Value (PPV) and Negative Positive Value (NPV) parameters. The results obtained prove that the use of otsu thresholding and morphological operations significantly increase the value of PPV compared to the value of PPV that does not use otsu thresholding. Whereas the NPV value increased but not significantly.

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12

Al-Rahlawee, Anfal Thaer Hussein, and Javad Rahebi. "Multilevel thresholding of images with improved Otsu thresholding by black widow optimization algorithm." Multimedia Tools and Applications 80, no. 18 (2021): 28217–43. http://dx.doi.org/10.1007/s11042-021-10860-w.

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13

Shahabi, Forough, Fereshteh Poorahangaryan, S. A. Edalatpanah, and Homayoun Beheshti. "A Multilevel Image Thresholding Approach Based on Crow Search Algorithm and Otsu Method." International Journal of Computational Intelligence and Applications 19, no. 02 (2020): 2050015. http://dx.doi.org/10.1142/s1469026820500157.

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Image segmentation is one of the fundamental problems in the image processing, which identifies the objects and other structures in the image. One of the widely used methods for image segmentation is image thresholding that can separate pixels based on the specified thresholds. Otsu method calculates the thresholds to divide two or multiple classes based on between-class variance maximization and within-class variance minimization. However, increasing the number of thresholds, surging the computational time of the segmentation. To combat this drawback, the combination of Otsu and the evolutionary algorithm is usually beneficial. Crow Search Algorithm (CSA) is a novel, and efficient swarm-based metaheuristic algorithm that inspired from the way crows storing and retrieving food. In this paper, we proposed a hybrid method based on employing CSA and Otsu for multilevel thresholding. The obtained results compared with the combination of the Otsu method with three other evolutionary algorithms consisting of improved Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and also the fuzzy version of FA. Our evaluation on the five benchmark images shows competitive/improved results both in time and uniformity.

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Sundaram, Ramakrishnan, Premaladha Jayaraman, R. Rangarajan, R. Rengasri, C. Rajeshwari, and K. S. Ravichandran. "Automated Optic Papilla Segmentation Approach Using Normalized Otsu Thresholding." Journal of Medical Imaging and Health Informatics 9, no. 7 (2019): 1346–53. http://dx.doi.org/10.1166/jmihi.2019.2783.

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15

Zhu, Xianyi, Yi Xiao, Guanghua Tan, Shizhe Zhou, Chi-Sing Leung, and Yan Zheng. "GPU-accelerated 2D OTSU and 2D entropy-based thresholding." Journal of Real-Time Image Processing 17, no. 4 (2019): 993–1005. http://dx.doi.org/10.1007/s11554-018-00848-5.

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16

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 between the proposed and existing firefly algorithms is carried using prevailing parameters such as objective function, standard deviation, peak-to-signal ratio (PSNR), structural similarity (SSIM) index, and search time of CPU. The results show that BD guided FA provides better objective function, PSNR, and SSIM, whereas LF based FA provides faster convergence with relatively lower CPU time.

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Tan, Zheng Yu, Shafriza Nisha Basah, Haniza Yazid, and Muhammad Juhairi Aziz Safar. "Performance analysis of Otsu thresholding for sign language segmentation." Multimedia Tools and Applications 80, no. 14 (2021): 21499–520. http://dx.doi.org/10.1007/s11042-021-10688-4.

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18

Barros, Wysterlânya K. P., Leonardo A. Dias, and Marcelo A. C. Fernandes. "Fully Parallel Implementation of Otsu Automatic Image Thresholding Algorithm on FPGA." Sensors 21, no. 12 (2021): 4151. http://dx.doi.org/10.3390/s21124151.

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This work proposes a high-throughput implementation of the Otsu automatic image thresholding algorithm on Field Programmable Gate Array (FPGA), aiming to process high-resolution images in real-time. The Otsu method is a widely used global thresholding algorithm to define an optimal threshold between two classes. However, this technique has a high computational cost, making it difficult to use in real-time applications. Thus, this paper proposes a hardware design exploiting parallelization to optimize the system’s processing time. The implementation details and an analysis of the synthesis results concerning the hardware area occupation, throughput, and dynamic power consumption, are presented. Results have shown that the proposed hardware achieved a high speedup compared to similar works in the literature.

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19

Dong, Yu Bing, Ming Jing Li, and Guang Liang Cheng. "Evaluation and Comparison of Thresholding Segmentation Techniques." Applied Mechanics and Materials 519-520 (February 2014): 689–92. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.689.

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Threshold technique is one of the important techniques in image segmentation. Various thresholding segmentation techniques such as histogram, grayscale expectations, Otsu, maximum entropy and iterative are studied and compared by using Matlab 7.0. Experimental results show that the iterative method can perform well and get a better result than the other thresholding segmentation methods.

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Khairuzzaman, Abdul Kayom Md, and Saurabh Chaudhury. "Brain MR Image Multilevel Thresholding by Using Particle Swarm Optimization, Otsu Method and Anisotropic Diffusion." International Journal of Applied Metaheuristic Computing 10, no. 3 (2019): 91–106. http://dx.doi.org/10.4018/ijamc.2019070105.

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Multilevel thresholding is widely used in brain magnetic resonance (MR) image segmentation. In this article, a multilevel thresholding-based brain MR image segmentation technique is proposed. The image is first filtered using anisotropic diffusion. Then multilevel thresholding based on particle swarm optimization (PSO) is performed on the filtered image to get the final segmented image. Otsu function is used to select the thresholds. The proposed technique is compared with standard PSO and bacterial foraging optimization (BFO) based multilevel thresholding techniques. The objective image quality metrics such as Peak Signal to Noise Ratio (PSNR) and Mean Structural SIMilarity (MSSIM) index are used to evaluate the quality of the segmented images. The experimental results suggest that the proposed technique gives significantly better-quality image segmentation compared to the other techniques when applied to T2-weitghted brain MR images.

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21

Nafi'iyah, Nur, and Retno Wardhani. "Perbandingan Otsu Dan Iterative Adaptive Thresholding Dalam Binerisasi Gigi Kaninus Foto Panoramik." Jurnal Ilmiah Teknologi Informasi Asia 11, no. 1 (2017): 21. http://dx.doi.org/10.32815/jitika.v11i1.39.

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Proses binerisasi bertujuan untuk memudahkan pengenalan citra dalam tahap computer vision. Binerisasi merupakan cara mengubah bentuk warna citra ke hitam putih atau biner. Metode otsu merupakan metode konversi citra ke bentuk hitam putih. Metode iterative dan adaptive thresholding merupakan gabungan metode dalam mengubah citra ke biner. Tujuan dari penelitian ini, yaitu: memudahkan dalam tahap ekstraksi citra atau pengambilan informasi terpenting dalam citra. Sehingga proses selanjutnya seperti pengenalan citra atau recognition. Hasil dari penelitian ini berupa citra biner gigi kaninus foto panoramik. Dari perbandingan metode, metode iterative dan adaptive thresholding menghasilkan gambar biner yang lebih baik.

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22

Fan, Chao-dong, Hong-lin Ouyang, and Ying-jie Zhang. "Small Probability Strategy Based Otsu Thresholding Method for Image Segmentation." Journal of Electronics & Information Technology 35, no. 9 (2014): 2081–87. http://dx.doi.org/10.3724/sp.j.1146.2012.01598.

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23

Pan Zhe, 潘喆, and 吴一全 Wu Yiquan. "The Two-dimensional Otsu Thresholding Based on Fish-swarm Algorithm." Acta Optica Sinica 29, no. 8 (2009): 2115–21. http://dx.doi.org/10.3788/aos20092908.2115.

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Wang, Na, Xia Li, and Xiao-hong Chen. "Fast three-dimensional Otsu thresholding with shuffled frog-leaping algorithm." Pattern Recognition Letters 31, no. 13 (2010): 1809–15. http://dx.doi.org/10.1016/j.patrec.2010.06.002.

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25

Liu, Chen Chung, Shyr Shen Yu, Chung Yen Tsai, and Ta Shan Tsui. "Pectoral Muscle Segmentation for Digital Mammograms Based on Otsu Thresholding." Applied Mechanics and Materials 121-126 (October 2011): 4537–41. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.4537.

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The appearance of pectoral muscle in medio-lateral oblique (MLO) views of mammograms can increase the false positive in computer aided detection (CAD) of breast cancer detection. Pectoral muscle has to be identified and segmented from the breast region in a mammogram before further analysis. The main goal of this paper is to propose an accurate and efficient algorithm of pectoral muscle extraction on MLO mammograms. The proposed algorithm bases on the positional characteristic of pectoral muscle in a breast region to combine the iterative Otsu thresholding scheme and the mathematic morphological processing to find the rough border of the pectoral muscle. The multiple regression analysis is then employed on the rough border to obtain the accurate segmentation of the pectoral muscle. The presented algorithm is tested on the digital mammograms from the Mammogram Image Analysis Society (MIAS) database. The experimental results show that the pectoral muscle extracted by the presented algorithm approximately follows that extracted by an expert radiologist.

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Satapathy, Suresh Chandra, N. Sri Madhava Raja, V. Rajinikanth, Amira S. Ashour, and Nilanjan Dey. "Multi-level image thresholding using Otsu and chaotic bat algorithm." Neural Computing and Applications 29, no. 12 (2016): 1285–307. http://dx.doi.org/10.1007/s00521-016-2645-5.

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27

Merzban, Mohamed H., and Mahmoud Elbayoumi. "Efficient solution of Otsu multilevel image thresholding: A comparative study." Expert Systems with Applications 116 (February 2019): 299–309. http://dx.doi.org/10.1016/j.eswa.2018.09.008.

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28

Suryani, E., E. I. Asmari, and B. Harjito. "Image Segmentation of Acute Myeloid Leukemia Using Multi Otsu Thresholding." Journal of Physics: Conference Series 1803, no. 1 (2021): 012016. http://dx.doi.org/10.1088/1742-6596/1803/1/012016.

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Singh, Neha, Ashish Kumar Bhandari, and Immadisetty Vinod Kumar. "Fusion-based contextually selected 3D Otsu thresholding for image segmentation." Multimedia Tools and Applications 80, no. 13 (2021): 19399–420. http://dx.doi.org/10.1007/s11042-021-10706-5.

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Oyebode, Kazeem Oyeyemi, and Jules Raymond Tapamo. "Automatic Segmentation of Cell Images by Improved Graph Cut-Based Approach." Journal of Biomimetics, Biomaterials and Biomedical Engineering 29 (October 2016): 74–80. http://dx.doi.org/10.4028/www.scientific.net/jbbbe.29.74.

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Cell segmentation provides an opportunity to reveal object of interest from the background of an image. In the traditional graph cut segmentation approach, the user initiates the segmentation process by selecting pixels for foreground and background. However, one of the problems of traditional graph cut is that it is time consuming, especially on a large dataset. Thus, we propose a fully automatic technique for cell segmentation on graph cut to automate the selection of sample foreground and background pixels. In order to achieve this, a combination of two methods namely Otsu thresholding and kmeans clustering algorithm is explored. The Otsu thresholding and the k-means provides an initial cell segmentation, creating a platform to automatically select sample foreground and background pixels initiating the graph cut segmentation. Experimental results on two public datasets suggest promising results.

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Pitoy, Pingkan Anggriani, and I. Putu Gede Hendra Suputra. "Dermoscopy Image Segmentation in Melanoma Skin Cancer using Otsu Thresholding Method." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 9, no. 3 (2021): 397. http://dx.doi.org/10.24843/jlk.2021.v09.i03.p11.

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Melanoma is a skin cancer that originates from melanocytes, melanin-producing cells in the skin. It requires quite a long time to detect melanoma through a biopsy. By utilizing technology, the time required to obtain biopsy results in detection of melanoma can be shortened using image pattern recognition. Segmentation is a stage that affects the results in image analysis for pattern recognition in digital images because of the accuracy of a confident segment in an image analysis. Otsu thresholding is a segmentation method aims to find the threshold point that divides the grayscale image of histogram into two different areas automatically. In this study, segmentation was carried out on 15 dermoscopy of melanoma images that were subjected to grayscaling, histogram, segmentation with Otsu Thresholding, binarization, image negation, and testing. The test carried out using the Receiver Operating Character (ROC) method exhibited a mean sensitivity level of 70.3%, a mean specificity level of 95.53%, and a mean accuracy of 94.82%.

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Widiyanto, Didit. "Tinjauan Algoritma RoI (Region of Interest) Dengan Metode Pengambangan Otsu Dan Klasterisasi K-Mean; Hasil Dan Tantangannya." Informatik : Jurnal Ilmu Komputer 16, no. 2 (2020): 75. http://dx.doi.org/10.52958/iftk.v16i2.1961.

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Akurasi sebuah klasifikasi citra ditentukan oleh pengklasifikasi. Meskipun RoI (Region of Interest) tidak menentukan secara langsung akurasi, namun RoI menentukan lingkup klasifikasi citra. Terdapat tiga algoritma yang dapat digunakan sebagai algoritma RoI yaitu; Balanced Histogram Thresholding (BHT), algoritma Otsu, dan algoritma klasterisasi K-Means. Paper ini meninjau algoritma Otsu dan algoritma klasterisasi K-Means yang digunakan oleh lima peneliti. Dari ke lima peneliti; tiga peneliti menerapkan algoritma Otsu dan dua peneliti menerapkan algoritma K-Means sebagai algoritma RoI. Setelah operasi RoI, ke lima peneliti menerapkan algoritma GLCM (Gray Level Co-occurance Matrix) sebagai pengekstraksi ciri tekstur. Hasil ekstraksi ciri diklasifikasi dengan menggunakan berbagai pengklasifikasi antara lain SVM (Support Vector Machine), Naive Bayes, dan Decision Tree. Akhirnya dengan membandingkan hasil dari ke lima peneliti, akurasi tertinggi diperoleh sebesar 100% dengan pengklasifikasi SVM menggunakan algoritma Otsu sebagai algoritma RoI, dan akurasi terendah adalah sebesar52% yang menggunakan algoritma Otsu pada kanal S dari citra HSV (Hue, Saturation Value).

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NG, HSIAO PIAU, SIM HENG ONG, KELVIN WENG CHIONG FOONG, POH SUN GOH, and WIESLAW L. NOWINSKI. "FUZZY C-MEANS ALGORITHM WITH LOCAL THRESHOLDING FOR GRAY-SCALE IMAGES." International Journal on Artificial Intelligence Tools 17, no. 04 (2008): 765–75. http://dx.doi.org/10.1142/s021821300800414x.

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An improved fuzzy C-means (FCM) clustering method is proposed. It incorporates Otsu thresholding with conventional FCM to reduce FCM's susceptibility to local minima, as well as its tendency to derive a threshold that is biased towards the component with larger probability, and derive threshold values with greater accuracy. Thresholding is performed at the cluster boundary region in feature space. A comparison of the results produced by improved and conventional algorithms confirms the superior performance of the former.

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Ma, Xing, Jun Li Han, and Chang Shun Liu. "Research on CCD Infrared Image Threshold Segmentation." Applied Mechanics and Materials 220-223 (November 2012): 1292–97. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.1292.

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In recent years, the gray-scale thresholding segmentation has emerged as a primary tool for image segmentation. However, the application of segmentation algorithms to an image is often disappointing. Based on the characteristics analysis of infrared image, this paper develops several gray-scale thresholding segmentation methods capable of automatic segmentation in regions of pedestrians of infrared image. The approaches of gray-scale thresholding segmentation method are described. Then the experimental system is established by using the infrared CCD device for pedestrian image detection. The image segmentation results generated by the algorithm in the experiment demonstrate that the Otsu thresholding segmentation method has achieved a kind of algorithm on automatic detection and segmentation of infrared image information in regions of interest of image.

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35

Rambabu, P., and C. Naga Raju. "The Optimal Thresholding Technique for Image Segmentaion Using Fuzzy Otsu Method." IAES International Journal of Artificial Intelligence (IJ-AI) 4, no. 3 (2015): 81. http://dx.doi.org/10.11591/ijai.v4.i3.pp81-88.

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<p>Image Segmentation plays a very important role in image processing. The single-mindedness of image segmentation is to partition the image into a set of disconnected regions with the homogeneous and uniform attributes like intensity, tone, color and texture. There are various methods for image segmentation but no method is suitable for low contrast images. In this paper, we are presenting an efficient and optimal thresholding image segmentation technique that can be used to separate the object and background pixels of the image to improve the quality of low contrast images. This innovative method consists of two steps. Firstly fuzzy logics are used to find optimum mean value using S-curve with automatic selection of controlled parameters to avoid the fuzziness in the image. Secondly, the fuzzy logic’s optimal threshold value used in Otsu method to improve the contrast of the image. This method, gives better results than traditional Otsu and Fuzzy logic techniques. The graphs and tables of values show that the proposed method is superior to traditional methods.</p>

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HE Zhi-yong, 何志勇, 孙立宁 SUN Li-ning, 黄伟国 HUANG Wei-guo, and 陈立国 CHEN Li-guo. "Thresholding segmentation algorithm based on Otsu criterion and line intercept histogram." Optics and Precision Engineering 20, no. 10 (2012): 2315–23. http://dx.doi.org/10.3788/ope.20122010.2315.

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Rasmana, Susijanto Tri, Yoyon K. Suprapto, and I. Ketut E. Purnama. "The New Otsu Thresholding for Binarization of the Ancient Copper Inscriptions." International Review on Computers and Software (IRECOS) 11, no. 10 (2016): 907. http://dx.doi.org/10.15866/irecos.v11i10.10359.

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Huang, Deng-Yuan, and Chia-Hung Wang. "Optimal multi-level thresholding using a two-stage Otsu optimization approach." Pattern Recognition Letters 30, no. 3 (2009): 275–84. http://dx.doi.org/10.1016/j.patrec.2008.10.003.

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Cao, Lian Lian, Sheng Ding, Xiao Wei Fu, and Li Chen. "Otsu multilevel thresholding segmentation based on quantum particle swarm optimisation algorithm." International Journal of Wireless and Mobile Computing 10, no. 3 (2016): 272. http://dx.doi.org/10.1504/ijwmc.2016.077215.

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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 convert the color and grayscale images to binary images. The idea is to accommodate the blue regions entirely with pores and transform it to white in resulting binary image. This paper presents the possibilities of using image processing for determining digital 2D rock samples porosity in carbonate reservoir rocks. MATLAB code created which automatically segment and determine the digital rock porosity, based on the OTSU's thresholding algorithm. In this work, twenty-two samples of 2D thin section petrographic image reservoir rocks of one Iraqi oil field are studied. The examples of thin section images are processed and digitized, utilizing MATLAB programming. In the present study, we have focused on determining of micro and macroporosity of the digital image. Also, some pore void characteristics, such as area and perimeter, were calculated. Digital 2D image analysis results are compared to laboratory core investigation results to determine the strength and restrictions of the digital image interpretation techniques. Thin microscopic image porosity determined using OTSU technique showed a moderate match with core porosity.

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Feng, Yuncong, Haiying Zhao, Xiongfei Li, Xiaoli Zhang, and Hongpeng Li. "A multi-scale 3D Otsu thresholding algorithm for medical image segmentation." Digital Signal Processing 60 (January 2017): 186–99. http://dx.doi.org/10.1016/j.dsp.2016.08.003.

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Tan, Yanli, and Yongqiang Zhao. "A Fast Otsu Thresholding Method Based on an Improved 2D Histogram." International Journal of Circuits, Systems and Signal Processing 15 (August 12, 2021): 953–59. http://dx.doi.org/10.46300/9106.2021.15.102.

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The regional division of a traditional 2D histogram is difficult to obtain satisfactory image segmentation results. Based on the gray level-gradient 2D histogram, we proposed a fast 2D Otsu method based on integral image. In this method, the average gray level is replaced by the gray level gradient in the neighborhood of pixels, and the edge features of the image are extracted according to the gray level difference between adjacent pixels to improve the segmentation effect. Calculating the integral image from the two-dimensional histogram reduces the computational complexity of searching the optimal threshold, thus reducing the amount of computation. The simulation results demonstrate that the proposed algorithm has better performance in image segmentation, with the increased computational speed and improved real-time capability.

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Rubini, S. Saranya, A. Kunthavai, M. B. Sachin, and S. Deepak Venkatesh. "Morphological Contour Based Blood Vessel Segmentation in Retinal Images Using Otsu Thresholding." International Journal of Applied Evolutionary Computation 9, no. 4 (2018): 48–63. http://dx.doi.org/10.4018/ijaec.2018100104.

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Retinal image analysis plays an important part in identifying various eye related diseases such as diabetic retinopathy (DR), glaucoma and many others. Accurate segmentation of blood vessels plays an important part in identifying the retinal diseases at an early stage. In this article, an unsupervised approach based on contour detection has been proposed for effective segmentation of retinal blood vessels. The proposed morphological contour-based blood vessel segmentation (MCBVS) method performs preprocessing using contrast limited adaptive histogram equalization followed by alternate sequential filtering to generate a noise-free image. The resultant image undergoes Otsu thresholding for candidate extraction followed by contour detection to properly segment the blood vessels. The MCBVS method has been tested on the DRIVE dataset and the experimental result shows that the proposed method achieved a sensitivity, specificity and accuracy of 58.79%, 90.77% and 86.7%, respectively. The MCBVS method performs better than the existing methods Sobel, Prewitt and Modified U-Net in terms of accuracy.

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Fachrurrozi, Muhammad, Erwin, Saparudin, Nur Rahma Dela, Yenita Mahyudin, and Hardians Kesuma Putra. "Tongue Image Segmentation using Hybrid Multilevel Otsu Thresholding and Harmony Search Algorithm." Journal of Physics: Conference Series 1196 (March 2019): 012072. http://dx.doi.org/10.1088/1742-6596/1196/1/012072.

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Yang, Shengli, Qiang Chen, and Ling Peng. "Bat algorithm for multilevel image thresholding based on Otsu and Kapur’s entropy." Journal of Physics: Conference Series 1982, no. 1 (2021): 012076. http://dx.doi.org/10.1088/1742-6596/1982/1/012076.

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Yazid, Haniza, Hamzah Arof, Hafizal Yazid, and Norazian Abd Razak. "Weld Detect Identification Using Texture Features and Dynamic Time Warping." Applied Mechanics and Materials 752-753 (April 2015): 1045–50. http://dx.doi.org/10.4028/www.scientific.net/amm.752-753.1045.

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In this paper, a simple yet robust algorithm for texture identification using 1 Dimensional Discrete Fourier Transform (1-D DFT) and Dynamic Time Warping (DTW) is presented with illumination variations. In the first stage, several image processing techniques namely Fuzzy C means (FCM) clustering, edge detection, Otsu thresholding and inverse surface thresholding method are utilized to locate the region of interest (ROI) where defects might exist. Next, the image undergoes the feature extraction process using 1-D DFT and finally, the features are classified using DTW. Several defect images consist of 2 types of defect namely the porosity and crack are experimented and classified using the DTW.

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Syahira M Zamani, N., Laily Azyan Ramlan, W. Mimi Diyana W Zaki, Aini Hussain, and Haliza Abdul Mutalib. "Mobile Screening Framework of Anterior Segment Photographed Images." International Journal of Engineering & Technology 7, no. 4.11 (2018): 85. http://dx.doi.org/10.14419/ijet.v7i4.11.20780.

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This work presents a qualitative measurement of anterior segment photographed images (ASPIs) to identify between normal eyes and eyes with pterygium and pinguecula through Otsu multi-thresholding approach without contrast enhancement. In addition, we also propose a mobile screening framework of ASPIs through smartphones. ASPIs were directly sent to the cloud storage once an ASPI was captured using a smartphone camera, and then each image was processed through a digital image processing approach in a processing platform. Three important steps, namely, pre-processing, image segmentation and qualitative assessment, are involved in the processing platform of the mobile screening framework. The ASPIs are pre-processed to minimise or eliminate any unwanted areas within the image. Then, these ASPIs are segmented through multi-thresholding Otsu approach with clustering number n = 3. Segmentation result shows that the accuracy of the proposed method is 87.5%, which is comparable with the previously established work that has applied three-step differencing (3SD) method. However, the proposed approach has better computational time which is six times faster than the 3SD method. These results demonstrate a remarkable effort to produce a simple but straightforward digital image processing approach to be implemented in cloud computing for future studies.

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Park, Seol Ah, Tamara Sipka, Zuzana Krivá, et al. "Macrophage Image Segmentation by Thresholding and Subjective Surface Method." Tatra Mountains Mathematical Publications 75, no. 1 (2020): 103–20. http://dx.doi.org/10.2478/tmmp-2020-0007.

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AbstractWe introduce two level-set method approaches to segmentation of 2D macrophage images. The first one is based on the Otsu thresholding and the second one on the information entropy thresholding, both followed by the classical subjective surface (SUBSURF) method. Results of both methods are compared with the semi-automatic Lagrangian method in which the segmentation curve evolves along the edge of the macrophage and it is controlled by an expert user. We present the comparison of all three methods with respect to the Hausdorff distance of resulting segmentation curves and we compare also their perimeter and enclosed area. We show that accuracy of the automatic SUBSURF method is comparable to the results of the semi-automatic Lagrangian segmentation.

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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 functions which are the between-class variance (the Otsu's function) and the entropy thresholding (the Kapur's function). Thus, two versions of the VNS metaheuristic are respectively obtained: the VNS-Otsu and the VNS-Kapur. These two novel proposed thresholding methods are tested on a set of benchmark brain MRI to show their robustness and proficiency.

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Renjith, Arokia, P. Manjula, and P. Mohan Kumar. "Brain tumour classification and abnormality detection using neuro-fuzzy technique and Otsu thresholding." Journal of Medical Engineering & Technology 39, no. 8 (2015): 498–507. http://dx.doi.org/10.3109/03091902.2015.1094148.

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