A Fusion Method for Atomic Force Acoustic Microscopy Cell Imaging Based on Local Variance in Non-Subsampled Shearlet Transform Domain
Abstract
:Featured Application
Abstract
1. Introduction
2. Materials and Methods
2.1. Non-Subsampled Shearlet Transform (NSST)
2.2. Contrast Limited Adaptive Histogram Equalization (CLAHE)
2.3. The Framework of Proposed Fusion Method
2.3.1. Decomposition
2.3.2. Low-Frequency Sub-Band Fusion
2.3.3. High-Frequency Sub-Bands Fusion
2.3.4. Image Quality Metrics
- Initialize ,,;
- Repeat
- /* get the histogram of the part greater than the threshold*/
- /* use otsu algorithm to get the new threshold */
- Until
- ROI=dilate (ROI).
- Step 1: Decompose the morphology and phase images using 3 level NSST to obtain their low-frequency sub-bands {, } and a series of high-frequency sub-bands {} at each K-scale and J-direction.
- Step 2: Use CLAHE for the source images, and use Equations (8) and (9) to calculate and . Get the weight map using Equation (10), and fuse the low-frequency sub-bands using Equation (11) to obtain . The weight coefficient in our experiment is set as and the size of the region to calculate the variance is .
- Step 3: Equation (12) is utilized to deal with the high-frequency sub-bands.
- Step 4: Perform the inverse NSST of the low-frequency and the high-frequency sub-bands to obtain the fused image.
- Step 5: Segment the image ROI and evaluate the results.
3. Results and Discussion
3.1. Quality Evaluation
3.1.1. MI
3.1.2.
3.1.3.
3.1.4. VIFF
3.2. Experimental Results
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
- Li, X.; Lu, A.; Deng, W.; Su, L.; Wang, J.; Ding, M. Noninvasive Subcellular Imaging Using Atomic Force Acoustic Microscopy (AFAM). Cells 2019, 8, 314. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wang, M.; Zhou, S.; Yang, Z.; Liu, Z.; Ren, S. Image fusion based on wavelet transform and gray-level features. J. Mod. Opt. 2019, 66, 77–86. [Google Scholar] [CrossRef]
- Li, Z.Y.; Zhou, J.M.; Wang, Y. Visibility-enhanced dual-band infrared image fusion based on nonsubsampled contourlet transform. AOPC 2017 Opt. Sens. Imaging Technol. Appl. 2017, 10462, 104621F. [Google Scholar]
- Agrawal, D.; Karar, V. Generation of enhanced information image using curvelet-transform-based image fusion for improving situation awareness of observer during surveillance. Int. J. Image Data Fusion 2019, 10, 45–57. [Google Scholar] [CrossRef]
- Kutyniok, G.; Labate, D. Resolution of the wavefront set using continuous shearlets. Trans. Am. Math. Soc. 2009, 361, 2719–2754. [Google Scholar] [CrossRef] [Green Version]
- Anandhi, D.; Valli, S. An algorithm for multi-sensor image fusion using maximum a posteriori and nonsubsampled contourlet transform. Comput. Electr. Eng. 2018, 65, 139–152. [Google Scholar] [CrossRef]
- He, K.; Zhou, D.; Zhang, X.; Nie, R.; Wang, Q.; Jin, X. Infrared and visible image fusion based on target extraction in the nonsubsampled contourlet transform domain. J. Appl. Remote Sens. 2017, 11, 015011. [Google Scholar] [CrossRef]
- Easley, G.; Labate, D.; Lim, W. Sparse directional image representations using the discrete shearlet transform. Appl. Comput. Harmon. Anal. 2008, 25, 25–46. [Google Scholar] [CrossRef] [Green Version]
- Lim, W.Q. The Discrete Shearlet Transform: A New Directional Transform and Compactly Supported Shearlet Frames. IEEE Trans. Image Process. 2010, 19, 1166–1180. [Google Scholar]
- Wu, W.; Qiu, Z.; Zhao, M.; Huang, Q.; Lei, Y. Visible and infrared image fusion using NSST and deep Boltzmann machine. Optik Int. J. Light Electron. Opt. 2018, 157, 334–342. [Google Scholar] [CrossRef]
- Luping, X.; Guorong, G.; Dongzhu, F. Multi-focus image fusion based on non-subsampled shearlet transform. IET Image Process. 2013, 7, 633–639. [Google Scholar]
- Vishwakarma, A.; Bhuyan, M.K. Image Fusion Using Adjustable Non-subsampled Shearlet Transfor. IEEE Trans. Instrum. Meas. 2018, 68, 3367–3378. [Google Scholar] [CrossRef]
- Deng, C.; Wang, Z.; Li, X.; Li, H.N.; Cavalcante, C.C. An Improved Remote Sensing Image Fusion Algorithm Based on IHS Transformation. KSII Trans. Internet Inf. Syst. 2017, 11. [Google Scholar] [CrossRef]
- Jin, Z.; Min, L.; Ng, M.K.; Zheng, M. Image colorization by fusion of color transfers based on DFT and variance features. Comput. Math. Appl. 2019, 77, 2553–2567. [Google Scholar] [CrossRef]
- Chang, Y.; Jung, C.; Ke, P.; Song, H.; Hwang, J. Automatic contrast-limited adaptive histogram equalization with dual gamma correction. IEEE Access 2018, 6, 11782–11792. [Google Scholar] [CrossRef]
- Otsu, N. A Threshold Selection Method from Gray-Level Histograms. IEEE Trans. Syst. Man Cybern. 1979, 9, 62–66. [Google Scholar] [CrossRef] [Green Version]
- Burt, P.J.; Adelson, E.H. The Laplacian pyramid as a compact image code. IEEE Trans. Commun. 1983, 31, 532–540. [Google Scholar] [CrossRef]
- Nencini, F.; Garzelli, A.; Baronti, S.; Alparone, L. Sensing image fusion using the curvelet transform. Inf. Fusion 2007, 8, 143–156. [Google Scholar] [CrossRef]
- Hou, R.; Zhou, D.; Nie, R.; Liu, D.; Ruan, X. Brain CT and MRI medical image fusion using convolutional neural networks and a dual-channel spiking cortical model. Med. Biol. Eng. Comput. 2018, 57, 887–900. [Google Scholar] [CrossRef]
- Ma, J.; Chen, C.; Li, C.; Huang, J. Infrared and visible image fusion via gradient transfer and total variation minimization. Inf. Fusion 2016, 31, 100–109. [Google Scholar] [CrossRef]
- Ma, J.; Yu, W.; Liang, P.; Li, C.; Jiang, J. FusionGAN: A generative adversarial network for infrared and visible image fusion. Inf. Fusion 2019, 48, 11–26. [Google Scholar] [CrossRef]
- Hossny, M.; Nahavandi, S.; Creighton, D. Comments on Information measure for performance of image fusion. Electron. Lett. 2008, 44, 1066–1067. [Google Scholar] [CrossRef] [Green Version]
- Xydeas, C.; Petrovic, V. Objective image fusion performance measure. Electron. Lett. 2000, 36, 308–309. [Google Scholar] [CrossRef] [Green Version]
- Yang, C.; Zhang, J.Q.; Wang, X.R.; Liu, X. A novel similarity based quality metric for image fusion. Inf. Fusion 2008, 9, 156–160. [Google Scholar] [CrossRef]
- Han, Y.; Cai, Y.; Cao, Y.; Xu, X. A new image fusion performance metric based on visual information fidelity. Inf. Fusion 2013, 14, 127–135. [Google Scholar] [CrossRef]
Method | MI | VIFF | ||
---|---|---|---|---|
LP | 0.5614 | 0.6279 | 0.9309 | 1.2307 |
CVT | 0.5614 | 0.4309 | 0.9390 | 0.9317 |
NSST-VGG | 0.5707 | 0.5438 | 0.9376 | 1.1296 |
GFT | 0.5978 | 0.4444 | 0.9663 | 0.8473 |
Fusion GAN | 0.6771 | 0.2319 | 0.9458 | 0.6397 |
Proposed | 0.6827 | 0.6532 | 0.9551 | 1.3062 |
Method | MI | VIFF | ||
---|---|---|---|---|
LP | 8.382 × 10−5 | 2.396 × 10−4 | 7.904 × 10−5 | 1.257 × 10−5 |
CVT | 3.246 × 10−6 | 5.494 × 10−12 | 1.312 × 10−4 | 2.392 × 10−11 |
NSST-VGG | 1.183 × 10−5 | 7.662 × 10−11 | 8.805 × 10−5 | 1.238 × 10−7 |
GFT | 1.601 × 10−3 | 1.510 × 10−11 | 0.0790 | 1.346 × 10−12 |
FusionGAN | 0.4428 | 1.049 × 10−11 | 0.2528 | 3.492 × 10−15 |
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Cao, P.; Li, X.; Ding, M. A Fusion Method for Atomic Force Acoustic Microscopy Cell Imaging Based on Local Variance in Non-Subsampled Shearlet Transform Domain. Appl. Sci. 2020, 10, 7424. https://doi.org/10.3390/app10217424
Cao P, Li X, Ding M. A Fusion Method for Atomic Force Acoustic Microscopy Cell Imaging Based on Local Variance in Non-Subsampled Shearlet Transform Domain. Applied Sciences. 2020; 10(21):7424. https://doi.org/10.3390/app10217424
Chicago/Turabian StyleCao, Pengxin, Xiaoqing Li, and Mingyue Ding. 2020. "A Fusion Method for Atomic Force Acoustic Microscopy Cell Imaging Based on Local Variance in Non-Subsampled Shearlet Transform Domain" Applied Sciences 10, no. 21: 7424. https://doi.org/10.3390/app10217424
APA StyleCao, P., Li, X., & Ding, M. (2020). A Fusion Method for Atomic Force Acoustic Microscopy Cell Imaging Based on Local Variance in Non-Subsampled Shearlet Transform Domain. Applied Sciences, 10(21), 7424. https://doi.org/10.3390/app10217424