Nonlocal Means Two Dimensional Histogram-Based Image Segmentation via Minimizing Relative Entropy
Abstract
:1. Introduction
2. Non-Local Mean Two Dimensional Histogram (NLMTDH)
2.1. Non-Local Mean Filter
2.2. Construction of NLMTDH
3. Image Thresholding Based on NLMTDH Using Relative Entropy
3.1. Relative Entropy
3.2. Threshold Selection Based on NLMTDH Using Relative Entropy
4. Experimental Results and Discussion
4.1. Results
4.2. Discussion
5. Conclusions
Author Contributions
Acknowledgments
Conflicts of Interest
Appendix A. Derivation of Equation (10)
References
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Methods | Advantages | Disadvantages |
---|---|---|
superpixel [5,6] | reduce redundant information; less complexity | cannot locate the edges accurately |
watershed [7,8] | simple and intuition | usually result in over segmention |
active contour models [9,10] | rigorous mathematical base; | sensitive noise; high computation complexity |
clustering [11,12] | intensive value is enough; simple | the number of cluster cannot be determined automatically; spatial information is ignored; |
deep learning [13,14] | high segmentation accuracy | large computation burden |
thresholding [15,16] | simple, easy to be implemented | ignore spatial information |
Image | The Proposed | 2DMCE | MCE | OTSU | KAPUR | |
---|---|---|---|---|---|---|
ant | threshold | 52 52 | 44 45 | 69 | 84 | 183 |
ME | 0.0344 | 0.0379 | 0.0481 | 0.0829 | 0.8852 | |
bacteria | threshold | 65 65 | 44 45 | 98 | 99 | 70 |
ME | 0.0101 | 0.0605 | 0.4266 | 0.4398 | 0.0221 | |
block | threshold | 26 26 | 36 36 | 38 | 120 | 88 |
ME | 0.0183 | 0.0621 | 0.0679 | 0.2861 | 0.2613 | |
geometric | threshold | 36 36 | 36 36 | 41 | 70 | 126 |
ME | 0.0324 | 0.0347 | 0.0381 | 0.0986 | 0.2323 | |
junk | threshold | 187 186 | 209 206 | 129 | 134 | 158 |
ME | 0.0072 | 0.0090 | 0.0633 | 0.0492 | 0.0166 | |
mask | threshold | 23 24 | 28 30 | 30 | 57 | 116 |
ME | 0.0016 | 0.0129 | 0.0136 | 0.1134 | 0.2860 | |
casting13 | threshold | 144 144 | 134 127 | 74 | 80 | 114 |
ME | 0.0115 | 0.0128 | 0.1046 | 0.0795 | 0.0170 | |
casting18 | threshold | 154 153 | 158 154 | 92 | 138 | 114 |
ME | 0.0050 | 0.0063 | 0.2200 | 0.0074 | 0.0611 |
Image | The Propsed | 2DMCE | MCE | Otsu | Kapur |
---|---|---|---|---|---|
ant | 170.3123 | 21.1812 | 0.0317 | 0.0032 | 0.0067 |
bacteria | 163.7921 | 23.9648 | 0.0097 | 0.0031 | 0.0081 |
block | 94.4108 | 21.3898 | 0.0079 | 0.0027 | 0.0077 |
casting13 | 56.5179 | 9.2832 | 0.0066 | 0.0023 | 0.0043 |
casting14 | 56.6691 | 10.2639 | 0.0068 | 0.0027 | 0.0051 |
geometric | 62.0219 | 18.1187 | 0.0073 | 0.0020 | 0.0059 |
junk | 116.4295 | 14.0563 | 0.0061 | 0.0019 | 0.0045 |
mask | 99.1112 | 22.3298 | 0.0088 | 0.0023 | 0.0057 |
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Jiang, C.; Yang, W.; Guo, Y.; Wu, F.; Tang, Y. Nonlocal Means Two Dimensional Histogram-Based Image Segmentation via Minimizing Relative Entropy. Entropy 2018, 20, 827. https://doi.org/10.3390/e20110827
Jiang C, Yang W, Guo Y, Wu F, Tang Y. Nonlocal Means Two Dimensional Histogram-Based Image Segmentation via Minimizing Relative Entropy. Entropy. 2018; 20(11):827. https://doi.org/10.3390/e20110827
Chicago/Turabian StyleJiang, Chundi, Wei Yang, Yu Guo, Fei Wu, and Yinggan Tang. 2018. "Nonlocal Means Two Dimensional Histogram-Based Image Segmentation via Minimizing Relative Entropy" Entropy 20, no. 11: 827. https://doi.org/10.3390/e20110827