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Journal articles on the topic 'FUZZY THRESHOLDING AND ANR'

Author: Grafiati

Published: 11 September 2023

Last updated: 31 July 2025

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1

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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Pal, Sankar K., and Ambarish Dasgupta. "Spectral fuzzy sets and soft thresholding." Information Sciences 65, no. 1-2 (1992): 65–97. http://dx.doi.org/10.1016/0020-0255(92)90078-m.

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3

Bhandari, Dinabandhu, Nikhil R. Pal, and D. Dutta Majumder. "Fuzzy divergence, probability measure of fuzzy events and image thresholding." Pattern Recognition Letters 13, no. 12 (1992): 857–67. http://dx.doi.org/10.1016/0167-8655(92)90085-e.

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4

Sowjanya, Kotte, Munazzar Ajreen, Paka Sidharth, Kakara Sriharsha, and Lade Aishwarya Rao. "Fuzzy thresholding technique for multiregion picture division." International Research Journal on Advanced Science Hub 4, no. 03 (2022): 45–50. http://dx.doi.org/10.47392/irjash.2022.011.

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5

Tizhoosh, Hamid R. "Image thresholding using type II fuzzy sets." Pattern Recognition 38, no. 12 (2005): 2363–72. http://dx.doi.org/10.1016/j.patcog.2005.02.014.

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6

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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Bogiatzis, Athanasios, and Basil Papadopoulos. "Global Image Thresholding Adaptive Neuro-Fuzzy Inference System Trained with Fuzzy Inclusion and Entropy Measures." Symmetry 11, no. 2 (2019): 286. http://dx.doi.org/10.3390/sym11020286.

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Thresholding algorithms segment an image into two parts (foreground and background) by producing a binary version of our initial input. It is a complex procedure (due to the distinctive characteristics of each image) which often constitutes the initial step of other image processing or computer vision applications. Global techniques calculate a single threshold for the whole image while local techniques calculate a different threshold for each pixel based on specific attributes of its local area. In some of our previous work, we introduced some specific fuzzy inclusion and entropy measures whi

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8

Shark, L. K., and C. Yu. "Denoising by optimal fuzzy thresholding in wavelet domain." Electronics Letters 36, no. 6 (2000): 581. http://dx.doi.org/10.1049/el:20000451.

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9

Li, Linguo, Xuwen Huang, Shunqiang Qian, Zhangfei Li, Shujing Li, and Romany F. Mansour. "Fuzzy Hybrid Coyote Optimization Algorithm for Image Thresholding." Computers, Materials & Continua 72, no. 2 (2022): 3073–90. http://dx.doi.org/10.32604/cmc.2022.026625.

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10

Barrenechea, E., H. Bustince, M. J. Campión, E. Induráin, and V. Knoblauch. "Topological interpretations of fuzzy subsets. A unified approach for fuzzy thresholding algorithms." Knowledge-Based Systems 54 (December 2013): 163–71. http://dx.doi.org/10.1016/j.knosys.2013.09.008.

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11

Chi, Z., and H. Yan. "Map image segmentation based on thresholding and fuzzy rules." Electronics Letters 29, no. 21 (1993): 1841. http://dx.doi.org/10.1049/el:19931225.

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12

Khan, Z. Faizal, and A. Kannan. "Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding." Measurement Science Review 14, no. 2 (2014): 94–101. http://dx.doi.org/10.2478/msr-2014-0013.

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Abstract The performance of assessment in medical image segmentation is highly correlated with the extraction of anatomic structures from them, and the major task is how to separate the regions of interests from the background and soft tissues successfully. This paper proposes a fuzzy logic based bitplane method to automatically segment the background of images and to locate the region of interest of medical images. This segmentation algorithm consists of three steps, namely identification, rule firing, and inference. In the first step, we begin by identifying the bitplanes that represent the

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13

Martino, Ferdinando Di, and Salvatore Sessa. "PSO image thresholding on images compressed via fuzzy transforms." Information Sciences 506 (January 2020): 308–24. http://dx.doi.org/10.1016/j.ins.2019.07.088.

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14

Liu, Dong, Zhaohui Jiang, and Huanqing Feng. "A novel fuzzy classification entropy approach to image thresholding." Pattern Recognition Letters 27, no. 16 (2006): 1968–75. http://dx.doi.org/10.1016/j.patrec.2006.05.006.

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15

Gallo, Giovanni, and Salvatore Spinello. "Thresholding and fast iso-contour extraction with fuzzy arithmetic." Pattern Recognition Letters 21, no. 1 (2000): 31–44. http://dx.doi.org/10.1016/s0167-8655(99)00131-2.

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16

Vlachos, Ioannis K., and George D. Sergiadis. "Comment on: “Image thresholding using type II fuzzy sets”." Pattern Recognition 41, no. 5 (2008): 1810–11. http://dx.doi.org/10.1016/j.patcog.2007.11.001.

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17

Pal, Sankar K., and Azriel Rosenfeld. "Image enhancement and thresholding by optimization of fuzzy compactness." Pattern Recognition Letters 7, no. 2 (1988): 77–86. http://dx.doi.org/10.1016/0167-8655(88)90122-5.

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18

PHAM, TUAN D., and MICHAEL WAGNER. "IMAGE ENHANCEMENT BY KRIGING AND FUZZY SETS." International Journal of Pattern Recognition and Artificial Intelligence 14, no. 08 (2000): 1025–38. http://dx.doi.org/10.1142/s0218001400000659.

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A kriging method is presented as a spatial filter for smoothing gray-scale images degraded by Gaussian white noise. The concepts are based on the analysis of semivariances, the linear combination scheme of kriging, and fuzzy sets. Application of fuzzy sets allows a gradual transition between two boundaries of semivariance levels as a criterion for smoothing the pixel values. This fuzzy thresholding also allows some degree of flexibility to suit various desired results for particular problems. Experimental results obtained by the fuzzy kriging filter are smoother and still preserve edges compar

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19

Silva Ramos, Aline, Cristiano Hora Fontes, Adonias Magdiel Ferreira, et al. "Somatic cell count in buffalo milk using fuzzy clustering and image processing techniques." Journal of Dairy Research 88, no. 1 (2021): 69–72. http://dx.doi.org/10.1017/s0022029921000042.

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AbstractThis research communication presents an automatic method for the counting of somatic cells in buffalo milk, which includes the application of a fuzzy clustering method and image processing techniques (somatic cell count with fuzzy clustering and image processing|, SCCFCI). Somatic cell count (SCC) in milk is the main biomarker for assessing milk quality and it is traditionally performed by exhaustive methods consisting of the visual observation of cells in milk smears through a microscope, which generates uncertainties associated with human interpretation. Unlike other similar works, t

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LEI Bo, 雷博, and 范九伦 FAN Jiu-lun. "Image Thresholding Method Based on Two-dimensional Generalized Fuzzy Entropy." ACTA PHOTONICA SINICA 39, no. 10 (2010): 1907–14. http://dx.doi.org/10.3788/gzxb20103910.1907.

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21

Zheng, Xiulian, Yinggan Tang, and Wenzhao Hu. "Image thresholding based on gray level-fuzzy local entropy histogram." IEEJ Transactions on Electrical and Electronic Engineering 13, no. 4 (2017): 627–31. http://dx.doi.org/10.1002/tee.22609.

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22

Sen, D., and S. K. Pal. "Histogram Thresholding Using Fuzzy and Rough Measures of Association Error." IEEE Transactions on Image Processing 18, no. 4 (2009): 879–88. http://dx.doi.org/10.1109/tip.2009.2012890.

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23

Khalid, El Fahssi, Ounasser Saida, Mohamed Taj Bennani, and Abenaou Abdenbi. "Development of a Medical Image Segmentation Algorithm based on Fuzzy C-Means Clustering." WSEAS TRANSACTIONS ON SIGNAL PROCESSING 19 (December 31, 2023): 215–20. http://dx.doi.org/10.37394/232014.2023.19.23.

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Breast mass segmentation in mammography plays a very important role in computer-aided diagnosis (CAD) systems. In this article, we propose a mammography image segmentation method based on a combined approach. The fuzzy clustering method and thresholding segmentation. Subsequently, we use the wavelet transform and the Canny filter for edge detection.

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24

Naji Alwerfali, Husein S., Mohammed A. A. Al-qaness, Mohamed Abd Elaziz, Ahmed A. Ewees, Diego Oliva, and Songfeng Lu. "Multi-Level Image Thresholding Based on Modified Spherical Search Optimizer and Fuzzy Entropy." Entropy 22, no. 3 (2020): 328. http://dx.doi.org/10.3390/e22030328.

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Multi-level thresholding is one of the effective segmentation methods that have been applied in many applications. Traditional methods face challenges in determining the suitable threshold values; therefore, metaheuristic (MH) methods have been adopted to solve these challenges. In general, MH methods had been proposed by simulating natural behaviors of swarm ecosystems, such as birds, animals, and others. The current study proposes an alternative multi-level thresholding method based on a new MH method, a modified spherical search optimizer (SSO). This was performed by using the operators of

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25

Dounis, Anastasios, Andreas-Nestor Avramopoulos, and Maria Kallergi. "Hybrid Intelligent Pattern Recognition Systems for Mass Segmentation and Classification: A Pilot Study on Full-Field Digital Mammograms." Applied Sciences 13, no. 18 (2023): 10401. http://dx.doi.org/10.3390/app131810401.

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Governments and health authorities emphasize the importance of early detection of breast cancer, usually through mammography, to improve prognosis, increase therapeutic options and achieve optimum outcomes. Despite technological advances and the advent of full-field digital mammography (FFDM), diagnosis of breast abnormalities on mammographic images remains a challenge due to qualitative variations in different tissue types and densities. Highly accurate computer-aided diagnosis (CADx) systems could assist in the differentiation between normal and abnormal tissue and the classification of abno

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26

Kulkarni, Miss Kashmira A. "Brain Tumour Detection Using Image Segmentation: A Review." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (2021): 208–18. http://dx.doi.org/10.22214/ijraset.2021.39184.

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Abstract: Medical Image Processing is one of the most challenging and emerging fields. MRI, CT scan , ultra scan, X-rays etc. are different machines to diagnose the condition of the patient. Human body is made up of several types of cells. Brain is a highly specialized and sensitive organ of human body. Brain tumour is one of the severe problems in the medical science. MRI imaging is often used when treating brain tumour. There are various image segmentation algorithms in order to detect brain tumour using image processing. Firstly quality of scanned MRI image is enhanced and then different im

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27

Mahajan, Shubham, Nitin Mittal, Rohit Salgotra, Mehedi Masud, Hesham A. Alhumyani, and Amit Kant Pandit. "An Efficient Adaptive Salp Swarm Algorithm Using Type II Fuzzy Entropy for Multilevel Thresholding Image Segmentation." Computational and Mathematical Methods in Medicine 2022 (January 29, 2022): 1–14. http://dx.doi.org/10.1155/2022/2794326.

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Salp swarm algorithm (SSA) is an innovative contribution to smart swarm algorithms and has shown its utility in a wide range of research domains. While it is an efficient algorithm, it is noted that SSA suffers from several issues, including weak exploitation, convergence, and unstable exploitation and exploration. To overcome these, an improved SSA called as adaptive salp swarm algorithm (ASSA) was proposed. Thresholding is among the most effective image segmentation methods in which the objective function is described in relation of threshold values and their position in the histogram. Only

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28

Raj, Aditya, Gunjan Gautam, Siti Norul Huda Sheikh Abdullah, Abbas Salimi Zaini, and Susanta Mukhopadhyay. "Multi-level thresholding based on differential evolution and Tsallis Fuzzy entropy." Image and Vision Computing 91 (November 2019): 103792. http://dx.doi.org/10.1016/j.imavis.2019.07.004.

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29

Henila, Manickam, and Palaniappan Chithra. "Segmentation using fuzzy cluster‐based thresholding method for apple fruit sorting." IET Image Processing 14, no. 16 (2020): 4178–87. http://dx.doi.org/10.1049/iet-ipr.2020.0705.

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30

Zainal Arifin, Agus, Aidila Fitri Heddyanna, and Hudan Studiawan. "Ultrafuzziness Optimization Based on Type II Fuzzy Sets for Image Thresholding." ITB Journal of Information and Communication Technology 4, no. 2 (2010): 79–94. http://dx.doi.org/10.5614/itbj.ict.2010.4.2.2.

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31

Paul, Nihal, Ashish Singh, Abhishek Midya, Partha Pratim Roy, and Debi Prosad Dogra. "Moving object detection using modified temporal differencing and local fuzzy thresholding." Journal of Supercomputing 73, no. 3 (2016): 1120–39. http://dx.doi.org/10.1007/s11227-016-1815-7.

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32

Murthy, Chivukula A., and Sankar K. Pal. "Fuzzy thresholding: mathematical framework, bound functions and weighted moving average technique." Pattern Recognition Letters 11, no. 3 (1990): 197–206. http://dx.doi.org/10.1016/0167-8655(90)90006-n.

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33

Sousa, W. P., C. C. P. Cruz, and R. S. Lanzillotti. "Fuzzy Divergence for Lung Radiography Image Enhancement." Trends in Computational and Applied Mathematics 24, no. 4 (2023): 699–716. http://dx.doi.org/10.5540/tcam.2023.024.04.00699.

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Segmentation is one of the inferential applications for detecting patterns indigital images, which has been widely used in the health area. Thresholding, a type of segmentation, consists of separating the gray groups of an image, through one or more thresholds applied to the histogram. Thus, we used the gray tone with the lowest Fuzzy Divergence found to apply the enhancement method, through membership values. This paper presents a method to assist physicians in interpreting lung radiography images, especially in the pandemic caused by COVID-19, when enhancing lung images. In addition, we cons

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34

Sran, Paramveer Kaur, Savita Gupta, and Sukhwinder Singh. "Integrating saliency with fuzzy thresholding for brain tumor extraction in MR images." Journal of Visual Communication and Image Representation 74 (January 2021): 102964. http://dx.doi.org/10.1016/j.jvcir.2020.102964.

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35

Peng, Hong, Jun Wang, Mario J. Pérez-Jiménez, and Peng Shi. "A novel image thresholding method based on membrane computing and fuzzy entropy." Journal of Intelligent & Fuzzy Systems 24, no. 2 (2013): 229–37. http://dx.doi.org/10.3233/ifs-2012-0549.

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36

Kim, Jinman, Weidong Cai, Dagan Feng, and Stefan Eberl. "Interactive fuzzy temporal thresholding for the segmentation of dynamic brain PET images." Journal of Cerebral Blood Flow & Metabolism 25, no. 1_suppl (2005): S620. http://dx.doi.org/10.1038/sj.jcbfm.9591524.0620.

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37

Supriyanti, Retno, Syadzwina Luke Dzihniza, Muhammad Alqaaf, Muhammad Rifqi Kurniawan, Yogi Ramadhani, and Haris Budi Widodo. "Morphological features of lung white spots based on the Otsu and Phansalkar thresholding method." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 1 (2024): 530. http://dx.doi.org/10.11591/ijeecs.v33.i1.pp530-539.

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<span>COVID-19 is a disease that causes respiratory system disorders, so various tests are needed. One of them uses a chest X-ray or thorax. A chest X-ray will depict the lungs as a whole so that patches like white shadows will be visible. In this study, the number of lung areas and white spots can be observed and detected using segmentation techniques in image processing. But before entering the segmentation stage, the image will go through the preprocessing stage using the tri-threshold fuzzy intensification operators (fuzzy IO) method. It then segmented the lungs using the Otsu method

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38

Thakran, Snekha. "A hybrid GPFA-EEMD_Fuzzy threshold method for ECG signal de-noising." Journal of Intelligent & Fuzzy Systems 39, no. 5 (2020): 6773–82. http://dx.doi.org/10.3233/jifs-191518.

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The Electrocardiogram (ECG) signal records the electrical activity of the heart. It is very difficult for physicians to analyze the ECG signal if noise is embedded during acquisition to inspect the heart’s condition. The denoising of electrocardiogram signals based on the genetic particle filter algorithm(GPFA) using fuzzy thresholding and ensemble empirical mode decomposition (EEMD) is proposed in this paper, which efficiently removes noise from the ECG signal. This paper proposes a two-phase scheme for eliminating noise from the ECG signal. In the first phase, the noisy signal is decomposed

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39

Fan, Jiu-lun, and Feng Zhao. "A Generalized Fuzzy Entropy Thresholding Segmentation Method Based on the Sugeno Complement Operator." Journal of Electronics & Information Technology 30, no. 8 (2011): 1865–68. http://dx.doi.org/10.3724/sp.j.1146.2007.00103.

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40

Kumar, G. Anand, and P. V. Sridevi. "E-fuzzy feature fusion and thresholding for morphology segmentation of brain MRI modalities." Multimedia Tools and Applications 80, no. 13 (2021): 19715–35. http://dx.doi.org/10.1007/s11042-020-08760-6.

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41

Barhoumi, Walid, Mohamed Chafik Bakkay, and Ezzeddine Zargouba. "Automated photo-consistency test for voxel colouring based on fuzzy adaptive hysteresis thresholding." IET Image Processing 7, no. 8 (2013): 713–24. http://dx.doi.org/10.1049/iet-ipr.2013.0098.

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42

Verma, Dipti, and Sipi Dubey. "Fuzzy Brain Storm Optimization and Adaptive Thresholding for Multimodal Vein-Based Recognition System." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 05 (2017): 1756007. http://dx.doi.org/10.1142/s0218001417560079.

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Nowadays, conventional security method of using passwords can be easily forged by unauthorized person. Hence, biometric cues such as fingerprints, voice, palm print, and face are more preferable for recognition but to preserve the liveliness, another one important biometric trait is vein pattern, which is formed by the subcutaneous blood vessels that contain all the achievable recognition properties. Accordingly, in this paper, we propose a multibiometric system using palm vein, hand vein, and finger vein. Here, Holoentropy-based thresholding mechanism is newly developed for extracting the vei

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43

Bustince, H., E. Barrenechea, M. Pagola, J. Fernandez, and J. Sanz. "Comment on: “Image thresholding using type II fuzzy sets”. Importance of this method." Pattern Recognition 43, no. 9 (2010): 3188–92. http://dx.doi.org/10.1016/j.patcog.2010.04.005.

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44

Benabdelkader, Souad, and Mohammed Boulemden. "Recursive algorithm based on fuzzy 2-partition entropy for 2-level image thresholding." Pattern Recognition 38, no. 8 (2005): 1289–94. http://dx.doi.org/10.1016/j.patcog.2004.03.018.

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45

Wahid, Farha Fatina, Raju G, Shijo M. Joseph, Debabrata Swain, Om Prakash Das, and Biswaranjan Acharya. "A Novel Fuzzy-Based Thresholding Approach for Blood Vessel Segmentation from Fundus Image." Journal of Advances in Information Technology 14, no. 2 (2023): 185–92. http://dx.doi.org/10.12720/jait.14.2.185-192.

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46

Song, Suhang, Heming Jia, and Jun Ma. "A Chaotic Electromagnetic Field Optimization Algorithm Based on Fuzzy Entropy for Multilevel Thresholding Color Image Segmentation." Entropy 21, no. 4 (2019): 398. http://dx.doi.org/10.3390/e21040398.

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Multilevel thresholding segmentation of color images is an important technology in various applications which has received more attention in recent years. The process of determining the optimal threshold values in the case of traditional methods is time-consuming. In order to mitigate the above problem, meta-heuristic algorithms have been employed in this field for searching the optima during the past few years. In this paper, an effective technique of Electromagnetic Field Optimization (EFO) algorithm based on a fuzzy entropy criterion is proposed, and in addition, a novel chaotic strategy is

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Ismail, Raneem, and Szilvia Nagy. "A Novel Gradient-Weighted Voting Approach for Classical and Fuzzy Circular Hough Transforms and Their Application in Medical Image Analysis—Case Study: Colonoscopy." Applied Sciences 13, no. 16 (2023): 9066. http://dx.doi.org/10.3390/app13169066.

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Classical circular Hough transform was proven to be effective for some types of colorectal polyps. However, the polyps are very rarely perfectly circular, so some tolerance is needed, that can be ensured by applying fuzzy Hough transform instead of the classical one. In addition, the edge detection method, which is used as a preprocessing step of the Hough transforms, was changed from the generally used Canny method to Prewitt that detects fewer edge points outside of the polyp contours and also a smaller number of points to be transformed based on statistical data from three colonoscopy datab

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Yan, Hua, Ying Gang Zhou, and Yi Fan Wang. "Three-dimensional electrical capacitance tomography reconstruction by the Landweber iterative algorithm with fuzzy thresholding." IET Science, Measurement & Technology 8, no. 6 (2014): 487–96. http://dx.doi.org/10.1049/iet-smt.2013.0124.

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49

Bogiatzis, Athanasios C., and Basil K. Papadopoulos. "Local thresholding of degraded or unevenly illuminated documents using fuzzy inclusion and entropy measures." Evolving Systems 10, no. 4 (2019): 593–619. http://dx.doi.org/10.1007/s12530-018-09262-5.

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50

Zagrouba, Ezzeddine, and Walid Barhoumi. "SEMIAUTOMATIC DETECTION OF TUMORAL ZONE." Image Analysis & Stereology 21, no. 1 (2011): 13. http://dx.doi.org/10.5566/ias.v21.p13-18.

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This paper describes a robust method based on the cooperation of fuzzy classification and regions segmentation algorithms, in order to detect the tumoral zone in the brain Magnetic Resonance Imaging (MRI). On one hand, the classification in fuzzy sets is done by the Fuzzy C-Means algorithm (FCM), where a study of its different parameters and its complexity has been previously realised, which led us to improve it. On the other hand, the segmentation in regions is obtained by an hierarchical method through adaptive thresholding. Then, an operator expert selects a germ in the tumoral zone, and th

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