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Journal articles on the topic 'Image thresholding'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Huang, Hui Xian, Juan Gong, and Te Zhang. "Method of Adaptive Wavelet Thresholding Used in Image Denoising." Advanced Materials Research 204-210 (February 2011): 1184–87. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.1184.

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According to multi-resolution analysis of wavelet threshold denoising principle, this paper presented two improved algorithms of continuity and adaptive threshold based on hard thresholding. The soft thresholding (hyperbolic thresholding) was used in the intervals after setting two thresholds, and the isolated points were removed according to the adjacent correlation coefficient during the processing. As a result, the hard thresholding’s shortcomings were reduced. The simulation results show that improved algorithms have both better visual effect and PSNR than the traditional approaches.

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Reining, Lars C., and Thomas S. A. Wallis. "A psychophysical evaluation of techniques for Mooney image generation." PeerJ 12 (September 27, 2024): e18059. http://dx.doi.org/10.7717/peerj.18059.

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Mooney images can contribute to our understanding of the processes involved in visual perception, because they allow a dissociation between image content and image understanding. Mooney images are generated by first smoothing and subsequently thresholding an image. In most previous studies this was performed manually, using subjective criteria for generation. This manual process could eventually be avoided by using automatic generation techniques. The field of computer image processing offers numerous techniques for image thresholding, but these are only rarely used to create Mooney images. Fu

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Riyaz, Mohammed M., and M. Sabibullah. "Thresholding techniques in computer vision applications." i-manager’s Journal on Image Processing 11, no. 2 (2024): 27. http://dx.doi.org/10.26634/jip.11.2.21001.

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Thresholding techniques are key pillars of image processing, especially for distinguishing objects in complex environments. This paper examines four types of thresholding strategies, each based on different theories, practical, popular, and advanced. Through a thorough literature review, the paper explains the thresholding techniques, thresholding operations, evaluation metrics, image processing techniques, and Python code for ROI of binary images in an understandable manner. The findings underscore the significance of thresholding in various applications, from object recognition to medical im

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Senthilkumaran, N1 and Vaithegi S2. "TOP 1 CITED PAPER - COMPUTER SCIENCE & ENGINEERING: AN INTERNATIONAL JOURNAL (CSEIJ)." COMPUTER SCIENCE & ENGINEERING: AN INTERNATIONAL JOURNAL (CSEIJ) 6, no. 1 (2019): 3. https://doi.org/10.5281/zenodo.3386005.

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Image binarization is the process of separation of pixel values into two groups, black as background and white as foreground. Thresholding can be categorized into global thresholding and local thresholding. This paper describes a locally adaptive thresholding technique that removes background by using local mean and standard deviation. Most common and simplest approach to segment an image is using thresholding. In this work we present an efficient implementation for threshoding and give a detailed comparison of Niblack and sauvola local thresholding algorithm. Niblack and sauvola thresholding

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KHASHMAN, ADNAN, and BORAN SEKEROGLU. "DOCUMENT IMAGE BINARISATION USING A SUPERVISED NEURAL NETWORK." International Journal of Neural Systems 18, no. 05 (2008): 405–18. http://dx.doi.org/10.1142/s0129065708001671.

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Advances in digital technologies have allowed us to generate more images than ever. Images of scanned documents are examples of these images that form a vital part in digital libraries and archives. Scanned degraded documents contain background noise and varying contrast and illumination, therefore, document image binarisation must be performed in order to separate foreground from background layers. Image binarisation is performed using either local adaptive thresholding or global thresholding; with local thresholding being generally considered as more successful. This paper presents a novel m

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Manda, Manikanta Prahlad, and Hi Seok Kim. "A Fast Image Thresholding Algorithm for Infrared Images Based on Histogram Approximation and Circuit Theory." Algorithms 13, no. 9 (2020): 207. http://dx.doi.org/10.3390/a13090207.

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Image thresholding is one of the fastest and most effective methods of detecting objects in infrared images. This paper proposes an infrared image thresholding method based on the functional approximation of the histogram. The one-dimensional histogram of the image is approximated to the transient response of a first-order linear circuit. The threshold value for the image segmentation is formulated using combinational analogues of standard operators and principles from the concept of the transient behavior of the first-order linear circuit. The proposed method is tested on infrared images gath

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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 quali

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Li, Qingyong, Weitao Lu, and Jun Yang. "A Hybrid Thresholding Algorithm for Cloud Detection on Ground-Based Color Images." Journal of Atmospheric and Oceanic Technology 28, no. 10 (2011): 1286–96. http://dx.doi.org/10.1175/jtech-d-11-00009.1.

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Abstract Cloud detection is the precondition for deriving other information (e.g., cloud cover) in ground-based sky imager applications. This paper puts forward an effective cloud detection approach, the Hybrid Thresholding Algorithm (HYTA) that fully exploits the benefits of the combination of fixed and adaptive thresholding methods. First, HYTA transforms an input color cloud image into a normalized blue/red channel ratio image that can keep a distinct contrast, even with noise and outliers. Then, HYTA identifies the ratio image as either unimodal or bimodal according to its standard deviati

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Chandrakala, M. "Image Analysis of Sauvola and Niblack Thresholding Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 2353–57. http://dx.doi.org/10.22214/ijraset.2021.34569.

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Image segmentation is a critical problem in computer vision and other image processing applications. Image segmentation has become quite challenging over the years due to its widespread use in a variety of applications. Image thresholding is a popular image segmentation technique. The segmented image quality is determined by the techniques used to determine the threshold value.A locally adaptive thresholding method based on neighborhood processing is presented in this paper. The performance of locally thresholding methods like Niblack and Sauvola was demonstrated using real-world images, print

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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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Widodo, Rulisiana, Tessy Badriyah, Iwan Syarif, and Willy Sandhika. "SEGMENTATION OF LUNG CANCER IMAGE BASED ON CYTOLOGIC EXAMINATION USING THRESHOLDING METHOD." Jurnal Ilmiah Kursor 12, no. 1 (2023): 41–48. http://dx.doi.org/10.21107/kursor.v12i01.277.

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Lung cancer is the most dangerous cases which mostly attacks the man with the biggest causes of smoking. This cancer threatens the second largest death after heart attack, lung cancer cases increase significantly every year in various countries. Several methods have been established to detect lung cancer, including Computed Tomography of the thorax, sputum examination and cytology examination. The most decisive examination is through cytologic examination of the pleural fluid. However, the current state of biopsy performed by doctors does not always get a lot of specimens, making it difficult

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Chikanbanjar, Milan. "Comparative analysis between non-linear wavelet based image denoising techniques." Journal of Science and Engineering 5 (August 31, 2018): 58–67. http://dx.doi.org/10.3126/jsce.v5i0.22373.

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Digital images have been a major form of transmission of visual information, but due to the presence of noise, the image gets corrupted. Thus, processing of the received image needs to be done before being used in an application. Denoising of image involves data manipulation to remove noise in order to produce a good quality image retaining different details. Quantitative measures have been used to show the improvement in the quality of the restored image by the use of various thresholding techniques by the use of parameters mainly, MSE (Mean Square Error), PSNR (Peak-Signal-to-Noise-Ratio) an

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Phanindra Kumar N.S.R. and Prasad Reddy P.V.G.D. "Evolutionary Image Thresholding for Image Segmentation." International Journal of Computer Vision and Image Processing 9, no. 1 (2019): 17–34. http://dx.doi.org/10.4018/ijcvip.2019010102.

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Image segmentation is a method of segregating the image into required segments/regions. Image thresholding being a simple and effective technique, mostly used for image segmentation, these thresholds are optimized by optimization techniques by maximizing the Tsallis entropy. However, as the two level thresholding extends to multi-level thresholding, the computational complexity of the algorithm is further increased. So there is need of evolutionary and swarm optimization techniques. In this article, first time optimal thresholds are obtained by maximizing the Tsallis entropy by using novel hyb

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Mohamad Roslan, Muhammad Ammar, Aimi Salihah Abdul Nasir, Marni Azira Markom, Allan Melvin Andrew, and Edy Victor Haryanto. "COVID-19 Chest X-Ray Lung Segmentation by Locally Adaptive Thresholding." Journal of Advanced Research in Applied Sciences and Engineering Technology 64, no. 3 (2025): 69–83. https://doi.org/10.37934/araset.64.3.6983.

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The novel coronavirus disease 2019 (COVID-19), first identified in December 2019 in Wuhan, China, rapidly escalated into a global pandemic. Effective and reliable solutions for automated detection and large-scale screening are crucial to monitor and control the spread of COVID-19. However, distinguishing between COVID-19 and pneumonia in chest X-ray (CXR) scans remains a challenge for radiologists due to overlapping image features. Additionally, modern diagnostic methods such as reverse transcription polymerase chain reaction (RT-PCR) are expensive, complex, and time-consuming. This study aims

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Aoun, Mohammed Salah Mesai, Bachir Dehda, and Bachir Douib. "A new image compression method based on comparative thresholding." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e11258. https://doi.org/10.54021/seesv5n2-588.

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In recent years, image compression methods are the important research topics, these methods are classified into reversible and irreversiblet techniques. The irreversible techniques or called transformation techniques are the most widely used today. In fact, the transformation methods decompose the image into an orthonormal basis, such as a great number of the coefficients be equal to zero, without significantly impairing the visual aspect of the original image. Decades ago, the wavelet theory has given high contributions in the transformation methods by its nice mathematical properties and usi

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Kalthom Adam H. Ibrahim, Mohammed Abdallah Almaleeh, Moaawia Mohamed Ahmed, and Dalia Mahmoud Adam. "Images Processing for Segmentation Neisseria Bacteria Cells." World Journal of Advanced Research and Reviews 12, no. 3 (2021): 573–79. http://dx.doi.org/10.30574/wjarr.2021.12.3.0672.

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This paper introduces the segmentation of Neisseria bacterial meningitis images. Images segmentation is an operation of identifying the homogeneous location in a digital image. The basic idea behind segmentation called thresholding, which be classified as single thresholding and multiple thresholding. To perform images segmentation, transformations and morphological operations processes are used to segment the images, as well as image transformation an edge detecting, filling operation, design structure element, and arithmetic operations technique is used to implement images segmentation. The

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Kalthom, Adam H. Ibrahim, Abdallah Almaleeh Mohammed, Mohamed Ahmed Moaawia, and Mahmoud Adam Dalia. "Images Processing for Segmentation Neisseria Bacteria Cells." World Journal of Advanced Research and Reviews 12, no. 3 (2021): 573–79. https://doi.org/10.5281/zenodo.5820372.

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This paper introduces the segmentation of <em>Neisseria </em>bacterial meningitis images. Images segmentation is an operation of identifying the homogeneous location in a digital image. The basic idea behind segmentation called thresholding, which be classified as single thresholding and multiple thresholding. To perform images segmentation, transformations and morphological operations processes are used to segment the images, as well as image transformation an edge detecting, filling operation, design structure element, and arithmetic operations technique is used to implement images

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CHENG, H. D., YANHUI GUO, and YINGTAO ZHANG. "A NOVEL APPROACH TO IMAGE THRESHOLDING BASED ON 2D HOMOGENEITY HISTOGRAM AND MAXIMUM FUZZY ENTROPY." New Mathematics and Natural Computation 07, no. 01 (2011): 105–33. http://dx.doi.org/10.1142/s1793005711001834.

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Image thresholding is an important topic for image processing, pattern recognition and computer vision. Fuzzy set theory has been successfully applied to many areas, and it is generally believed that image processing bears some fuzziness in nature. In this paper, we employ the newly proposed 2D homogeneity histogram (homogram) and the maximum fuzzy entropy principle to perform thresholding. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can select the thresholds automatically and effectively. Especially, it not only can pro

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Roshan, A., and Y. Zhang. "MOVING OBJECT DETECTION USING SPATIAL CORRELATION IN LAB COLOUR SPACE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W12 (May 9, 2019): 173–77. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w12-173-2019.

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<p><strong>Abstract.</strong> Background subtraction-based techniques of moving object detection are very common in computer vision programs. Each technique of background subtraction employs image thresholding algorithms. Different thresholding methods generate varying threshold values that provide dissimilar moving object detection results. A majority of background subtraction techniques use grey images which reduce the computational cost but statistics-based image thresholding methods do not consider the spatial distribution of pixels. In this study, authors have developed

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CALZADA-NAVARRETE, V., and C. TORRES-HUITZIL. "A LOCAL ADAPTIVE THRESHOLD APPROACH TO ASSIST AUTOMATIC CHROMOSOME IMAGE SEGMENTATION." Latin American Applied Research - An international journal 44, no. 3 (2014): 277–82. http://dx.doi.org/10.52292/j.laar.2014.452.

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In cytogenetics, karyotype analysis is used to assess the presence of genetic defects by visualization chromosomes structure from microscopic images. A key step in this process is image thresholding, used to detect and extract objects of interest from background, as it affects the performance of further processing steps in image analysis. In this paper, an adaptive local thresholding for Qband chromosome image segmentation is presented. A re-threshold process based on the Sauvola’s local adaptive technique is applied to extract chromosomes from background. Local adaptive histogram equalization

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Jumiawi, Walaa Ali H., and Ali El-Zaart. "Gumbel (EVI)-Based Minimum Cross-Entropy Thresholding for the Segmentation of Images with Skewed Histograms." Applied System Innovation 6, no. 5 (2023): 87. http://dx.doi.org/10.3390/asi6050087.

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In this study, we delve into the realm of image segmentation, a field characterized by a multitude of approaches; one frequently used technique is thresholding-based image segmentation. This process divides intensity levels into different regions based on a specified threshold value. Minimum Cross-Entropy Thresholding (MCET) stands out as an independent objective function that can be applied with any distribution and is regarded as a mean-based thresholding method. In certain cases, images exhibit diverse structures that result in different histogram distributions. Some images possess symmetri

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Ramadhan, Redho Putra, Pahrizal Pahrizal, and Yovi Apridiansyah. "Eksperimen Perbandingan Otsu Thresholding dan Canny Edge Detection Terhadap Peningkatan Kualitas Citra Beresolusi Rendah." Ranah Research : Journal of Multidisciplinary Research and Development 7, no. 3 (2025): 2210–16. https://doi.org/10.38035/rrj.v7i3.1575.

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In today's digital era, digital images play an important role in various fields, including image processing, surveillance systems, and medical image processing. However, one of the main challenges is that low-resolution images often experience quality degradation, so the loss of important details caused by noise and blurring makes the information in the image unclear. To overcome this problem, various image processing methods have been developed, including the Otsu Thresholding and Canny Edge Detection methods. This study will compare two methods for improving the quality of low-resolution ima

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P.D., Sathya. "IMAGE SEGMENTATION WITH IMPROVED BACTERIAL FORAGING ALGORITHM." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES5 5, no. 3 (2018): 110–18. https://doi.org/10.5281/zenodo.1404195.

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Image thresholding is an important technique for image processing and pattern recognition. Multilevel thresholding problem is often treated as a problem of optimization of an objective function. In this paper, minimum cross entropy (MCE) is introduced for multilevel thresholding which uses Improved Bacterial Foraging (IBF) algorithm for minimizing the MCE objective function. Some examples of test images are presented to compare the segmentation methods based on the IBF approach, with bacterial foraging (BF) algorithm, particle swarm optimization (PSO) algorithm and genetic algorithm (GA). From

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S, Anbazhagan. "APPLICATION OF TEACHING LEARNING BASED OPTIMIZATION IN MULTILEVEL IMAGE THRESHOLDING." ICTACT Journal on Image and Video Processing 11, no. 4 (2021): 2413–22. http://dx.doi.org/10.21917/ijivp.2021.0344.

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This paper proposes a Teaching learning-based optimization (TLBO) algorithm for the multilevel image thresholding using Kapur entropy. In image processing, the thresholding arises to help medical imaging, detection, and recognition in making an informed decision about the image. However, they are computationally expensive reaching out to multilevel thresholding since they thoroughly search the optimal thresholds to enhance the fitness functions. In order to validate the chaotic characteristic of multilevel thresholding, a TLBO algorithm is modeled. The proposed model is an algorithm-specific,

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Gill, Tarnjot Kaur, and Aman Arora. "Evaluating the performance of different image binarization techniques." COMPUSOFT: An International Journal of Advanced Computer Technology 03, no. 11 (2014): 1294–99. https://doi.org/10.5281/zenodo.14768316.

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Image binarization is the methodology of separating of pixel values into dual collections, dark as frontal area and white as background. Thresholding has discovered to be a well-known procedure utilized for binarization of document images. Thresholding is further divided into global and local thresholding technique. In document with contrast delivery of background and foreground, global thresholding is discovered to be best technique. In corrupted documents, where extensive background noise or difference in contrast and brightness exists i.e. there exists numerous pixels that cannot be effortl

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O'Mara, Aidan R., Jessica M. Collins, Anna E. King, James C. Vickers та Matthew T. K. Kirkcaldie. "Accurate and Unbiased Quantitation of Amyloid-β Fluorescence Images Using ImageSURF". Current Alzheimer Research 16, № 2 (2019): 102–8. http://dx.doi.org/10.2174/1567205016666181212152622.

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Background: Images of amyloid-β pathology characteristic of Alzheimer’s disease are difficult to consistently and accurately segment, due to diffuse deposit boundaries and imaging variations. Methods: We evaluated the performance of ImageSURF, our open-source ImageJ plugin, which considers a range of image derivatives to train image classifiers. We compared ImageSURF to standard image thresholding to assess its reproducibility, accuracy and generalizability when used on fluorescence images of amyloid pathology. Results: ImageSURF segments amyloid-β images significantly more faithfully, and wit

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Bottega and Dongiovanni. "Diesel Spray Macroscopic Parameter Estimation Using a Synthetic Shapes Database." Applied Sciences 9, no. 23 (2019): 5248. http://dx.doi.org/10.3390/app9235248.

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The paper presents a method for the macroscopic characterization of diesel sprays starting from digital images. Macroscopic spray characterization mainly consists in the definition of two parameters, namely penetration and cone angle. The latter can be evaluated according to many possible definitions, all based on the spray contour that is obtained by means of image thresholding. Therefore, the obtained cone angle value depends on the adopted angle definition and on the used thresholding algorithm. In order to avoid this double dependence, an alternative method has hence been proposed. The alg

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Jasim, Wala’a, and Rana Mohammed. "A Survey on Segmentation Techniques for Image Processing." Iraqi Journal for Electrical and Electronic Engineering 17, no. 2 (2021): 73–93. http://dx.doi.org/10.37917/ijeee.17.2.10.

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The segmentation methods for image processing are studied in the presented work. Image segmentation can be defined as a vital step in digital image processing. Also, it is used in various applications including object co-segmentation, recognition tasks, medical imaging, content based image retrieval, object detection, machine vision and video surveillance. A lot of approaches were created for image segmentation. In addition, the main goal of segmentation is to facilitate and alter the image representation into something which is more important and simply to be analyzed. The approaches of image

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Kwon, Soon H. "Image-Edge based Thresholding of Gray Images." Journal of Korean Institute of Intelligent Systems 30, no. 3 (2020): 189–94. http://dx.doi.org/10.5391/jkiis.2020.30.3.189.

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Nobuhara, Hajime, and Kaoru Hirota. "A Fuzzification of Morphological Wavelets Based on Fuzzy Relational Calculus and its Application to Image Compression/Reconstruction." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 4 (2004): 373–78. http://dx.doi.org/10.20965/jaciii.2004.p0373.

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A new style of fuzzy wavelets is proposed by the fuzzification of morphological wavelets. Due to the correspondence of the morphological wavelets operations and fuzzy relational ones, wavelets analysis/synthesis schemes can be formulated based on fuzzy relational calculus. To enable efficient image compression/reconstruction, the concept of the alpha-band which is an alpha-cut generalization, is also proposed for thresholding wavelets. In an image compression/reconstruction experiment using test images extracted from the Standard Image DataBAse (SIDBA), it is confirmed that the root mean squar

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Dehda, Bachir, and Mohammed Salah Mesai Aoun. "An efficient method for image denoising based on a new nonlinear wavelet thresholding function." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e11193. https://doi.org/10.54021/seesv5n2-583.

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Image noise is random variation of brightness or color information in images, and is usually an aspect of electronic noise. It can be produced by the image sensor and circuitry of a scanner or digital camera. In fact, there are different kinds of noise functions such as Gaussian noise, salt and pepper noise and speckle noise. Hence, image denoising is the process of removing noise from an image using one of the denoising methods such as spatial or transform techniques. The main aim of an image denoising technique is to achieve both noise reduction and feature preservation. In this context, the

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Nombo, Josiah, Alfred Mwambela, and Michael Kisngiri. "Analysis and Performance Evaluation of Entropic Thresholding Image Processing Techniques for Electrical Capacitance Tomography Measurement System." Tanzania Journal of Science 47, no. 3 (2021): 928–42. http://dx.doi.org/10.4314/tjs.v47i3.5.

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To improve image quality generated from the electrical capacitance tomography measurement system, the use of entropic thresholding techniques is investigated in this article. Based on the analysis of the principle of Electrical Capacitance Tomography (ECT) image reconstruction and entropic thresholding, various algorithms have been proposed for easy extraction of quantitative information from tomograms generated from the ECT system. Experiments indicate that proposed algorithms can provide high-quality images at no or minimum computational cost. It is easier to implement and integrate with cla

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P.D., Sathya. "MINIMUM CROSS ENTROPY BASED IMAGE SEGMENTATION USING NEW HEURISTIC OPTIMIZATION TECHNIQUE." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 5, no. 7 (2019): 522–30. https://doi.org/10.5281/zenodo.2656443.

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Image thresholding is an important technique for image processing and pattern recognition. Multilevel thresholding problem is often treated as a problem of optimization of an objective function. In this paper, minimum cross entropy (MCE) is introduced for multilevel thresholding which uses Improved Bacterial Foraging (IBF) algorithm for minimizing the MCE objective function. Some examples of test images are presented to compare the segmentation methods based on the IBF approach, with bacterial foraging (BF) algorithm, particle swarm optimization (PSO) algorithm and genetic algorithm (GA). From

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Srikanth M. V., V. V. K. D. V. Prasad, and K. Satya Prasad. "An Improved Firefly Algorithm-Based 2-D Image Thresholding for Brain Image Fusion." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 3 (2020): 60–96. http://dx.doi.org/10.4018/ijcini.2020070104.

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In this article, an attempt is made to diagnose brain diseases like neoplastic, cerebrovascular, Alzheimer's, and sarcomas by the effective fusion of two images. The two images are fused in three steps. Step 1. Segmentation: The images are segmented on the basis of optimal thresholding, the thresholds are optimized with an improved firefly algorithm (pFA) by assuming Renyi entropy as an objective function. Earlier, image thresholding was performed with a 1-D histogram, but it has been recently observed that a 2-D histogram-based thresholding is better. Step 2: the segmented features are extrac

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Chandra De, Utpal, Madhabananda Das, Debashis Mishra, and Debashis Mishra. "Threshold based brain tumor image segmentation." International Journal of Engineering & Technology 7, no. 3 (2018): 1801. http://dx.doi.org/10.14419/ijet.v7i3.12425.

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Image processing is most vital area of research and application in field of medical-imaging. Especially it is a major component in medical science. Starting from radiology to ultrasound (sonography), MRI, etc. in lots of area image is the only source of diagnosis process. Now-a-days, different types of devices are being introduced to capture the internal body parts in medical science to carry the diagnosis process correctly. However, due to various reasons, the captured images need to be tuned digitally to gain the more information. These processes involve noise reduction, segmentations, thres

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Song, Shuwu, Mengyang Liao, and Jamei Qin. "Multiresolution image dynamic thresholding." Machine Vision and Applications 3, no. 1 (1990): 13–16. http://dx.doi.org/10.1007/bf01211448.

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Xue, Jing-Hao, and D. Michael Titterington. "Median-based image thresholding." Image and Vision Computing 29, no. 9 (2011): 631–37. http://dx.doi.org/10.1016/j.imavis.2011.06.003.

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Manda, Manikanta Prahlad, and Daijoon Hyun. "Double Thresholding with Sine Entropy for Thermal Image Segmentation." Traitement du Signal 38, no. 6 (2021): 1713–18. http://dx.doi.org/10.18280/ts.380614.

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Traditional thresholding methods are often used for image segmentation of real images. However, due to distinct characteristics of infrared thermal images, it is difficult to ensure an optimal image segmentation using the traditional thresholding algorithms, and therefore, sometimes this can lead to over-segmentation, missing object information, and/or spurious responses in the output. To overcome these issues, we propose a new thresholding technique that makes use of the sine entropy-based criterion. Moreover, we build a double thresholding technique that makes use of two thresholds to get th

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Prahara, Murinto, and Erik Ujianto. "Multilevel Thresholding Image Segmentation Based-Logarithm Decreasing Inertia Weight Particle Swarm Optimization." International Journal of Advances in Soft Computing and its Applications 14, no. 3 (2022): 65–77. http://dx.doi.org/10.15849/ijasca.221128.05.

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Abstract The image segmentatation technique that is often used is thresholding. Image segmentation is a process of dividing the image into different regions according to their similar characteristics. This research proposes a multilevel thresholding algorithm using modified particle swarm optimization to solve a segmentation problem. The threshold optimal values are determined by maximizing Otsu’s objective function using optimization technique namely particle swarm optimization based on the logarithmic decreasing inertia weight (LogDIWPSO). The proposed method reduces the computational time t

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Badgainya, Shruti, Prof Pankaj Sahu, and Prof Vipul Awasthi. "Image Denoising for AWGN Corrupted Image Using OWT and Thresholding." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (2018): 220–26. http://dx.doi.org/10.31142/ijtsrd18338.

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T. A. A., Enow, Ngalle H. B., and Ngonkeu M. E. L. "Automated Estimation of Plant Leaf Disease Severity Using Classical Image Segmentation Techniques." Biotechnology Journal International 29, no. 2 (2025): 59–76. https://doi.org/10.9734/bji/2025/v29i2772.

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Aim: This study aimed to propose a computationally cost-effective method for automated estimation of plant leaf disease severity in resource-limited settings. Study Design: The performance of four image segmentation algorithms—global thresholding, adaptive thresholding, Otsu thresholding, and edge detection—was evaluated using nine curated images of disease-affected leaves from tomato, bell pepper, and potato plants. Each image was segmented into healthy and diseased regions, and quantitative metrics—including diseased pixel counts, percentage of affected area, healthy-to-diseased ratios, and

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Jiang, Chundi, Wei Yang, Yu Guo, Fei Wu, and Yinggan Tang. "Nonlocal Means Two Dimensional Histogram-Based Image Segmentation via Minimizing Relative Entropy." Entropy 20, no. 11 (2018): 827. http://dx.doi.org/10.3390/e20110827.

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Spatial correlation information between pixels is considered to be very important in thresholding methods. However, it is often ignored and thus unsatisfied segmentation results maybe obtained. To overcome this shortcoming, we propose a new image segmentation approach by taking not only pixels’ spatial information but also pixels’s gray level into account. First, a non-local mean filter is imposed on the image. Then the filtered image and the original image together are adopted to build a two dimensional histogram, it is called non-local mean two dimensional histogram. Finally, a minimum relat

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Wonohadidjojo, Daniel Martomanggolo. "Performance Comparison of Firefly and Cuckoo Search Algorithms in Optimal Thresholding of Cancer Cell Images." ComTech: Computer, Mathematics and Engineering Applications 10, no. 1 (2019): 29. http://dx.doi.org/10.21512/comtech.v10i1.5632.

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This research presented a performance comparison of the two methods in cancer cells image processing. Each method consisted of two stages. The first stage was image enhancement using fuzzy sets. The second stage was optimal fuzzy entropy based image thresholding. In the thresholding stage, the first method used Firefly Algorithm (FA) and the second used Cuckoo Search (CS). In both methods, four performance metrics (Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structured Similarity Indexing Method (SSIM), and Feature Similarity Indexing Method (FSIM)) and variance and entropy of

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Panchaxri, N. Jagadale Basavaraj, S. Priya B, and N. Nargund Mukund. "Image Denoising using Adaptive NL Means Filtering with Method Noise Thresholding." Indian Journal of Science and Technology 14, no. 39 (2021): 2961–70. https://doi.org/10.17485/IJST/v14i39.1532.

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Abstract <strong>Background/Objectives:</strong> Image denoising is an important step in image processing applications. Usually noise is added to the original image during transmission, acquisition and storage process and is considered as noisy image. For precise analysis and extraction of image features, the noisy image is denoised without losing the original image details. This study aims to introduce a novel denoising method to obtain denoised image(s) such that it has fewer artifacts and is more efficient at higher noise levels. <strong>Method:</strong> The proposed novel de

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Prahara, Adhi, Andri Pranolo, Nuril Anwar, and Yingchi Mao. "Parallel Approach of Adaptive Image Thresholding Algorithm on GPU." Knowledge Engineering and Data Science 4, no. 2 (2022): 69. http://dx.doi.org/10.17977/um018v4i22021p69-84.

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Image thresholding is used to segment an image into background and foreground using a given threshold. The threshold can be generated using a specific algorithm instead of a pre-defined value obtained from observation or experiment. However, the algorithm involves per pixel operation, histogram calculation, and iterative procedure to search the optimum threshold that is costly for high-resolution images. In this research, parallel implementations on GPU for three adaptive image thresholding methods, namely Otsu, ISODATA, and minimum cross-entropy, were proposed to optimize their computational

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Wang, Xiangluo, Chunlei Yang, Guo-Sen Xie, and Zhonghua Liu. "Image Thresholding Segmentation on Quantum State Space." Entropy 20, no. 10 (2018): 728. http://dx.doi.org/10.3390/e20100728.

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Aiming to implement image segmentation precisely and efficiently, we exploit new ways to encode images and achieve the optimal thresholding on quantum state space. Firstly, the state vector and density matrix are adopted for the representation of pixel intensities and their probability distribution, respectively. Then, the method based on global quantum entropy maximization (GQEM) is proposed, which has an equivalent object function to Otsu’s, but gives a more explicit physical interpretation of image thresholding in the language of quantum mechanics. To reduce the time consumption for searchi

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CHENG, H. D., and YANHUI GUO. "A NEW NEUTROSOPHIC APPROACH TO IMAGE THRESHOLDING." New Mathematics and Natural Computation 04, no. 03 (2008): 291–308. http://dx.doi.org/10.1142/s1793005708001082.

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A neutrosophic set (Ns), a part of neutrosophy theory, studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. The neutrosophic set is a powerful general formal framework that has been recently proposed. However, the neutrosophic set needs to be specified from a technical point of view. We apply the neutrosophic set in image domain and define some concepts and operations for image thresholding. The image G is transformed into Ns domain, which is described using three subsets T, I and F. The entropy in neutrosophic set is defined a

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Shareefunnisa, Syed, and M. Bhargavi. "Solving Image Thresholding Problem Using Hybrid Algorithm." International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (2017): 736–40. http://dx.doi.org/10.31142/ijtsrd7021.

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Anita Ratnasari. "Analisis Retinal Optical Coherence Tomography (OCT) Untuk Deteksi Kerusakan Retina Menggunakan Metode Machine Learning." JSAI (Journal Scientific and Applied Informatics) 7, no. 2 (2024): 247–52. http://dx.doi.org/10.36085/jsai.v7i2.6420.

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This study attempts to use support vector machine and otsu thresholding as proposed algorithm models to classify Retinal optical coherence tomography (OCT) images. In this study, there are two types implemented in classifying retinal image datasets. The first scenario is to classify using the support vector machine algorithm without the otsu thresholding method and the second scenario is to classify using the support vector machine algorithm with the otsu thresholding method with various parameter values. Based on the experimental results, classification of retina image datasets using the supp

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Altay Açar, S., and Ş. Bayır. "PRE-PROCESSES FOR URBAN AREAS DETECTION IN SAR IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W6 (November 13, 2017): 15–17. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w6-15-2017.

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In this study, pre-processes for urban areas detection in synthetic aperture radar (SAR) images are examined. These pre-processes are image smoothing, thresholding and white coloured regions determination. Image smoothing is carried out to remove noises then thresholding is applied to obtain binary image. Finally, candidate urban areas are detected by using white coloured regions determination. All pre-processes are applied by utilizing the developed software. Two different SAR images which are acquired by TerraSAR-X are used in experimental study. Obtained results are shown visually.

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