Applications of Neutrosophic Logic in Image Processing: A Survey

Ghanbari Talouki, A.; Koochari, A.; Edalatpanah, S. A.

doi:10.22061/jecei.2021.8069.474

تعداد نشریات	11
تعداد شماره‌ها	226
تعداد مقالات	2,285
تعداد مشاهده مقاله	3,519,815
تعداد دریافت فایل اصل مقاله	2,580,420

	Applications of Neutrosophic Logic in Image Processing: A Survey
Journal of Electrical and Computer Engineering Innovations (JECEI)
دوره 10، شماره 1، فروردین 2022، صفحه 243-258 اصل مقاله (1.51 M)
نوع مقاله: Review paper
شناسه دیجیتال (DOI): 10.22061/jecei.2021.8069.474
نویسندگان
A. Ghanbari Talouki¹؛ A. Koochari^* ¹؛ S. A. Edalatpanah²
¹Department of Mechanic, Electrical and Computer, Science and Research Branch, Islamic Azad University, Tehran, Iran
²Department of Applied Mathematics, Ayandegan Institute of Higher Education, Tonekabon, Iran
تاریخ دریافت: 22 خرداد 1400، تاریخ بازنگری: 20 شهریور 1400، تاریخ پذیرش: 27 مهر 1400
چکیده
Background and Objectives: Images and videos play significant roles in our lives. Moreover, there are considerable improvements in computer data collection systems; therefore, anyone is able to acquire much many images or videos, whereas he/she cannot process them manually. Images and videos became attractive since depicting and digital processing of these kinds of data became possible. Since indeterminacy surrounded the world, including images and videos of that; imprecision is needed to interpret this world. Methods: Neutosophic logic, which is from philosophy and is also included of logic, set theory and probability/statistics, is able to depict this imprecision. As a result, advanced image processing can be defined by translation of image processing into neutrosophic domain. In this paper, first of all, a general introduction about image/video processing (segmentation, noise reduction and image retrieval) and uncertainty is stated. Then, definitions of fuzzy sets, intuitionistic fuzzy sets and neutrosophic sets are expressed. In the following, applications of neutrosophic domain in image and video processing such as segmentation, noise reduction and image retrieval are introduced. Results: Although, neutrosophic is used for image restoration and segmentation; input images are usually medical images or gray level natural images. The remarkable point is that there are few researches that focuse on image restoration or segmentation using neutrosophic and consider color images. Therefore, color image restoration and segmentation in neutrosophic environment is novel to be done. Conclusion: Neutrosophic usage in image retrieval brings out an improvement in average recall and precision measure compared to earlier methods. Considering different textures and shapes can extend usages of neutrosophic space in image retrieval applications.
کلیدواژه‌ها
neutrosophhic؛ image processing؛ segmentation؛ noise reduction؛ image retrieval

مراجع
[1] Y.D. Zhang, M.A. Khan, Z. Zhu, S. Wang, “Pseudo zernike moment and deep stacked sparse autoencoder for COVID-19 diagnosis,” Tech. Science Press, 69(3): 3145–3162, 2021. [2] M.A. Khan, M. Sharif, T. Akram, R. Damaševičius, R. Maskeliūnas, “Skin lesion segmentation and multiclass classification using deep learning features and improved moth flame optimization,” Diagnostics, 11(5): 811, 2021. [3] M.A. Khan, T. Akram, M. Sharif, M. Alhaisoni, T. Saba, N. Nawaz, “A probabilistic segmentation and entropy-rank correlation-based feature selection approach for the recognition of fruit diseases,” EURASIP J. Image Video Process., 14: 1-28, 2021. [4] I.M. Nasir, M. Raza, J.H. Shah, M. Attique Khan, A. Rehman, "Human action recognition using machine learning in uncontrolled environment," in Proc. 1st Int. Conf. Artificial Intelligence and Data Analytics (CAIDA): 182-187, 2021. [5] Z. Rehman, M.A. Khan, F. Ahmed, R. Damaševičius, S. R. Naqvi, W. Nisar, K. Javed, “Recognizing apple leaf diseases using a novel parallel real-time processing framework based on mask RCNN and transfer learning: an application for smart agriculture,” IET Image Process, 15(10): 2157-2168, 2021. [6] M.A. Khan, M. Alhaisoni, A. Armghan, F. Alenezi, U. Tariq, Y. Nam, T. Akram, "Video analytics framework for human action recognition," Comput. Mater. Continua, 68(3): 3841–3859, 2021. [7] M.A. Khan, K. Muhammad, M. Sharif, T. Akram, V.H.C. Albuquerque, “Multi-class skin lesion detection and classification via teledermatology,” IEEE J. Biomed. Health. Inf., Epub ahead of print. PMID: 33750716, 2021. [8] Y. Guo, H.D. Cheng, "A new neutrosophic approach to image denoising," in Proc. New Mathematics and Natural Computation (NMNC), 5(3): 653-662, 2009. [9] D. Nilson, S. Cristian, "Semantic video segmentation by gated recurrent flow propagation," in Proc. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR): 6819-6828, 2018. [10] A.Garcia, S. Escolano, S. Oprea, V. Martinez, P. Gonzalez, J. Rodriguez, "A survey on deep learning techniques for image and video semantic segmentation," Appl. Soft Comput., 70: 41-65, 2018. [11] R. Yao, G. Lin, S. Xia, J. Zhao, Z. Yong, "Video object segmentation and tracking: a survey," ACM Trans. Intell. Syst. Technol., 11(4): 1-47, 2020. [12] H. Liang, N. Li, S. Zhao, "Salt and pepper noise removal method based on a detail-aware filter," Symmetry, 13(3): 515-538, 2021. [13] B. Fu, X. Zhao, S. Chuanming, L. Ximing, W. Xianghai, "A salt and pepper noise image denoising method based on the generative classification,” Multimedi. Tool. Appl., 78(9): 12043-12053, 2019. [14] A.A. Salama, M. Eisa, A. E. Fawzy, "A neutrosophic image retrieval classifier," Int. J. Comput. Appl., 170(9): 1-6, 2017. [15] L.A. Zadeh, "Fuzzy sets," Inf. Control, 8(3): 338-353, 1965. [16] F. Smarandache, "Neutrosophy: neutrosophic probability, set, and logic: analytic synthesis & synthetic analysis," American Research Press, 1998: 105. [17] R. Kumar, S.A. Edalatpanah, S. Jha, R. Singh, "A pythagorean fuzzy approach to the transportation problem," Complex Intell. Syst., 5: 255–263, 2019. [18] R. Kumar, S. A. Edalatpanah, S. Jha, S. Gayen, R. Singh, "Shortest path problems using fuzzy weighted arc length," Int. J. Innovative Technol. Explor. Eng. (IJITEE), 8(6): 724-731, 2019. [19] S.A. Edalatpanah, "A data envelopment analysis model with triangular intuitionistic fuzzy numbers,” Int. J. Data Envelopment Anal., 7(4): 47-58, 2019. [20] E.Afful-Dadzie, O. Z. Komínková, B.P.L. Antonio, "Comparative state-of-the-art survey of classical fuzzy set and intuitionistic fuzzy sets in multi-criteria decision making," Int. J. Fuzzy Syst., 19: 726–738, 2017. [21] A. Ishizaka, "Comparison of fuzzy logic, AHP, FAHP and hybrid fuzzy AHP for new supplier selection and its performance analysis," Int. J. Integrated Supply Manag., 9(½): 1-22, 2014. [22] X. Zhang, M. Jian, Y. Sun, H. Wang, C. Zhang, "Improving image segmentation based on patch-weighted distance and fuzzy clustering," Multimedi. Tool. Appl., 79: 633–657, 2020. [23] S. Naim, H. Hargas, "A type 2-hesitation fuzzy logic based multi-criteria group decision making system for intelligent shared environments," Soft Computing, 18(7): 1305–1319, 2014. [24] Y. Guoa, H.D. Cheng, "New neutrosophic approach to image segmentation," Pattern Recognit., 42(5): 587-595, 2009. [25] M. Eisa, "A new approach for enhancing image retrieval using neutrosophic sets," Int. J. Comput. Appl., 95(8): 12-20, 2014. [26] H.D. Cheng, Y. Guo, "A new neutrosophic approach to image thresholding," New Math. Nat. Comput. (NMNC), 4(3): 291–308, 2011. [27] S.A. Edalatpanah, "Neutrosophic perspective on DEA," J. Appl. Res. Ind. Eng., 5(4): 339–345, 2018. [28] S.A. Edalatpanah, F. Smarandache, "Data envelopment analysis for simplified neutrosophic sets," Neutrosophic Sets Syst., 29: 215-226, 2019. [29] S.A. Edalatpanah, "A nonlinear approach for neutrosophic linear programming,” J. Appl. Res. Ind. Eng., 6(4): 367–373, 2019. [30] S.A. Edalatpanah, "A direct model for triangular neutrosophic linear programming," Int. J. Neutrosophic Sci. (IJNS), 1(1): 14-23, 2019. [31] S.A. Edalatpanah, “Data envelopment analysis based on triangular neutrosophic numbers,” CAAI Trans. Intell. Technol., 5(2): 94-98, 2020. [32] Y. Yang, D. Yan, J. Zhao, “Optimal path selection approach for fuzzy reliable shortest path problem,” J. Intell. Fuzzy syst., 32(1): 197-205, 2017. [33] R.Kumar, S.A. Edalatpanah, H. Mohapatra, “Note on “Optimal path selection approach for fuzzy reliable shortest path problem”,” J. Intell. Fuzzy Syst., (Preprint): 1-4, 2020. [34] E. AboElHamd, H.M. Shamma, M. Saleh, I. El-Khodary, “Neutrosophic logic theory and applications,” Neutrosophic Sets Syst., 41: 30-51, 2021. [35] Y. Guo, A. Ashour, "Neutrosophic multiple deep convolutional neural network for skin dermoscopic image classification," Neutrosophic Set in Medical Image Analysis: 269-285, 2019. [36] L. R. Nair, K. Subramaniam, G.P. Venkatesan, "An effective image retrieval system using machine learning and fuzzy c-means clustering approach," Multimed. Tool. Applicat., 79(15): 10123-10140, 2020. [37] F. Özyurt, E. Sert, D. Avci, "An expert system for brain tumor detection: fuzzy c-means with super resolution and convolutional neural network with extreme learning machine," Med. Hypotheses, 134: 109433-109440, 2020. [38] G. Cai, Y. Guo, W. Chen, H. Zeng, Y. Zhou, Y. Lu, "Neutrosophic set-based deep learning in mammogram analysis," Neutrosophic Set Med. Image Anal., 2019: 287-310, 2019. [39] A. Garcia, S. Orts, S. Oprea, M. Villena, P. Martinez, J. Rodríguez, "A survey on deep learning techniques for image and video semantic segmentation," Appl. Soft Comput., 70: 41-65, 2018. [40] K. Maninis, S. Caelles, Y. Chen, J. Pont, L. Leal, D. Cremers, L. Van, "Video object segmentation without temporal information," IEEE Trans. Pattern Anal. Mach. Intell., 41(6): 1515-1530. 2018. [41] Z. Yang, Q. Wang, S. Bai, "Video segmentation by detection for the 2019 unsupervised DAVIS challenge," in Proc. The 2019 DAVIS Challenge on Video Object Segmentation (VOS) – CVPR 2019 Workshop, 2019. [42] A. Garcia, E. Sergio, O. Sergiu, V. Martinez, P. Gonzalez, J. Rodriguez, "A survey on deep learning techniques for image and video semantic segmentation," Appl. Soft Comput., 70: 41-65, 2018. [43] Y. Guo, H.D. Cheng, "New neutrosophic approach to image segmentation", Pattern Recognit., 42(5): 587-595, 2009. [44] E. Rashno, B. Minaei, Y. Guo, "An effective clustering method based on data indeterminacy in neutrosophic set domain," Eng. Appl. Artif. Intell., 89: 103411, 2020. [45] G. Xu, S. Wang, T. Yang, W. Jiang, "A neutrosophic approach based on TOPSIS method to image segmentation," Int. J. Comput. Commun. Control, 13(6): 1047-1061, 2018. [46] A.Rashno, D.D. Koozekanani, P.M. Drayna, B. Nazari, S. Sadri, H. Rabbani, K.K. Parhi, "Fully automated segmentation of fluid/cyst regions in optical coherence tomography images with diabetic macular edema using neutrosophic sets and graph algorithms," IEEE Trans. Biomed. Eng., 65(5): 989-1001, 2018. [47] A.I. Shahin, Y. Guo, A. Ashour, "Advanced neutrosophic sets in microscopic image analysis," Neutrosophic Set in Medical Image Analysis: 31-50, eBook ISBN: 9780128181492, Academic Press, 2019. [48] D. Koundal, S. Bhisham, "Advanced neutrosophic set-based ultrasound image analysis," Neutrosophic Set .Med. Image Anal., 2019: 51-73, 2019. [49] N. Nguyen, D. Nilanjan, A. Ashour, L. Son, "A survey of the State-of-arts on neutrosophic sets in biomedical diagnoses," Int. J. Mach. Learn. Cybern., 10: 1-13, 2019. [50] E. Sert, D. Avci, "Brain tumor segmentation using neutrosophic expert maximum fuzzy-sure entropy and other approaches," Biomed. Signal Process. Control, 47: 276-287, 2019. [51] G. Xu, S. Wang, T. Yang, W. Jiang, "A neutrosophic approach based on TOPSIS method to image segmentation," Int. J. Comput. Commun. Control, 13(6): 1047-1061, 2018. [52] A.S. Ashour, C. Du, Y. Guo, A.R. Hawas, Y. Lai, F. Smarandache, "A novel neutrosophic subsets definition for dermoscopic image segmentation," IEEE Access, 7: 151047-151053, 2019. [53] M. Habibi, A. Ayatollahi, N. Dallalazar, A. Kermani, "Lumen boundary detection using neutrosophic c-means in IVOCT images", in Proc. 2019 IEEE 5th International Conference on Knowledge-Based Engineering and Innovation (KBEI): 1-6, 2019. [54] N. Rahimizadeh, R.P. Hasanzadeh, M. Ghahramani, F. Janabi-Sharifi, "A neutrosophic based non-local means filter for despeckling of medical ultrasound images," in Proc. 9th International Conference on Computer and Knowledge Engineering (ICCKE): 249-254, 2019. [55] S. Song, Z. Jia, J. Yang, N.K. Kasabov, "A fast image segmentation algorithm based on saliency map and neutrosophic set theory," IEEE Photonics J., 12(5): 1-16, 2020. [56] S. Datta, N. Chaki, "Dental x-ray image segmentation using maker based watershed technique in neutrosophic domain," in Proc. 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA): 1-5, 2020. [57] F. Smarandache, M.A. Quiroz-Martínez, J.E. Ricardo, N.B. Hernández, M.Y.L. Vázquez, “Application of neutrosophic offsets for digital image processing,” Rev. Invest. Operacional, 41(5): 603-611, 2020. [58] J. Zhao, X. Wang, M. Li, “A novel Neutrosophic image segmentation based on improved fuzzy C-means algorithm (NIS-IFCM),” Int. J. Pattern Recognit Artif Intell., 34(05), 2055011, 2020. [59] A.S. Ashour, Y. Guo, “Optimization-based neutrosophic set in computer-aided diagnosis,” Optimization Theory Based on Neutrosophic and Plithogenic Sets: 405-421, Academic Press, 2020. [60] A.R. Hawas, Y. Guo, C. Du, K. Polat, A.S. Ashour, “OCE-NGC: A neutrosophic graph cut algorithm using optimized clustering estimation algorithm for dermoscopic skin lesion segmentation,” Appl. Soft Comput., 86: 105931, 2020. [61] P. Singh, “A neutrosophic-entropy based adaptive thresholding segmentation algorithm: A special application in MR images of Parkinson's disease,” Artif. Intell. Med. , 104: 101838, 2020. [62] S. Dhar, M.K. Kundu, H. Roy, “Nondestructive testing image segmentation based on neutrosophic set and bat algorithm,” in Proc. 2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN): 93-98, 2020. [63] A. Namburu, M.V. Cruz, H. Seetha, “Single valued triangular neutrosophic fuzzy c-means for Mr. Brain image segmentation,” Recent Advances in Computer Based Systems, Processes and Applications: 81-88, 2020. [64] M.A. Khan, S. Kadry, P. Parwekar, R. Damaševičius, A. Mehmood, J.A. Khan, S.R. Naqvi, “Human gait analysis for osteoarthritis prediction: a framework of deep learning and kernel extreme learning machine,” Complex Intell. Syst., 2021: 1-19, 2021. [65] M.A. Khan, Y. Zhang, M. Sharif, T. Akram, “Pixels to classes: intelligent learning framework for multiclass skin lesion localization and classification,” Comput. Electr. Eng., 90: 106956, 2021. [66] M.A. Khan, T. Akram, Y. Zhang, M. Sharif, “Attributes based skin lesion detection and recognition: a mask RCNN and transfer learning-based deep learning framework,” Pattern Recognit. Lett., 143: 58-66, 2021. [67] A. Sengur, U. Budak, Y. Akbulut, M. Karabatak, E. Tanyildizi, “A survey on neutrosophic medical image segmentation,” Neutrosophic Set in Medical Image Analysis, 2019: 145-165, 2019. [68] P. Arulpandy, M.T. Pricilla, “Salt and pepper noise reduction and edge detection algorithm based on neutrosophic logic,” Comput. Sci., 21(2): 1-17, 2020. [69] A. Salama, M. Eisa, H. Elghawalb, A. Fawzy, H. Ghawalby, "Neutrosophic features for image retrieval," Neutrosophic Sets Syst., 13: 56-61, 2016. [70] A. Rashno, S. Sadri, "Content-based image retrieval with color and texture features in neutrosophic domain," in Proc. 3rd Int. Conf. Pattern Recognition and Image Analysis (IPRIA): 50-55, 2017. [71] A. Rashno, F. Smarandache, S. Sadri, "Refined neutrosophic sets in content-based image retrieval application," in Proc. 10th Iranian Conference on Machine Vision and Image Processing (MVIP): 197- 202, 2017. [72] A. Salama, H. Ghawalby, A. Fawzy, M. Eisa, "A new approach in content-based image retrieval neutrosophic domain," Fuzzy Multi-criteria Decision-Making Using Neutrosophic Sets. Studies in Fuzziness and Soft Computing, vol. 369.edition: 369, chapter: 14, Springer, Cham, 2018. [73] X.Y. Wang, Y.J. Yu, H.Y. Yang, "An effective image retrieval scheme using color, texture and shape features," Comput. Stand. Interfaces, 33(1): 59–68, 2018. [74] A. Rashno, S. Sadri, "Content-based image retrieval with color and texture features in neutrosophic domain," in Proc. 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA): 50–55, 2017. [75] A. Rashno, S. Sadri, H. SadeghianNejad, "An efficient content-based image retrieval with ant colony optimization feature selection schema based on wavelet and color features," in Proc. International Symposium on Artificial Intelligence and Signal Processing (AISP): 59–64, 2015. [76] A. Rashno, F. Smarandache, S. Sadri, "Refined neutrosophic sets in content-based image retrieval application," in Proc. 10th Iranian Conference on Machine Vision and Image Processing (MVIP): 197-202, 2017. [77] A.A. Salama, M. Eisa, H. ElGhawalby, A. E. Fawzy, "Neutrosophic image retrieval with hesitancy degree," New Trends in Neutrosophic Theories and Applications, 2: 38-42, Infinite Study, 2018. [78] S.K. Sinha, C. Kumar, “Healthcare information retrieval based on neutrosophic logic,” in Proc. International Conference in Machine Intelligence and Signal Processing: 1085: 225, 2020. [79] M.A. Alaran, A. A. Agboola, A.T. Akinwale, O. Folorunso, “A new LCS-neutrosophic similarity measure for text information retrieval,” Neutrosophic Sets in Decision Analysis and Operations Research: 258-280, IGI Global, 2020. [80] O.G. El Barbary, “Document classification in information retrieval system based on neutrosophic sets,” Infinite Study, 2020.
آمار تعداد مشاهده مقاله: 1,054 تعداد دریافت فایل اصل مقاله: 1,208

سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب

پیوندهای مفید

آمار

Applications of Neutrosophic Logic in Image Processing: A Survey