Laser & Optoelectronics Progress, Volume. 61, Issue 6, 0618013(2024)

Advancements in Quantitative Evaluation Methods for Optical Microscopic Images (Invited)

Jin Wang1, Zuxin Zhang1, Xieyu Chen1, Jianjie Dong1, Cuifang Kuang1,2, and Wenjie Liu1,2、*
Author Affiliations
  • 1Zhejiang Lab, Hangzhou 311121, Zhejiang, China
  • 2State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China
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    References(100)

    [1] Sheikh H R, Sabir M F, Bovik A C. A statistical evaluation of recent full reference image quality assessment algorithms[J]. IEEE Transactions on Image Processing, 15, 3440-3451(2006).

    [2] Zhang L, Zhang L, Mou X Q et al. A comprehensive evaluation of full reference image quality assessment algorithms[C], 1477-1480(2013).

    [3] Moorthy A K, Bovik A C. Blind image quality assessment: from natural scene statistics to perceptual quality[J]. IEEE Transactions on Image Processing, 20, 3350-3364(2011).

    [4] Mittal A, Moorthy A K, Bovik A C. No-reference image quality assessment in the spatial domain[J]. IEEE Transactions on Image Processing, 21, 4695-4708(2012).

    [5] Saad M A, Bovik A C, Charrier C. Blind image quality assessment: a natural scene statistics approach in the DCT domain[J]. IEEE Transactions on Image Processing, 21, 3339-3352(2012).

    [6] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).

    [7] Wang W J, Wang H Y, Yang S K et al. Resolution enhancement in microscopic imaging based on generative adversarial network with unpaired data[J]. Optics Communications, 503, 127454(2022).

    [8] Chen L C, Papandreou G, Kokkinos I et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848(2018).

    [9] Shah Z H, Müller M, Wang T C et al. Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images[J]. Photonics Research, 9, B168-B181(2021).

    [10] Faklaris O, Bancel-Vallée L, Dauphin A et al. Quality assessment in light microscopy for routine use through simple tools and robust metrics[J]. The Journal of Cell Biology, 221, e202107093(2022).

    [11] Chen X Z, Zeng Z P, Li R Q et al. Superior performance with sCMOS over EMCCD in super-resolution optical fluctuation imaging[J]. Journal of Biomedical Optics, 21, 66007(2016).

    [14] An S, Dan D, Yu X H et al. Progress and prospect of research on single-molecule localization super-resolution microscopy(invited review)[J]. Acta Photonica Sinica, 49, 0918001(2020).

    [15] Willig K I, Rizzoli S O, Westphal V et al. STED microscopy reveals that synaptotagmin remains clustered after synaptic vesicle exocytosis[J]. Nature, 440, 935-939(2006).

    [16] Schermelleh L, Carlton P M, Haase S et al. Subdiffraction multicolor imaging of the nuclear periphery with 3D structured illumination microscopy[J]. Science, 320, 1332-1336(2008).

    [17] Betzig E, Patterson G H, Sougrat R et al. Imaging intracellular fluorescent proteins at nanometer resolution[J]. Science, 313, 1642-1645(2006).

    [18] Rust M J, Bates M, Zhuang X W. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM)[J]. Nature Methods, 3, 793-796(2006).

    [19] Li M T, Huang Z L. Rethinking resolution estimation in fluorescence microscopy: from theoretical resolution criteria to super-resolution microscopy[J]. Science China Life Sciences, 63, 1776-1785(2020).

    [20] Douglass K M, Sieben C, Archetti A et al. Super-resolution imaging of multiple cells by optimized flat-field epi-illumination[J]. Nature Photonics, 10, 705-708(2016).

    [21] Li M T, Song Q H, Xiao Y H et al. LuckyProfiler: an ImageJ plug-in capable of quantifying FWHM resolution easily and effectively for super-resolution images[J]. Biomedical Optics Express, 13, 4310-4325(2022).

    [22] Pospíšil J, Fliegel K, Švihlík J et al. Comparison of resolution estimation methods in optical microscopy[J]. Proceedings of SPIE, 10752, 107522Q(2018).

    [23] Demmerle J, Wegel E, Schermelleh L et al. Assessing resolution in super-resolution imaging[J]. Methods, 88, 3-10(2015).

    [24] Banterle N, Bui K H, Lemke E A et al. Fourier ring correlation as a resolution criterion for super-resolution microscopy[J]. Journal of Structural Biology, 183, 363-367(2013).

    [25] Koho S, Tortarolo G, Castello M et al. Fourier ring correlation simplifies image restoration in fluorescence microscopy[J]. Nature Communications, 10, 3103(2019).

    [26] van Heel M, Schatz M. Fourier shell correlation threshold criteria[J]. Journal of Structural Biology, 151, 250-262(2005).

    [27] Tortarolo G, Castello M, Diaspro A et al. Evaluating image resolution in stimulated emission depletion microscopy[J]. Optica, 5, 32-35(2018).

    [29] Sage D, Kirshner H, Pengo T et al. Quantitative evaluation of software packages for single-molecule localization microscopy[J]. Nature Methods, 12, 717-724(2015).

    [30] Culley S, Albrecht D, Jacobs C et al. Quantitative mapping and minimization of super-resolution optical imaging artifacts[J]. Nature Methods, 15, 263-266(2018).

    [31] Zhao W S, Huang X S, Yang J Y et al. Quantitatively mapping local quality of super-resolution microscopy by rolling Fourier ring correlation[J]. Light: Science & Applications, 12, 298(2023).

    [32] Luo X L, Zhou Z, Huang J F et al. Resolution evaluation method and applications of 3D microscopic images[J]. Chinese Journal of Lasers, 49, 0507205(2022).

    [33] Descloux A, Grußmayer K S, Radenovic A. Parameter-free image resolution estimation based on decorrelation analysis[J]. Nature Methods, 16, 918-924(2019).

    [34] Tariq T, Gonzalez Bello J L, Kim M. A HVS-inspired attention to improve loss metrics for CNN-based perception-oriented super-resolution[C], 3904-3912(2020).

    [35] Girod B. What’s wrong with mean-squared error?[J]. Digital Images and Human Vision, 207-220(1993).

    [36] Qiao C, Li D, Guo Y T et al. Evaluation and development of deep neural networks for image super-resolution in optical microscopy[J]. Nature Methods, 18, 194-202(2021).

    [37] Greeshma M S, Bindu V R. Super-resolution Quality Criterion (SRQC): a super-resolution image quality assessment metric[J]. Multimedia Tools and Applications, 79, 35125-35146(2020).

    [38] Ma L, Rathgeb A, Tran M et al. Unsupervised super resolution network for hyperspectral histologic imaging[J]. Proceedings of SPIE, 12039, 120390P(2022).

    [39] Laine R F, Tosheva K L, Gustafsson N et al. NanoJ: a high-performance open-source super-resolution microscopy toolbox[J]. Journal of Physics D: Applied Physics, 52, 163001(2019).

    [40] Luisier F, Blu T, Unser M. Image denoising in mixed Poisson-Gaussian noise[J]. IEEE Transactions on Image Processing, 20, 696-708(2011).

    [41] Moester M J B, Ariese F, de Boer J F. Optimized signal-to-noise ratio with shot noise limited detection in stimulated Raman scattering microscopy[J]. Journal of the European Optical Society: Rapid Publications, 10, 15022(2015).

    [42] Wang Y F, Kuang C F, Gu Z T et al. Image subtraction method for improving lateral resolution and SNR in confocal microscopy[J]. Optics & Laser Technology, 48, 489-494(2013).

    [43] Guney G, Uluc N, Demirkiran A et al. Comparison of noise reduction methods in photoacoustic microscopy[J]. Computers in Biology and Medicine, 109, 333-341(2019).

    [44] de Vos W H, Munck S, Timmermans J P[M]. Focus on bio-image informatics(2016).

    [45] Su H, Zhou J, Zhang Z H. Survey of super-resolution image reconstruction methods[J]. Acta Automatica Sinica, 39, 1202-1213(2013).

    [46] Nelson K, Bhatti A, Nahavandi S. Performance evaluation of multi-frame super-resolution algorithms[C](2013).

    [47] Paul P, Kalamatianos D, Duessmann H et al. Automatic quality assessment for fluorescence microscopy images[C](2008).

    [48] Vietz C, Schütte M L, Wei Q S et al. Benchmarking smartphone fluorescence-based microscopy with DNA origami nanobeads: reducing the gap toward single-molecule sensitivity[J]. ACS Omega, 4, 637-642(2019).

    [49] Brunstein M, Cattoni A, Estrada L et al. Improving image contrast in fluorescence microscopy with nanostructured substrates[J]. Optics Express, 23, 29772-29778(2015).

    [50] Zhou Y, Hong M H. Realization of noncontact confocal optical microsphere imaging microscope[J]. Microscopy Research and Technique, 84, 2381-2387(2021).

    [51] Wang Z, Bovik A C, Sheikh H R et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 13, 600-612(2004).

    [52] Mudeng V, Kim M, Choe S W. Prospects of structural similarity index for medical image analysis[J]. Applied Sciences, 12, 3754(2022).

    [53] Palubinskas G. Image similarity/distance measures: what is really behind MSE and SSIM?[J]. International Journal of Image and Data Fusion, 8, 32-53(2017).

    [54] Zhang L, Zhang L, Mou X Q et al. FSIM: a feature similarity index for image quality assessment[J]. IEEE Transactions on Image Processing, 20, 2378-2386(2011).

    [55] Gao S, Xu F, Li H J et al. DETECTOR: structural information guided artifact detection for super-resolution fluorescence microscopy image[J]. Biomedical Optics Express, 12, 5751-5769(2021).

    [56] Laine R F, Arganda-Carreras I, Henriques R et al. Avoiding a replication crisis in deep-learning-based bioimage analysis[J]. Nature Methods, 18, 1136-1144(2021).

    [57] Liu L X, Liu B, Huang H et al. No-reference image quality assessment based on spatial and spectral entropies[J]. Signal Processing: Image Communication, 29, 856-863(2014).

    [58] Rajagopal H, Mokhtar N, Khairuddin A S M et al. Gray level co-occurrence matrix (GLCM) and Gabor features based no-reference image quality assessment for wood images[J]. Proceedings of International Conference on Artificial Life and Robotics, 26, 736-741(2021).

    [59] Sadiq A, Nizami I F, Anwar S M et al. Blind image quality assessment using natural scene statistics of stationary wavelet transform[J]. Optik, 205, 164189(2020).

    [60] Joshi P, Prakash S. Continuous wavelet transform based no-reference image quality assessment for blur and noise distortions[J]. IEEE Access, 6, 33871-33882(2018).

    [61] Zhang M, Muramatsu C, Zhou X R et al. Blind image quality assessment using the joint statistics of generalized local binary pattern[J]. IEEE Signal Processing Letters, 22, 207-210(2015).

    [62] Zhang M, Xie J, Zhou X R et al. No reference image quality assessment based on local binary pattern statistics[C](2014).

    [63] Liu A M, Lin W S, Narwaria M. Image quality assessment based on gradient similarity[J]. IEEE Transactions on Image Processing, 21, 1500-1512(2012).

    [64] Wasson V, Kaur B. Image Quality Assessment: edge based entropy features estimation using Soft Computing Techniques[J]. Materials Today: Proceedings, 56, 3261-3271(2022).

    [65] Dong X J, Fu L, Liu Q. No-reference image quality assessment for confocal endoscopy images with perceptual local descriptor[J]. Journal of Biomedical Optics, 27, 056503(2022).

    [66] Abd-Alameer S A, Daway H G, Rashid H G. Quality of medical microscope image at different lighting condition[J]. IOP Conference Series: Materials Science and Engineering, 871, 012072(2020).

    [67] Huang X W, Li Y B, Xu X F et al. High-precision lensless microscope on a chip based on in-line holographic imaging[J]. Sensors, 21, 720(2021).

    [68] Koho S, Fazeli E, Eriksson J E et al. Image quality ranking method for microscopy[J]. Scientific Reports, 6, 28962(2016).

    [69] Mittal A, Soundararajan R, Bovik A C. Making a “completely blind” image quality analyzer[J]. IEEE Signal Processing Letters, 20, 209-212(2013).

    [70] Li L D, Lin W S, Wang X S et al. No-reference image blur assessment based on discrete orthogonal moments[J]. IEEE Transactions on Cybernetics, 46, 39-50(2016).

    [71] Knapp T, Lima N, Duan S et al. Evaluation of tile artifact correction methods for multiphoton microscopy mosaics of whole-slide tissue sections[J]. Proceedings of SPIE, 11966, 119660D(2022).

    [72] Wu P, Zhang D J, Yuan J et al. Large depth-of-field fluorescence microscopy based on deep learning supported by Fresnel incoherent correlation holography[J]. Optics Express, 30, 5177-5191(2022).

    [73] Laine R F, Heil H S, Coelho S et al. High-fidelity 3D live-cell nanoscopy through data-driven enhanced super-resolution radial fluctuation[J]. Nature Methods, 20, 1949-1956(2023).

    [75] Ball G, Demmerle J, Kaufmann R et al. SIMcheck: a toolbox for successful super-resolution structured illumination microscopy[J]. Scientific Reports, 5, 15915(2015).

    [76] Peng T Y, Thorn K, Schroeder T et al. A BaSiC tool for background and shading correction of optical microscopy images[J]. Nature Communications, 8, 14836(2017).

    [77] Liang Y X, Yan M, Tang Z H et al. Learning to autofocus based on Gradient Boosting Machine for optical microscopy[J]. Optik, 198, 163002(2019).

    [78] Wei L, Roberts E. Neural network control of focal position during time-lapse microscopy of cells[J]. Scientific Reports, 8, 7313(2018).

    [79] Wang X Z, Liu L, Du X H et al. GMANet: gradient mask attention network for finding clearest human fecal microscopic image in autofocus process[J]. Applied Sciences, 11, 10293(2021).

    [80] Quintana-Quintana L, Ortega S, Fabelo H et al. Blur-specific image quality assessment of microscopic hyperspectral images[J]. Optics Express, 31, 12261-12279(2023).

    [81] Sigdel M S, Sigdel M, Dinç S et al. Autofocusing for microscopic images using Harris Corner Response Measure[C](2014).

    [82] Jiang H S, Ma L, Wang X Y et al. Focus prediction of medical microscopic images based on Lightweight Densely Connected with Squeeze-and-Excitation Network[J]. Frontiers in Neuroscience, 17, 1213176(2023).

    [83] Yuan T, Yi D R, Jiang W et al. Double blur micro-images focusing evaluation method[J]. Acta Optica Sinica, 43, 1010001(2023).

    [84] Xu Y, Wang X, Zhai C H et al. A single-shot autofocus approach for surface plasmon resonance microscopy[J]. Analytical Chemistry, 93, 2433-2439(2021).

    [85] Dong C, Loy C C, He K M, Fleet D, Pajdla T, Schiele B et al. Learning a deep convolutional network for image super-resolution[M]. Computer vision-ECCV 2014, 8692, 184-199(2014).

    [86] Kim J, Lee J K, Lee K M. Accurate image super-resolution using very deep convolutional networks[C], 1646-1654(2016).

    [87] Fang Y M, Zhang C, Yang W H et al. Blind visual quality assessment for image super-resolution by convolutional neural network[J]. Multimedia Tools and Applications, 77, 29829-29846(2018).

    [88] Fang L J, Monroe F, Novak S W et al. Deep learning-based point-scanning super-resolution imaging[J]. Nature Methods, 18, 406-416(2021).

    [89] Jena B, Digdarshi D, Paul S et al. Effect of learning parameters on the performance of the U-Net architecture for cell nuclei segmentation from microscopic cell images[J]. Microscopy, 72, 249-264(2023).

    [90] Weigert M, Schmidt U, Boothe T et al. Content-aware image restoration: pushing the limits of fluorescence microscopy[J]. Nature Methods, 15, 1090-1097(2018).

    [91] Li J Y, Tong G, Pan Y N et al. Spatial and temporal super-resolution for fluorescence microscopy by a recurrent neural network[J]. Optics Express, 29, 15747-15763(2021).

    [92] Zhang H, Fang C Y, Xie X L et al. High-throughput, high-resolution deep learning microscopy based on registration-free generative adversarial network[J]. Biomedical Optics Express, 10, 1044-1063(2019).

    [93] Yang W M, Zhang X C, Tian Y P et al. Deep learning for single image super-resolution: a brief review[J]. IEEE Transactions on Multimedia, 21, 3106-3121(2019).

    [94] Wang T, Dong W D, Shen K et al. Sparse-view photoacoustic image quality enhancement based on a modified U-Net[J]. Laser & Optoelectronics Progress, 59, 0617022(2022).

    [95] Qu Y Y, Chen Y Z, Huang J Y et al. Enhanced Pix2pix dehazing network[C], 8152-8160(2020).

    [97] Ma Y D, Liu Q, Qian Z B. Automated image segmentation using improved PCNN model based on cross-entropy[C], 743-746(2005).

    [98] Yang B, Wu M X, Teizer W. Modified UNet++ with attention gate for graphene identification by optical microscopy[J]. Carbon, 195, 246-252(2022).

    [99] Milletari F, Navab N, Ahmadi S A. V-net: fully convolutional neural networks for volumetric medical image segmentation[C], 565-571(2016).

    [100] Patel G, Tekchandani H, Verma S. Cellular segmentation of bright-field absorbance images using residual U-net[C](2020).

    [101] von Chamier L, Laine R F, Jukkala J et al. Democratising deep learning for microscopy with ZeroCostDL4Mic[J]. Nature Communications, 12, 2276(2021).

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    Jin Wang, Zuxin Zhang, Xieyu Chen, Jianjie Dong, Cuifang Kuang, Wenjie Liu. Advancements in Quantitative Evaluation Methods for Optical Microscopic Images (Invited)[J]. Laser & Optoelectronics Progress, 2024, 61(6): 0618013

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    Paper Information

    Category: Microscopy

    Received: Nov. 3, 2023

    Accepted: Dec. 11, 2023

    Published Online: Mar. 22, 2024

    The Author Email: Liu Wenjie (wenjieliu@zju.edu.cn)

    DOI:10.3788/LOP232433

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