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Статті в журналах з теми "FUZZY THRESHOLDING AND ANR"

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Автор: Grafiati

Опубліковано: 11 вересня 2023

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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 (July 20, 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 square error (RMSE) of the proposed soft thresholding is decreased to 87.3% of conventional hard thresholding, when the original image is "Lenna."

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2

Pal, Sankar K., and Ambarish Dasgupta. "Spectral fuzzy sets and soft thresholding." Information Sciences 65, no. 1-2 (November 1, 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 (December 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 (March 29, 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 (December 2005): 2363–72. http://dx.doi.org/10.1016/j.patcog.2005.02.014.

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6

Bogiatzis, Athanasios, and Basil Papadopoulos. "Global Image Thresholding Adaptive Neuro-Fuzzy Inference System Trained with Fuzzy Inclusion and Entropy Measures." Symmetry 11, no. 2 (February 22, 2019): 286. http://dx.doi.org/10.3390/sym11020286.

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Анотація:

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 which we efficiently managed to use on both global and local thresholding. The general method which we presented was an open and adaptable procedure, it was free of sensitivity or bias parameters and it involved image classification, mathematical functions, a fuzzy symmetrical triangular number and some criteria of choosing between two possible thresholds. Here, we continue this research and try to avoid all these by automatically connecting our measures with the wanted threshold using some Artificial Neural Network (ANN). Using an ANN in image segmentation is not uncommon especially in the domain of medical images. However, our proposition involves the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) which means that all we need is a proper database. It is a simple and immediate method which could provide researchers with an alternative approach to the thresholding problem considering that they probably have at their disposal some appropriate and specialized data.

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7

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 (March 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 process "clean" images, but also can process images with different kinds of noises and images with multiple kinds of noise well without knowing the type of the noise, which is the most difficult task for image thresholding. It will be useful for applications in computer vision and image processing.

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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 (April 1, 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 lungs clearly. For this purpose, the intensity value of a pixel is separated into bitplanes. In the second step, the triple signum function assigns an optimum threshold based on the grayscale values for the anatomical structure present in the medical images. Fuzzy rules are formed based on the available bitplanes to form the membership table and are stored in a knowledge base. Finally, rules are fired to assign final segmentation values through the inference process. The proposed new metrics are used to measure the accuracy of the segmentation method. From the analysis, it is observed that the proposed metrics are more suitable for the estimation of segmentation accuracy. The results obtained from this work show that the proposed method performs segmentation effectively for the different classes of medical images.

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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 (December 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 (January 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 (May 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 (February 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 (December 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 compared with those by the adaptive Wiener filter.

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19

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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20

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 (December 28, 2017): 627–31. http://dx.doi.org/10.1002/tee.22609.

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21

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

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22

Silva Ramos, Aline, Cristiano Hora Fontes, Adonias Magdiel Ferreira, Camila Costa Baccili, Karen Nascimento da Silva, Viviani Gomes, and Gabriel Jesus Alves de Melo. "Somatic cell count in buffalo milk using fuzzy clustering and image processing techniques." Journal of Dairy Research 88, no. 1 (February 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, the proposed method applies the Fuzzy C-Means (FCM) method as a preprocessing step in order to separate the images (objects) of the cells into clusters according to the color intensity. This contributes signficantly to the performance of the subsequent processing steps (thresholding, segmentation and recognition/identification). Two methods of thresholding were evaluated and the Watershed Transform was used for the identification and separation of nearby cells. A detailed statistical analysis of the results showed that the SCCFCI method is able to provide results which are consistent with those obtained by conventional counting. This method therefore represents a viable alternative for quality control in buffalo milk production.

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23

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 (March 12, 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 the sine cosine algorithm (SCA) to enhance the exploitation ability of the SSO. Moreover, Fuzzy entropy is applied as the main fitness function to evaluate the quality of each solution inside the population of the proposed SSOSCA since Fuzzy entropy has established its performance in literature. Several images from the well-known Berkeley dataset were used to test and evaluate the proposed method. The evaluation outcomes approved that SSOSCA showed better performance than several existing methods according to different image segmentation measures.

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24

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 (December 31, 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 image segmentation techniques are applied to detect the tumour in the scanned image. Different segmentation methods reviewed here are thresholding, kmeans, watershed, edge detection, morphological, fuzzy c-means. Here sample 5 MRI images are taken and processed by using MATLAB software. With the help of these techniques, area of the tumour, execution time, number pixel can be determined. Keywords: MATLAB, segmentation, thresholding , kmeans, watershed, edge detection, morphological, fuzzy c-means.

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25

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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26

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

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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 if one threshold is assumed, a segmented image of two groups is obtained. But on other side, several groups in the output image are generated with multilevel thresholds. The methods proposed by authors previously were traditional measures to identify objective functions. However, the basic challenge with thresholding methods is defining the threshold numbers that the individual must choose. In this paper, ASSA, along with type II fuzzy entropy, is proposed. The technique presented is examined in context with multilevel image thresholding, specifically with ASSA. For this reason, the proposed method is tested using various images simultaneously with histograms. For evaluating the performance efficiency of the proposed method, the results are compared, and robustness is tested with the efficiency of the proposed method to multilevel segmentation of image; numerous images are utilized arbitrarily from datasets.

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28

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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29

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 (July 14, 2016): 1120–39. http://dx.doi.org/10.1007/s11227-016-1815-7.

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30

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

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31

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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32

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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33

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 (August 2005): S620. http://dx.doi.org/10.1038/sj.jcbfm.9591524.0620.

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34

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 (March 30, 2011): 1865–68. http://dx.doi.org/10.3724/sp.j.1146.2007.00103.

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35

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 (March 1, 2021): 19715–35. http://dx.doi.org/10.1007/s11042-020-08760-6.

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36

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 (November 1, 2013): 713–24. http://dx.doi.org/10.1049/iet-ipr.2013.0098.

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37

Thakran, Snekha. "A hybrid GPFA-EEMD_Fuzzy threshold method for ECG signal de-noising." Journal of Intelligent & Fuzzy Systems 39, no. 5 (November 19, 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 into a true intrinsic mode function (IMFs) with the help of EEMD. EEMD is better than EMD because it removes the mode-mixing effect. In the second phase, IMFs which are corrupted by noise is obtained by using spectral flatness of each IMF and fuzzy thresholding. The corrupted IMFs are filtered using a GPF method to remove the noise. Then, the signal is reconstructed with the processed IMFs to get the de-noised ECG. The proposed algorithm is analyzed for a different local hospital database, and it gives better root mean square error and signal to noise ratio than other existing techniques (Wavelet transform (WT), EMD, Particle filter(PF) based method, extreme-point symmetric mode decomposition with Nonlocal Means(ESMD-NLM), and discrete wavelet with Savitzky-Golay(DW-SG) filter).

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38

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 (February 27, 2017): 1756007. http://dx.doi.org/10.1142/s0218001417560079.

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Анотація:

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 vein patterns. Also, Fuzzy Brain Storm Optimization (FBSO) method is proposed for score level fusion to achieve the better recognition performance. These two contributions are effectively included in the biometric recognition system and the performance analysis of the proposed method is carried out using the benchmark datasets of palm vein image, finger vein image, and hand vein image. The quantitative results are analyzed with the help of FAR, FRR, and accuracy. From outcome, we proved that the proposed FBSO approach attained a higher accuracy of 81.3% than the existing methods.

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39

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 (September 2010): 3188–92. http://dx.doi.org/10.1016/j.patcog.2010.04.005.

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40

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

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41

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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42

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 (April 15, 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 embedded into EFO to develop a new algorithm named CEFO. To evaluate the robustness of the proposed algorithm, other competitive algorithms such as Artificial Bee Colony (ABC), Bat Algorithm (BA), Wind Driven Optimization (WDO), and Bird Swarm Algorithm (BSA) are compared using fuzzy entropy as the fitness function. Furthermore, the proposed segmentation method is also compared with the most widely used approaches of Otsu’s variance and Kapur’s entropy to verify its segmentation accuracy and efficiency. Experiments are conducted on ten Berkeley benchmark images and the simulation results are presented in terms of peak signal to noise ratio (PSNR), mean structural similarity (MSSIM), feature similarity (FSIM), and computational time (CPU Time) at different threshold levels of 4, 6, 8, and 10 for each test image. A series of experiments can significantly demonstrate the superior performance of the proposed technique, which can deal with multilevel thresholding color image segmentation excellently.

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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 (August 8, 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 databases. According to the statistical study we performed, in the colonoscopy images the polyp contours usually belong to gradient domain of neither too large, nor too small gradients, though they can also have stronger or weaker segments. In order to prioritize the gradient domain typical for the polyps, a relative gradient-based thresholding as well as a gradient-weighted voting was introduced in this paper. For evaluating the improvement of the shape deviation tolerance of the classical and fuzzy Hough transforms, the maximum radial displacement and the average radius were used to characterize the roundness of the objects to be detected. The gradient thresholding proved to decrease the calculation time to less than 50% of the full Hough transforms, and the number of the resulting circles outside the polyp’s environment also decreased, especially for low resolution images.

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44

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 (November 1, 2014): 487–96. http://dx.doi.org/10.1049/iet-smt.2013.0124.

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45

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 (February 13, 2019): 593–619. http://dx.doi.org/10.1007/s12530-018-09262-5.

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46

Yuksel, M. E., and M. Borlu. "Accurate Segmentation of Dermoscopic Images by Image Thresholding Based on Type-2 Fuzzy Logic." IEEE Transactions on Fuzzy Systems 17, no. 4 (August 2009): 976–82. http://dx.doi.org/10.1109/tfuzz.2009.2018300.

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Jiang, Shengtao, Xuewen Mu, Huan Cheng, and Qiyue Song. "Image thresholding segmentation of generalized fuzzy entropy based on double adaptive ant colony algorithm." Journal of Intelligent & Fuzzy Systems 35, no. 2 (August 26, 2018): 1979–90. http://dx.doi.org/10.3233/jifs-171643.

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48

Abd Elaziz, Mohamed, Uddalok Sarkar, Sayan Nag, Salvador Hinojosa, and Diego Oliva. "Improving image thresholding by the type II fuzzy entropy and a hybrid optimization algorithm." Soft Computing 24, no. 19 (March 13, 2020): 14885–905. http://dx.doi.org/10.1007/s00500-020-04842-7.

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Tao, Wen-Bing, Jin-Wen Tian, and Jian Liu. "Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm." Pattern Recognition Letters 24, no. 16 (December 2003): 3069–78. http://dx.doi.org/10.1016/s0167-8655(03)00166-1.

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50

Zagrouba, Ezzeddine, and Walid Barhoumi. "SEMIAUTOMATIC DETECTION OF TUMORAL ZONE." Image Analysis & Stereology 21, no. 1 (May 3, 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 the class containing the sick zone is localised in return for the FCM algorithm. Finally, the superposition of the two partitions of the image will determine the sick zone. The originality of our approach is the parallel exploitation of different types of information in the image by the cooperation of two complementary approaches. This allows us to carry out a pertinent approach for the detection of sick zone in MRI images.

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