range | ||||||
Lena1 | 21.9715 | 22.1662 | 21.5974 | 21.9076 | 21.1507 | 21.6422 |
Lena2 | 22.8407 | 23.1224 | 22.5366 | 22.8503 | 22.3337 | 22.6582 |
Pepper | 20.9797 | 21.4961 | 20.4562 | 20.8171 | 20.1147 | 20.4185 |
As a generalization of the fuzzy set and intuitionistic fuzzy set, the neutrosophic set (NS) have been developed to represent uncertain, imprecise, incomplete and inconsistent information existing in the real world. Now the interval neutrosophic set (INS) which is an expansion of the neutrosophic set have been proposed exactly to address issues with a set of numbers in the real unit interval, not just one specific number. After definition of concepts and operations, INS is applied to image segmentation. Images are converted to the INS domain, which is described using three membership interval sets: T, I and F. Then, in order to increase the contrast between membership and evaluate the indeterminacy, a fuzzy intensification for each element in the interval set is made and a score function in the INS is defined. Finally, the proposed method is employed to perform image segmentation using the traditional k-means clustering. The experimental results on a variety of images demonstrate that the proposed approach can segment different sorts of images. Especially, it can segment "clean" images and images with various levels of noise.
Citation: |
Figure 6. For Lena image, the first part is the original image, the middle part is the segmentation by the approach in [9], and the third part is the segmentation by interval neutrosophic set method
Table 1.
The value of PSNR in different ranges for the three images (
range | ||||||
Lena1 | 21.9715 | 22.1662 | 21.5974 | 21.9076 | 21.1507 | 21.6422 |
Lena2 | 22.8407 | 23.1224 | 22.5366 | 22.8503 | 22.3337 | 22.6582 |
Pepper | 20.9797 | 21.4961 | 20.4562 | 20.8171 | 20.1147 | 20.4185 |
Table 2. The value of PSNR for the three images with different noises
noise | gaussian noise(1) | gaussian noise(2) | salt noise | speckle noise |
k-means | 19.1443 | 13.8363 | 22.6033 | 19.5381 |
INI | 21.2599 | 18.4293 | 21.1605 | 21.6448 |
k-means | 19.1969 | 13.9415 | 23.3081 | 20.9689 |
INI | 22.1729 | 18.6085 | 22.4192 | 22.6730 |
k-means | 18.6936 | 13.8596 | 21.4768 | 19.4912 |
INI | 20.6764 | 18.0361 | 20.7433 | 20.6611 |
[1] | M. R. Anderberg, Cluster analysis for applications, Probability & Mathematical Statistics New York Academic Press, 1 (1973), 347-353. |
[2] | K. T. Atanassov, Intuitionistic fuzzy sets, Physica-Verlag HD, 20 (1986), 87-96. doi: 10.1007/978-3-7908-1870-3. |
[3] | K. T. Atanassov and G. Gargov, Interval valued intuitionistic fuzzy sets, Physica-Verlag HD, (1999), 343–349. doi: 10.1016/0165-0114(89)90205-4. |
[4] | C. Bai, D. Dhavale and J. Sarkis, Complex investment decisions using rough set and fuzzy C-means: An example of investment in green supply chains, European Journal of Operational Research, 248 (2016), 507-521. doi: 10.1016/j.ejor.2015.07.059. |
[5] | H. D. Cheng, Y. H. Chen and X. H. Jiang, Thresholding using two-dimensionalh histogram and fuzzy entropy principle, IEEE Transactions on Image Processing, 9 (2000), 732-735. doi: 10.1109/83.841949. |
[6] | H. D. Cheng and Y. Guo, A new neutrosophic approach to image thresholding, New Mathematics & Natural Computation, 4 (2008), 291-308. |
[7] | C. Feng, D. Zhao and M. Huang, Image segmentation using CUDA accelerated non-local means denoising and bias correction embedded fuzzy C-means (BCEFCM), Signal Processing, 122 (2016), 164-189. |
[8] | Y. Guo, H. D. Cheng and W. Zhao et al., A novel image segmentation algorithm based on fuzzy C-means algorithm and neutrosophic set, JCIS-2008 Proceedings, 2008. |
[9] | Y. Guo and H. D. Cheng, New neutrosophic approach to image segmentation, Pattern Recognition, 42 (2009), 587-595. |
[10] | Y. Guo, A novel image edge detection algorithm based on neutrosophic set, Pergamon Press, Inc, 40 (2014), 3-25. |
[11] | Y. Guo and A. Sengür, A novel image segmentation algorithm based on neutrosophic similarity clustering, Applied Soft Computing, 25 (2014), 391-398. |
[12] | Y. Guo, A. Sengür and J. Ye, A novel image thresholding algorithm based on neutrosophic similarity score, Measurement, 58 (2014), 175-186. |
[13] | Y. Guo and A. Sengür, NECM: Neutrosophic evidential C-means clustering algorithm, Neural Computing & Applications, 26 (2014), 1-11. |
[14] | Y. Guo and A. Sengür, NCM: Neutrosophic C-means clustering algorithm, Pattern Recognition, 48 (2015), 2710-2724. |
[15] | Y. Guo and Y. Akbulut et al., An efficient image segmentation algorithm using neutrosophic graph cut, Symmetry, 9 (2017), 185. |
[16] | K. Hanbay and M. F. Talu, Segmentation of SAR images using improved artificial bee colony algorithm and neutrosophic set, Applied Soft Computing Journal, 21 (2014), 433-443. |
[17] | T. Kanungo, D. M. Mount and N. S. Netanyahu et al., An efficient k-means clustering algorithm: Analysis and implementation, IEEE Transactions on Pattern Analysis & Machine Intelligence, 24 (2002), 881-892. |
[18] | X. Liu, The fuzzy theory based on AFS algebras and AFS structure, Journal of Mathematical Analysis & Applications, 217 (1998), 459-478. doi: 10.1006/jmaa.1997.5718. |
[19] | S. K. Pal and R. A. King, Image enhancement using smoothing with fuzzy sets, IEEE Transactions on Systems Man & Cybernetics, 11 (1981), 494-501. |
[20] | S. K. Pal and R. A. King, Image enhancement using fuzzy sets, Electronics Letters, 16 (1980), 376-378. |
[21] | A. Sengur and Y. Guo, Color texture image segmentation based on neutrosophic set and wavelet transformation, Computer Vision & Image Understanding, 115 (2011), 1134-1144. |
[22] | J. Shan, H. D. Cheng and Y. Wang, A novel segmentation method for breast ultrasound images based on neutrosophic k-means clustering, Medical Physics, 39 (2012), 5669. |
[23] | F. Smarandache, A unifying field in logics: Neutrosophic logic, Multiple-Valued Logic, 8 (1999), 489-503. |
[24] | F. Smarandache, A unifying field in logics: Neutrsophic logic, neutrosophy, neutrosophic set, neutrosophic probability (Fourth edition), University of New Mexico, 332 (2002), 5-21. |
[25] | F. Smarandache, Neutrosophic Topologies, 1999. |
[26] | Turksen, Interval valued fuzzy sets based on normal forms, Fuzzy Sets & Systems, 20 (1986), 191-210. doi: 10.1016/0165-0114(86)90077-1. |
[27] | J. K. Udupa and S. Samarasekera, Fuzzy connectedness and object definition: Theory, algorithms, and applications in image segmentation, Academic Press Inc., 58 (1996), 246-261. |
[28] | H. Wang, P. Madiraju and Y. Zhang et al, Interval neutrosophic sets, Mathematics, 1 (2004), 274-277. |
[29] | L. A. Zadeh, Fuzzy sets, Information and Control, 8 (1965), 338-353. |
[30] | H. Zhang, J. Wang and X. Chen, Interval neutrosophic sets and their application in multicriteria decision making problems, The Scientific World Journal, 2014 (2014), 645953. |
[31] | X. Zhao and S. T. Wang, Neutrosophic image segmentation approach based on similarity, Application Research of Computers, 29 (2012), 2371-2374. |