21.1.4 Medical Applications, Microscope Image Analysis

Chapter Contents (Back)
Microscope.
See also Super Resolution in Microscope Image Analysis.
See also Electron Microscope Images and Sensors.
See also Histopathology, Tissue Analysis.
See also Mitochondria DNA Analysis and Extraction.

MOTA Object Tracking Benchmark,
2021 for workshop.
WWW Link. Dataset, Cell Tracking.

von Wegner, F., Both, M., Fink, R.H.A., Friedrich, O.,
Fast XYT Imaging of Elementary Calcium Release Events in Muscle With Multifocal Multiphoton Microscopy and Wavelet Denoising and Detection,
MedImg(26), No. 7, July 2007, pp. 925-934.
IEEE DOI 0707
BibRef

Yeom, S., Moon, I, Javidi, B.,
Real-Time 3-D Sensing, Visualization and Recognition of Dynamic Biological Microorganisms,
PIEEE(94), No. 3, March 2006, pp. 550-566.
IEEE DOI 0603
BibRef

Cortés, L.[Leandro], Amit, Y.[Yali],
Efficient Annotation of Vesicle Dynamics Video Microscopy,
PAMI(30), No. 11, November 2008, pp. 1998-2010.
IEEE DOI 0809
BibRef

Bodvarsson, B., Klim, S., Morkebjerg, M., Mortensen, S., Yoon, C.H., Chen, J., Maclaren, J.R., Luther, P.K., Squire, J.M., Bones, P.J., Millane, R.P.,
A morphological image processing method for locating myosin filaments in muscle electron micrographs,
IVC(26), No. 8, 1 August 2008, pp. 1073-1080.
Elsevier DOI 0806
Image analysis; Morphology; Electron micrograph; Disorder; Myosin; Muscle BibRef

Wojtas, D.H., Ayyer, K., Liang, M., Mossou, E., Seuring, C., Forsyth, V.T., Chapman, H.N., Millane, R.P.,
Orientation and analysis of XFEL serial diffraction patterns from fibrous molecular assemblies,
IVCNZ17(1-6)
IEEE DOI 1902
biological techniques, crystal structure, free electron lasers, molecular biophysics, molecular configurations, biological system BibRef

Begelman, G., Zibulevsky, M., Rivlin, E., Kolatt, T.,
Blind Decomposition of Transmission Light Microscopic Hyperspectral Cube Using Sparse Representation,
MedImg(28), No. 8, August 2009, pp. 1317-1324.
IEEE DOI 0909
BibRef

Begelman, G.[Grigory], Pechuk, M.[Michael], Rivlin, E.[Ehud], Sabo, E.[Edmond],
System for Computer-Aided Multiresolution Microscopic Pathology Diagnostics,
CVS06(16).
IEEE DOI 0602
BibRef

Maurer, M.R.[Mauricio Rafael], Pedrini, H.[Helio], Ferreira Randi, M.A.[Marco Antonio],
Processing and Visualization of Light Microscope Images,
IJIG(9), No. 3, July 2009, pp. 369-388.
DOI Link 0911
BibRef

Calapez, A., Rosa, A.,
A Statistical Pixel Intensity Model for Segmentation of Confocal Laser Scanning Microscopy Images,
IP(19), No. 9, September 2010, pp. 2408-2418.
IEEE DOI 1008
BibRef

Roberts, T.J.[Timothy J.], McKenna, S.J.[Stephen J.], Du, C.J.[Cheng-Jin], Wuyts, N.[Nathalie], Valentine, T.A.[Tracy A.], Bengough, A.G.[A. Glyn],
Estimating the motion of plant root cells from in vivo confocal laser scanning microscopy images,
MVA(21), No. 6, October 2010, pp. 921-939.
WWW Link. 1011
BibRef

Díaz, M.E.[María Elena], Ayala, G.[Guillermo], Díaz, E.[Ester],
Estimating the Duration of Overlapping Events from Image Sequences Using Cylindrical Temporal Boolean Models,
JMIV(38), No. 2, October 2010, pp. 83-94.
WWW Link. 1011
Cell biology, microscope analysis. BibRef

Kenig, T.[Tal], Kam, Z.[Zvi], Feuer, A.[Arie],
Blind Image Deconvolution Using Machine Learning for Three-Dimensional Microscopy,
PAMI(32), No. 12, December 2010, pp. 2191-2204.
IEEE DOI 1011
for microscopic image enhancement. BibRef

Wang, Z., Millet, L.J., Mir, M., Ding, H., Unarunotai, S., Rogers, J.A., Gillette, M.U., Popescu, G.,
Spatial light interference microscopy (SLIM),
OptExp(19), No. 2, 2011, pp. 1016.
WWW Link.
WWW Link. 1109
BibRef

Babacan, D., Wang, Z., Do, M., opescu, G.,
Cell imaging beyond the diffraction limit using sparse deconvolution spatial light interference microscopy,
BioOptExp(2), No. 7, 2011.
WWW Link. 1109
BibRef

Pham, H., Ding, H., Sobh, N., Do, M., Patel, S., Popescu, G.,
Off-axis quantitative phase imaging processing using CUDA: Toward real-time applications,
BioOptExp(2), No. 7, 2011.
WWW Link. 1109
BibRef

Fehrenbach, J., Weiss, P., Lorenzo, C.,
Variational Algorithms to Remove Stationary Noise: Applications to Microscopy Imaging,
IP(21), No. 10, October 2012, pp. 4420-4430.
IEEE DOI 1209
BibRef

Fehrenbach, J., Weiss, P.,
Processing Stationary Noise: Model and Parameter Selection in Variational Methods,
SIIMS(7), No. 2, 2014, pp. 613-640.
DOI Link 1405
BibRef

Seelamantula, C.S.[Chandra Sekhar], Pavillon, N.[Nicolas], Depeursinge, C.[Christian], Unser, M.[Michael],
Local demodulation of holograms using the Riesz transform with application to microscopy,
JOSA-A(29), No. 10, October 2012, pp. 2118-2129.
WWW Link. 1210
BibRef

Benazzouz, M.[Mourtada], Baghli, I.[Ismahan], Chikh, M.A.[Med Amine],
Microscopic image segmentation based on pixel classification and dimensionality reduction,
IJIST(23), No. 1, March 2013, pp. 22-28.
DOI Link 1303
BibRef

Park, C.[Chiwoo], Huang, J.Z., Ji, J.X., Ding, Y.[Yu],
Segmentation, Inference and Classification of Partially Overlapping Nanoparticles,
PAMI(35), No. 3, March 2013, pp. 1.
IEEE DOI 1303
morphology process. BibRef

Histace, A., Meziou, L., Matuszewski, B.J., Precioso, F., Murphy, M.F., Carreiras, F.,
Statistical region based active contour using a fractional entropy descriptor: Application to nuclei cell segmentation in confocal microscopy images,
BMVA(2013), No. 1, 2013, pp. 5, 1-15.
PDF File. 1304
BibRef

Meziou, L.[Leila], Histace, A.[Aymeric], Precioso, F.[Frederic],
Statistical region-based active contour using optimization of alpha-divergence family for image segmentation,
ICIP14(6066-6070)
IEEE DOI 1502
Decision support systems BibRef

Meziou, L., Histace, A., Precioso, F., Matuszewski, B.J., Murphy, M.F.,
Confocal microscopy segmentation using active contour based on alpha(alpha)-divergence,
ICIP11(3077-3080).
IEEE DOI 1201
BibRef

Smochina, C.[Cristian], Manta, V.[Vasile], Kropatsch, W.G.[Walter G.],
Crypts detection in microscopic images using hierarchical structures,
PRL(34), No. 8, June 2013, pp. 934-941.
Elsevier DOI 1305
BibRef
Earlier:
Semantic Segmentation of Microscopic Images Using a Morphological Hierarchy,
CAIP11(I: 102-109).
Springer DOI 1109
Crypt segmentation; Morphological hierarchy; Morphological pyramid; Biomedical imaging; Pathology; Microscopy BibRef

Kumar, V.,
Orientation Imaging Microscopy With Optimized Convergence Angle Using CBED Patterns in TEMs,
IP(22), No. 7, 2013, pp. 2637-2645.
IEEE DOI 1307
electron diffraction; orientation imaging microscopy BibRef

Bostan, E., Kamilov, U.S., Nilchian, M., Unser, M.,
Sparse Stochastic Processes and Discretization of Linear Inverse Problems,
IP(22), No. 7, 2013, pp. 2699-2710.
IEEE DOI 1307
X-ray microscopy; magnetic resonance imaging BibRef

Chao, J.[Jerry], Ram, S.[Sripad], Ober, R.[Raimund], Ward, E.S.[E. Sally],
Low-light imaging method provides highly accurate molecule localization,
SPIE(Newsroom), June 24, 2013
DOI Link 1307
Imaging technique estimates the location of individual particles with nearly the same accuracy that is achievable only in the absence of detector noise and pixelation. BibRef

Maire, G.[Guillaume], Ruan, Y.[Yi], Zhang, T.[Ting], Chaumet, P.C.[Patrick C.], Giovannini, H.[Hugues], Sentenac, D.[Daniel], Talneau, A.[Anne], Belkebir, K.[Kamal], Sentenac, A.[Anne],
High-resolution tomographic diffractive microscopy in reflection configuration,
JOSA-A(30), No. 10, October 2013, pp. 2133-2139.
WWW Link. 1310
BibRef

Trattner, S.[Sigal], Kashdan, E.[Eugene], Feigin, M.[Micha], Sochen, N.A.[Nir A.],
Image formation of thick three-dimensional objects in differential-interference-contrast microscopy,
JOSA-A(31), No. 5, May 2014, pp. 968-980.
DOI Link 1405
Image formation theory; Microscopy; Scattering BibRef

Dietlmeier, J.[Julia], Ghita, O.[Ovidiu], Whelan, P.F.[Paul F.],
On the projection similarity in line grouping,
PRL(51), No. 1, 2015, pp. 50-56.
Elsevier DOI 1412
Line grouping BibRef

Rieger, B., Nieuwenhuizen, R., Stallinga, S.,
Image Processing and Analysis for Single-Molecule Localization Microscopy: Computation for nanoscale imaging,
SPMag(32), No. 1, January 2015, pp. 49-57.
IEEE DOI 1502
fluorescence BibRef

Bal, U.[Ufuk], Engin, M.[Mehmet], Utzinger, U.[Urs],
A multiresolution approach for enhancement and denoising of microscopy images,
SIViP(9), No. 4, May 2015, pp. 787-799.
WWW Link. 1504
BibRef

Merola, F., Memmolo, P., Miccio, L., Bianco, V., Paturzo, M., Ferraro, P.,
Diagnostic Tools for Lab-on-Chip Applications Based on Coherent Imaging Microscopy,
PIEEE(103), No. 2, February 2015, pp. 192-204.
IEEE DOI 1504
Biomedical optical imaging BibRef

Becker, C., Christoudias, C.M., Fua, P.,
Domain Adaptation for Microscopy Imaging,
MedImg(34), No. 5, May 2015, pp. 1125-1139.
IEEE DOI 1505
Boosting BibRef

Nellros, F.[Frida], Thurley, M.J.[Matthew J.], Jonsson, H.[Hĺkan], Andersson, C.[Charlotte], Forsmo, S.P.E.[Seija P.E.],
Automated measurement of sintering degree in optical microscopy through image analysis of particle joins,
PR(48), No. 11, 2015, pp. 3451-3465.
Elsevier DOI 1506
Image analysis BibRef

Zuo, C.[Chao],
Computational phase imaging for light microscopes,
SPIE(Newsroom), November 4, 2015
DOI Link 1511
A combination of optics coding and digital processing enhances the capabilities of traditional light microscopes, enabling acquisition of information such as phase, which cannot otherwise be captured. BibRef

Moser, C.[Christophe], Loterie, D.[Damien],
Digital confocal microscopy through a multimode fiber,
SPIE(Newsroom), October 16, 2015
DOI Link 1511
Compensating for modal scrambling during illumination and detection enables the use of multimode fibers to transmit high-contrast, confocal images for endoscopic applications. BibRef

Cohen, E.A.K., Kim, D., Ober, R.J.,
Cramer-Rao Lower Bound for Point Based Image Registration With Heteroscedastic Error Model for Application in Single Molecule Microscopy,
MedImg(34), No. 12, December 2015, pp. 2632-2644.
IEEE DOI 1601
affine transforms BibRef

Yukihara, E.G., Foiez Ahmed, M.,
Pixel Bleeding Correction in Laser Scanning Luminescence Imaging Demonstrated Using Optically Stimulated Luminescence,
MedImg(34), No. 12, December 2015, pp. 2506-2517.
IEEE DOI 1601
aluminium compounds BibRef

Verrier, N.[Nicolas], Fournier, C.[Corinne], Cazier, A.[Anthony], Fournel, T.[Thierry],
Co-design of an in-line holographic microscope with enhanced axial resolution: Selective filtering digital holography,
JOSA-A(33), No. 1, January 2016, pp. 107-116.
DOI Link 1601
Image reconstruction techniques BibRef

Bostan, E.[Emrah], Froustey, E.[Emmanuel], Nilchian, M., Sage, D.[Daniel], Unser, M.[Michael],
Variational Phase Imaging Using the Transport-of-Intensity Equation,
IP(25), No. 2, February 2016, pp. 807-817.
IEEE DOI 1601
Image reconstruction BibRef

Bostan, E.[Emrah], Froustey, E.[Emmanuel], Rappaz, B.[Benjamin], Shaffer, E.[Etienne], Sage, D.[Daniel], Unser, M.[Michael],
Phase retrieval by using transport-of-intensity equation and differential interference contrast microscopy,
ICIP14(3939-3943)
IEEE DOI 1502
Equations BibRef

Wang, Y.F.[Yun-Feng], Kilpatrick, J.I.[Jason I.], Jarvis, S.P.[Suzanne P.], Boland, F.M.[Francis M.], Kokaram, A.[Anil], Corrigan, D.[David],
Double-Tip Artifact Removal From Atomic Force Microscopy Images,
IP(25), No. 6, June 2016, pp. 2774-2788.
IEEE DOI 1605
Deconvolution BibRef

Yu, Z.X.[Zhi-Xian], Prasad, S.[Sudhakar],
High-numerical-aperture microscopy with a rotating point spread function,
JOSA-A(33), No. 7, July 2016, pp. B58-B69.
DOI Link 1608
Image analysis BibRef

Gopakumar, G., Babu, K.H.[K. Hari], Mishra, D.[Deepak], Gorthi, S.S.[Sai Siva], Subrahmanyam, G.R.K.S.[Gorthi. R. K. Sai],
Cytopathological image analysis using deep-learning networks in microfluidic microscopy,
JOSA-A(34), No. 1, January 2017, pp. 111-121.
DOI Link 1701
Digital image processing BibRef

Yoshida, S.[Shunsuke],
Novel glasses-free tabletop 3D imaging technology for collaborative applications,
SPIE(Newsroom), February 2, 2017
DOI Link 1703
BibRef

Simsek, B.[Burcin], Iyengar, S.[Satish],
On the Distribution of Photon Counts with Censoring in Two-Photon Laser Scanning Microscopy,
JMIV(58), No. 1, May 2017, pp. 47-56.
WWW Link. 1704
BibRef

Kosov, S.[Sergey], Shirahama, K.[Kimiaki], Li, C.[Chen], Grzegorzek, M.[Marcin],
Environmental microorganism classification using conditional random fields and deep convolutional neural networks,
PR(77), 2018, pp. 248-261.
Elsevier DOI 1802
Environmental microorganism, Conditional random fields, Global feature extraction, Image classification, Image segmentation BibRef

Zou, Y., Li, C.[Chen], Shirahama, K.[Kimiaki], Jiang, T., Grzegorzek, M.[Marcin],
Environmental microorganism image retrieval using multiple colour channels fusion and particle swarm optimisation,
ICIP16(2475-2479)
IEEE DOI 1610
Feature extraction BibRef

Nguyen, Q.T.[Quoc Thong], Delignon, Y.[Yves], Septier, F.[François], Phan-Ho, A.T.[Anh Thu],
Probabilistic modelling of printed dots at the microscopic scale,
SP:IC(62), 2018, pp. 129-138.
Elsevier DOI 1802
Probabilistic model, Bernoulli process, Metropolis-Hastings within Gibbs, Microscopic printing, Markov chain BibRef

Meiniel, W., Olivo-Marin, J.C., Angelini, E.D.,
Denoising of Microscopy Images: A Review of the State-of-the-Art, and a New Sparsity-Based Method,
IP(27), No. 8, August 2018, pp. 3842-3856.
IEEE DOI 1806
Gaussian noise, image denoising, microscopy, Gaussian noise, Poisson noise, biological microscopy images, denoising methods, total variation BibRef

Qin, F., Shen, F., Zhang, D., Liu, X., Xu, D.,
Contour Primitives of Interest Extraction Method for Microscopic Images and Its Application on Pose Measurement,
SMCS(48), No. 8, August 2018, pp. 1348-1359.
IEEE DOI 1808
Feature extraction, Microscopy, Cameras, Image edge detection, Manipulators, Machine vision, Robustness, Geometric constraint, precision assembly BibRef

Han, L.[Liang], Yin, Z.Z.[Zhao-Zheng],
Learning to transfer microscopy image modalities,
MVA(29), No. 8, November 2018, pp. 1257-1267.
WWW Link. 1811
BibRef

Kuniyoshi, F.[Fusataka], Funatomi, T.[Takuya], Kubo, H.[Hiroyuki], Sawada, Y.[Yoshihide], Kato, Y.O.[Yumiko O.], Mukaigawa, Y.[Yasuhiro],
Visibility Enhancement by Integrating Refocusing and Direct-Global Separation with Contact Imaging,
IJCV(127), No. 8, August 2019, pp. 1162-1174.
Springer DOI 1907
Compact lensless microscopy technique for living cells. BibRef

Chouzenoux, E.[Emilie], Lau, T.T.K.[Tim Tsz-Kit], Lefort, C.[Claire], Pesquet, J.C.[Jean-Christophe],
Optimal Multivariate Gaussian Fitting with Applications to PSF Modeling in Two-Photon Microscopy Imaging,
JMIV(61), No. 7, September 2019, pp. 1037-1050.
Springer DOI 1908
BibRef

Kim, D.W.[Dae Woo], Aguilar, C., Zhao, H., Comer, M.L.[Mary L.],
Narrow Gap Detection in Microscope Images Using Marked Point Process Modeling,
IP(28), No. 10, October 2019, pp. 5064-5076.
IEEE DOI 1909
BibRef
Earlier: A1, A4, Only:
Channel detection in microscope images of materials using marked point process modeling,
ICIP15(3054-3058)
IEEE DOI 1512
Image segmentation, Microscopy, Channel models, Shape, Markov processes, Task analysis, Monte Carlo methods, MPP, segmentation. Channel detection; Marked Point Process; Segmentation BibRef

Sintorn, M., Bischof, L., Jackway, P., Haggarty, S., Buckley, M.,
Gradient based intensity normalization,
J. Microsc(240), 2010, pp. 249-258.
DOI Link BibRef 1000

Sheppard, C.J.R.[Colin J. R.],
Partially coherent microscope imaging system in phase space: Effect of defocus and phase reconstruction,
JOSA-A(35), No. 11, November 2018, pp. 1846-1854.
DOI Link 1912
Paraxial wave optics, Microscopy, Optical transfer functions, Partial coherence in imaging, X-ray imaging, Spatial frequency BibRef

Mehta, S.B.[Shalin B.], Sheppard, C.J.R.[Colin J. R.],
Partially coherent microscope in phase space,
JOSA-A(35), No. 8, August 2018, pp. 1272-1282.
DOI Link 1912
Paraxial wave optics , Microscopy, Optical transfer functions, Partial coherence in imaging, X-ray imaging, Spatial frequency BibRef

Usmani, K.[Kashif], Ahmad, A.[Azeem], Joshi, R.[Rakesh], Dubey, V.[Vishesh], Butola, A.[Ankit], Mehta, D.S.[Dalip Singh],
Relationship between the source size at the diffuser plane and the longitudinal spatial coherence function of the optical coherence microscopy system,
JOSA-A(36), No. 12, December 2019, pp. D41-D46.
DOI Link 1912
Coherence theory, Laser sources, Light sources, Optical fields, Spatial frequency, White light BibRef

Zhang, L.G.[Li-Guo], Yin, G.S.[Gui-Sheng], Han, Q.L.[Qi-Long], Sun, J.G.[Jian-Guo],
Wide-field and full-focus optical microscopic imaging system,
JOSA-A(36), No. 6, June 2019, pp. 950-963.
DOI Link 1912
Digital image processing, Image fusion, Image metrics, Image processing, Image quality, Medical image processing BibRef

Francis, B.[Bibin], Mathew, M.[Manoj], Arigovindan, M.[Muthuvel],
Multiresolution-based weighted regularization for denoised image interpolation from scattered samples with application to confocal microscopy,
JOSA-A(35), No. 10, October 2018, pp. 1749-1759.
DOI Link 1912
Image reconstruction-restoration, Fluorescence microscopy, Image reconstruction techniques, Inverse problems BibRef

Sentenac, A.[Anne], Mertz, J.[Jerome],
Unified description of three-dimensional optical diffraction microscopy: from transmission microscopy to optical coherence tomography: tutorial,
JOSA-A(35), No. 5, May 2018, pp. 748-754.
DOI Link 1912
Imaging systems, Three-dimensional microscopy, Confocal microscopy, Light fields, Phase imaging, Three dimensional imaging BibRef

Mobiny, A., Lu, H., Nguyen, H.V., Roysam, B., Varadarajan, N.,
Automated Classification of Apoptosis in Phase Contrast Microscopy Using Capsule Network,
MedImg(39), No. 1, January 2020, pp. 1-10.
IEEE DOI 2001
Routing, Task analysis, Training, Microscopy, Face, Feature extraction, Pediatrics, Apoptosis, capsule network, cell classification BibRef

Hosseini, M.S., Brawley-Hayes, J.A.Z., Zhang, Y., Chan, L., Plataniotis, K.N., Damaskinos, S.,
Focus Quality Assessment of High-Throughput Whole Slide Imaging in Digital Pathology,
MedImg(39), No. 1, January 2020, pp. 62-74.
IEEE DOI 2001
Pathology, Measurement, Kernel, Quality assessment, Feature extraction, Microscopy, MaxPol derivative library BibRef

de Haan, K., Rivenson, Y., Wu, Y., Ozcan, A.,
Deep-Learning-Based Image Reconstruction and Enhancement in Optical Microscopy,
PIEEE(108), No. 1, January 2020, pp. 30-50.
IEEE DOI 2001
Deep learning, Biomedical imaging, Optical imaging, Microscopy, Image reconstruction, Machine learning, Biomedical imaging, deep learning BibRef

Liu, W.Q.[Wen-Qian], Li, W.H.[Wei-Hong], Gong, W.G.[Wei-Guo],
Ensemble of fine-tuned convolutional neural networks for urine sediment microscopic image classification,
IET-CV(14), No. 1, February 2020, pp. 18-25.
DOI Link 2002
BibRef

Yamaguchi, T.[Takahiro], Nagahara, H.[Hajime], Morooka, K.[Ken'ichi], Nakashima, Y.[Yuta], Uranishi, Y.[Yuki], Miyauchi, S.[Shoko], Kurazume, R.[Ryo],
3d Image Reconstruction from Multi-focus Microscopic Images,
PSIVT19(73-85).
Springer DOI 2003
BibRef

Loewke, N.O., Qiu, Z., Mandella, M.J., Ertsey, R., Loewke, A., Gunaydin, L.A., Rosenthal, E.L., Contag, C.H., Solgaard, O.,
Software-Based Phase Control, Video-Rate Imaging, and Real-Time Mosaicing With a Lissajous-Scanned Confocal Microscope,
MedImg(39), No. 4, April 2020, pp. 1127-1137.
IEEE DOI 2004
Phase control, Microscopy, Spatial resolution, Real-time systems, Mirrors, Confocal microscopy, Lissajous imaging, mosaicing, software-based phase control BibRef

Shajkofci, A., Liebling, M.,
Spatially-Variant CNN-Based Point Spread Function Estimation for Blind Deconvolution and Depth Estimation in Optical Microscopy,
IP(29), 2020, pp. 5848-5861.
IEEE DOI 2005
BibRef
Earlier:
Semi-Blind Spatially-Variant Deconvolution in Optical Microscopy with Local Point Spread Function Estimation by Use of Convolutional Neural Networks,
ICIP18(3818-3822)
IEEE DOI 1809
Microscopy, Optical imaging, Optical diffraction, Deconvolution, Estimation, Optical microscopy, Calibration, depth from focus. Deconvolution, Optical imaging, Training, Integrated optics, Optical diffraction, convolutional neural networks BibRef

Cardoen, B., Yedder, H.B., Sharma, A., Chou, K.C., Nabi, I.R., Hamarneh, G.,
ERGO: Efficient Recurrent Graph Optimized Emitter Density Estimation in Single Molecule Localization Microscopy,
MedImg(39), No. 6, June 2020, pp. 1942-1956.
IEEE DOI 2006
Image reconstruction, Microscopy, Proteins, Estimation, Labeling, astigmatism BibRef

Jirik, M.[Miroslav], Moulisova, V.[Vladimira], Schindler, C.[Claudia], Cervenkova, L.[Lenka], Palek, R.[Richard], Rosendorf, J.[Jachym], Arlt, J.[Janine], Bolek, L.[Lukas], Dejmek, J.[Jiri], Dahmen, U.[Uta], Jirikova, K.[Kamila], Gruber, I.[Ivan], Liska, V.[Vaclav], Zelezny, M.[Milos],
Micrant: Towards Regression Task Oriented Annotation Tool for Microscopic Images,
IWCIA20(209-218).
Springer DOI 2009
BibRef

Liu, D., Zhang, D., Song, Y., Zhang, F., O'Donnell, L., Huang, H., Chen, M., Cai, W.,
PDAM: A Panoptic-Level Feature Alignment Framework for Unsupervised Domain Adaptive Instance Segmentation in Microscopy Images,
MedImg(40), No. 1, January 2021, pp. 154-165.
IEEE DOI 2012
Image segmentation, Semantics, Task analysis, Feature extraction, Microscopy, Training, Adaptation models, microscopy images BibRef

Nehme, E.[Elias], Ferdman, B.[Boris], Weiss, L.E.[Lucien E.], Naor, T.[Tal], Freedman, D.[Daniel], Michaeli, T.[Tomer], Shechtman, Y.[Yoav],
Learning Optimal Wavefront Shaping for Multi-Channel Imaging,
PAMI(43), No. 7, July 2021, pp. 2179-2192.
IEEE DOI 2106
Imaging, Microscopy, Location awareness, Optical microscopy, Optical imaging, end-to-end optimization BibRef

Li, A.C.[An-Cin], Vyas, S.I.[Sun-Il], Lin, Y.H.[Yu-Hsiang], Huang, Y.Y.[Yi-You], Huang, H.M.[Hsuan-Ming], Luo, Y.[Yuan],
Patch-Based U-Net Model for Isotropic Quantitative Differential Phase Contrast Imaging,
MedImg(40), No. 11, November 2021, pp. 3229-3237.
IEEE DOI 2111
Microscopy, Transfer functions, Imaging, Phase measurement, Image reconstruction, Biomedical measurement, CycleGAN BibRef

Huang, L.[Ling], Cheng, D.[Deruo], Yang, X.[Xulei], Lin, T.[Tong], Shi, Y.Q.[Yi-Qiong], Yang, K.[Kaiyi], Gwee, B.H.[Bah Hwee], Wen, B.[Bihan],
Joint Anomaly Detection and Inpainting for Microscopy Images Via Deep Self-Supervised Learning,
ICIP21(3497-3501)
IEEE DOI 2201
Deep learning, Training, Shape, Microscopy, Manufacturing, Labeling, Microstructure, Microscopy, anomaly detection, inpainting, real-world dataset BibRef

Arbelle, A.[Assaf], Cohen, S.[Shaked], Raviv, T.R.[Tammy Riklin],
Dual-Task ConvLSTM-UNet for Instance Segmentation of Weakly Annotated Microscopy Videos,
MedImg(41), No. 8, August 2022, pp. 1948-1960.
IEEE DOI 2208
Computer architecture, Microprocessors, Image segmentation, Annotations, Microscopy, Training, Deep learning, image sequences, object segmentation BibRef

Xie, Y.C.[Yao-Chen], Ding, Y.[Yu], Ji, S.W.[Shui-Wang],
Augmented Equivariant Attention Networks for Microscopy Image Transformation,
MedImg(41), No. 11, November 2022, pp. 3194-3206.
IEEE DOI 2211
Computational modeling, Training, Deep learning, Superresolution, Task analysis, Electron microscopy, Neural networks, Deep learning, image transformation BibRef

Wang, Y.K.[Yu-Kun], Gu, Y.F.[Yan-Feng], Li, X.M.[Xiao-Mei],
A Novel Low Rank Smooth Flat-Field Correction Algorithm for Hyperspectral Microscopy Imaging,
MedImg(41), No. 12, December 2022, pp. 3862-3872.
IEEE DOI 2212
Microscopy, Hyperspectral imaging, Imaging, Pathology, Optical microscopy, Optical imaging, Adaptive optics, vignetting BibRef

Yang, Y.[Yang], Tu, Y.[Yanlun], Lei, H.[Houchao], Long, W.[Wei],
HAMIL: Hierarchical aggregation-based multi-instance learning for microscopy image classification,
PR(136), 2023, pp. 109245.
Elsevier DOI 2301
Multi-instance learning, Biomedical image, Hierarchical aggregation BibRef

Courbot, J.B.[Jean-Baptiste], Colicchio, B.[Bruno],
Transformed Gaussian Random Fields for Unsupervised Image Deconvolution,
SPLetters(29), 2022, pp. 2702-2706.
IEEE DOI 2301
Solid modeling, Deconvolution, Monte Carlo methods, Computational modeling, Microscopy, Numerical models, Hamiltonian Monte Carlo BibRef

di Marco, N.[Niccolň], Frosini, A.[Andrea],
The Generalized Microscopic Image Reconstruction Problem for Hypergraphs,
IWCIA22(317-331).
Springer DOI 2301
BibRef

Ç?nar, A.[Ahmet], Erku?, M.[Merve], Tuncer, T.[Taner], Ayy?ld?z, H.[Hakan], Tuncer, S.A.[Seda Arslan],
YOLOv5 based detector for eight different urine particles components on single board computer,
IJIST(34), No. 1, 2024, pp. e22968.
DOI Link 2401
urine particles, urine sediment examination, YOLOv5 BibRef

Li, F.[Fangda], Hu, Z.Q.[Zhi-Qiang], Chen, W.[Wen], Kak, A.[Avinash],
A Laplacian Pyramid Based Generative H&E Stain Augmentation Network,
MedImg(43), No. 2, February 2024, pp. 701-713.
IEEE DOI Code:
WWW Link. 2402
Computer architecture, Training, Morphology, Microprocessors, Computational modeling, Laplace equations, Image color analysis, stain augmentation BibRef


Morin, L.[Lucas], Danelljan, M.[Martin], Agea, M.I.[Maria Isabel], Nassar, A.[Ahmed], Weber, V.[Valery], Meijer, I.[Ingmar], Staar, P.[Peter], Yu, F.[Fisher],
MolGrapher: Graph-based Visual Recognition of Chemical Structures,
ICCV23(19495-19504)
IEEE DOI 2401
BibRef

Ye, E.[Enze], Wang, Y.H.[Yu-Hang], Zhang, H.[Hong], Gao, Y.Q.[Yi-Qin], Wang, H.[Huan], Sun, H.[He],
Recovering a Molecule's 3D Dynamics from Liquid-phase Electron Microscopy Movies,
ICCV23(10733-10743)
IEEE DOI 2401
BibRef

Ebert, N.[Nikolas], Stricker, D.[Didier], Wasenmüller, O.[Oliver],
Transformer-based Detection of Microorganisms on High-Resolution Petri Dish Images,
BioIm23(3963-3972)
IEEE DOI 2401
BibRef

Sonneck, J.[Justin], Zhao, S.[Shuo], Chen, J.[Jianxu],
On the risk of manual annotations in 3D confocal microscopy image segmentation,
BioIm23(3896-3904)
IEEE DOI 2401
BibRef

Jiang, C.[Cheng], Hou, X.H.[Xin-Hai], Kondepudi, A.[Akhil], Chowdury, A.[Asadur], Freudiger, C.W.[Christian W.], Orringer, D.A.[Daniel A.], Lee, H.L.[Hong-Lak], Hollon, T.C.[Todd C.],
Hierarchical Discriminative Learning Improves Visual Representations of Biomedical Microscopy,
CVPR23(19798-19808)
IEEE DOI 2309
BibRef

Möller, B.[Björn], Pirklbauer, J.[Jan], Klingner, M.[Marvin], Kasten, P.[Peer], Etzkorn, M.[Markus], Seifert, T.J.[Tim J.], Schlickum, U.[Uta], Fingscheidt, T.[Tim],
A Super-Resolution Training Paradigm Based on Low-Resolution Data Only to Surpass the Technical Limits of STEM and STM Microscopy,
CVMI23(4263-4272)
IEEE DOI 2309
BibRef

Shats, D.[Daniel], Hezi, H.[Hadar], Shani, G.[Guy], Maruvka, Y.E.[Yosef E.], Freiman, M.[Moti],
Patient-level Microsatellite Stability Assessment from Whole Slide Images by Combining Momentum Contrast Learning and Group Patch Embeddings,
MCV22(454-465).
Springer DOI 2304
BibRef

Liu, Y.[Yi], Caplan, J.[Jeffrey], Kambhamettu, C.[Chandra],
Extraction and Quantification of Actin Cytoskeleton in Microscopic Images Using a Deep Learning Based Framework and a Curve Clustering Model,
ICPR22(4270-4276)
IEEE DOI 2212
Deep learning, Histograms, Shape, Microscopy, Pipelines, Organizations, Feature extraction BibRef

Yayci, Z.O.[Zeynep Ovgu], Dura, U.[Ugur], Kaya, Z.B.[Zeynep Betul], Cetin, A.E.[Arif E.], Turkan, M.[Mehmet],
Microscale Image Enhancement Via PCA and Well-Exposedness Maps,
ICIP22(2092-2096)
IEEE DOI 2211
Histograms, Visualization, Laplace equations, Image color analysis, Microscopy, Lighting, Colored noise, Microscale image enhancement, principle component analysis BibRef

Chattopadhyay, S., Malachowski, A., Swain, J.K., Dalmo, R.A., Horsch, A., Prasad, D.K.,
Mapping Functional Changes in the Embryonic Heart of Atlantic Salmon Post Viral Infection Using AI Technique,
ICIP22(3101-3105)
IEEE DOI 2211
Heart, Perturbation methods, Microscopy, Heuristic algorithms, Fish, Salmon fish, Embryonic heart BibRef

Pedraza, A.[Anibal], Ruiz-Santaquiteria, J.[Jesus], Deniz, O.[Oscar], Bueno, G.[Gloria],
Parasitic Egg Detection and Classification with Transformer-Based Architectures,
ICIP22(4301-4305)
IEEE DOI 2211
Microscopy, Object detection, Medical services, Transformers, Object recognition, Task analysis, Deep Learning, Object Detection, Parasitic Eggs BibRef

Pratama, Y.[Yohanssen], Fujimura, Y.[Yuki], Funatomi, T.[Takuya], Mukaigawa, Y.[Yasuhiro],
Parasitic Egg Detection and Classification by Utilizing the YOLO Algorithm with Deep Latent Space Image Restoration and GrabCut Augmentation,
ICIP22(4311-4315)
IEEE DOI 2211
Image resolution, Microscopy, Lighting, Object detection, Detectors, Classification algorithms, Image restoration, Classification, YOLO BibRef

Ruiz-Santaquiteria, J.[Jesus], Pedraza, A.[Anibal], Vallez, N.[Noelia], Velasco, A.[Alberto],
Parasitic Egg Detection with a Deep Learning Ensemble,
ICIP22(4283-4286)
IEEE DOI 2211
Deep learning, Object detection, Medical services, Manuals, Task analysis, Parasitic Eggs, Object Detection, Neural networks, Deep Learning BibRef

Aung, Z.H.[Zaw Htet], Srithaworn, K.[Kittinan], Achakulvisut, T.[Titipat],
Multitask learning via pseudo-label generation and ensemble prediction for parasitic egg cell detection: IEEE ICIP Challenge 2022,
ICIP22(4273-4277)
IEEE DOI 2211
Training, Representation learning, Head, Computational modeling, Parasitic diseases, Training data, Object detection, parasitic egg BibRef

Tureckova, A.[Alzbeta], Turecek, T.[Tomas], Oplatkova, Z.K.[Zuzana Kominkova],
ICIP 2022 Challenge: PEDCMI, TOOD Enhanced by Slicing-Aided Fine-Tuning and Inference,
ICIP22(4292-4295)
IEEE DOI 2211
Deep learning, Training, Analytical models, Data analysis, Image resolution, Microscopy, Pipelines, Deep Learning Pipeline, Slicing Aided Inference BibRef

AlDahoul, N.[Nouar], Karim, H.A.[Hezerul Abdul], Kee, S.L.[Shaira Limson], Tan, M.J.T.[Myles Joshua Toledo],
Localization and Classification of Parasitic Eggs in Microscpic Images Using An Efficientdet Detector,
ICIP22(4253-4257)
IEEE DOI 2211
Location awareness, Training, Microscopy, Detectors, Pattern recognition, EfficientDet, microscopic image, parasitic egg BibRef

Wang, Y.Q.[Yu-Qi], He, Z.Q.[Zhi-Qiang], Huang, S.H.[Sheng-Hui], Du, H.B.[Hua-Bin],
A Robust Ensemble Model For Parasitic Egg Detection And Classification,
ICIP22(4258-4262)
IEEE DOI 2211
Deep learning, Biological system modeling, Microscopy, Transfer learning, Interference, Feature extraction, Robustness, robustness BibRef

Anantrasirichai, N.[Nantheera], Chalidabhongse, T.H.[Thanarat H.], Palasuwan, D.[Duangdao], Naruenatthanaset, K.[Korranat], Kobchaisawat, T.[Thananop], Nunthanasup, N.[Nuntiporn], Boonpeng, K.[Kanyarat], Ma, X.D.[Xu-Dong], Achim, A.[Alin],
ICIP 2022 Challenge on Parasitic Egg Detection and Classification in Microscopic Images: Dataset, Methods and Results,
ICIP22(4306-4310)
IEEE DOI 2211
Deep learning, Image color analysis, Microscopy, Brightness, Object detection, Manuals, Colored noise, parasitic egg, deep learning BibRef

Zhang, H.[Hongrun], Meng, Y.[Yanda], Zhao, Y.T.[Yi-Tian], Qiao, Y.H.[Yi-Hong], Yang, X.Y.[Xiao-Yun], Coupland, S.E.[Sarah E.], Zheng, Y.L.[Ya-Lin],
DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification,
CVPR22(18780-18790)
IEEE DOI 2210
Measurement, Image resolution, Histopathology, Lung cancer, Feature extraction, Pattern recognition, Medical, Self- semi- meta- unsupervised learning BibRef

Albuquerque, T.[Tomé], Moreira, A.[Ana], Cardoso, J.S.[Jaime S.],
Deep Ordinal Focus Assessment for Whole Slide Images,
CDPath21(657-663)
IEEE DOI 2112
Training, Measurement, Image quality, Decision support systems, Manufacturing processes, Databases, Computational modeling BibRef

Theelke, L.[Luisa], Wilm, F.[Frauke], Marzahl, C.[Christian], Bertram, C.A.[Christof A.], Klopfleisch, R.[Robert], Maier, A.[Andreas], Aubreville, M.[Marc], Breininger, K.[Katharina],
Iterative Cross-Scanner Registration for Whole Slide Images,
CDPath21(582-590)
IEEE DOI 2112
Pathology, Image resolution, Microscopy, Estimation, Registers BibRef

Yang, K.[Karren], Goldman, S.[Samuel], Jin, W.[Wengong], Lu, A.X.[Alex X.], Barzilay, R.[Regina], Jaakkola, T.[Tommi], Uhler, C.[Caroline],
Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis,
CVPR21(6684-6694)
IEEE DOI 2111
Training, Measurement, Image resolution, Image synthesis, Microprocessors, Biological system modeling, Microscopy BibRef

Li, B.[Bin], Li, Y.[Yin], Eliceiri, K.W.[Kevin W.],
Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning,
CVPR21(14313-14323)
IEEE DOI 2111
Training, Location awareness, Image resolution, Annotations, Feature extraction, Distance measurement, Pattern recognition BibRef

Venkataramanan, A.[Aishwarya], Laviale, M.[Martin], Figus, C.[Cécile], Usseglio-Polatera, P.[Philippe], Pradalier, C.[Cédric],
Tackling Inter-class Similarity and Intra-class Variance for Microscopic Image-Based Classification,
CVS21(93-103).
Springer DOI 2109
BibRef

Shrivastava, A.[Aman], Adorno, W.[William], Sharma, Y.[Yash], Ehsan, L.[Lubaina], Ali, S.A.[S. Asad], Moore, S.R.[Sean R.], Amadi, B.[Beatrice], Kelly, P.[Paul], Syed, S.[Sana], Brown, D.E.[Donald E.],
Self-attentive Adversarial Stain Normalization,
AIDP20(120-140).
Springer DOI 2103
Stained biopsy images. BibRef

Zhang, Q.C.[Qing-Chao], Heldermon, C.D.[Coy D.], Toler-Franklin, C.[Corey],
Multiscale Detection of Cancerous Tissue in High Resolution Slide Scans,
ISVC20(II:139-153).
Springer DOI 2103
BibRef

Page, J., Favaros, P.,
Learning to Model and Calibrate Optics Via a Differentiable Wave Optics Simulator,
ICIP20(2995-2999)
IEEE DOI 2011
Microscopy, Optical diffraction, Optical imaging, Adaptive optics, Optical sensors, Computational modeling, Lenses, PSF engineering, differentiable simulator BibRef

Aktar, R., Huxley, V.H., Guidoboni, G., Ali Akbarpour, H., Bunyak, F., Palaniappan, K.,
Mosaicing of Dynamic Mesentery Video with Gradient Blending,
ICIP20(563-567)
IEEE DOI 2011
Correlation, Robustness, Microscopy, Feature extraction, Biomedical imaging, Image edge detection, Image registration, Gradient blending BibRef

Sandhan, T., Choi, J.Y.,
Separating Particulate Matter From a Single Microscopic Image,
CVPR20(4583-4592)
IEEE DOI 2008
Microscopy, Glass, Optical microscopy, Visualization, Diffraction, Lenses BibRef

Miolane, N.[Nina], Holmes, S.,
Learning Weighted Submanifolds With Variational Autoencoders and Riemannian Variational Autoencoders,
CVPR20(14491-14499)
IEEE DOI 2008
Manifolds, Data models, Principal component analysis, Task analysis, Probabilistic logic, Biomedical imaging, Uncertainty BibRef

Miolane, N.[Nina], Poitevin, F., Li, Y., Holmes, S.,
Estimation of Orientation and Camera Parameters from Cryo-Electron Microscopy Images with Variational Autoencoders and Generative Adversarial Networks,
Microscopy20(4174-4183)
IEEE DOI 2008
Space vehicles, Orbits, Cameras, Estimation, Image reconstruction BibRef

Weigert, M., Schmidt, U., Haase, R., Sugawara, K., Myers, G.,
Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy,
WACV20(3655-3662)
IEEE DOI 2006
Shape, Microscopy, Image segmentation, Anisotropic magnetoresistance, Training BibRef

Khan, S.S.[Salman Siddique], Adarsh, V.R., Boominathan, V.[Vivek], Tan, J.[Jasper], Veeraraghavan, A.[Ashok], Mitra, K.[Kaushik],
Towards Photorealistic Reconstruction of Highly Multiplexed Lensless Images,
ICCV19(7859-7868)
IEEE DOI 2004
cameras, image reconstruction, image sensors, neural nets, optical microscopes, miniature cameras, distributed monitoring, Lenses BibRef

Singh, H.[Harbinder], Sánchez, C.[Carlos], Cristóbal, G.[Gabriel], Bueno, G.[Gloria],
Pencil Drawing of Microscopic Images Through Edge Preserving Filtering,
IbPRIA19(II:189-200).
Springer DOI 1910
BibRef

Forero, M.G.[Manuel G.], Arias-Rubio, C.[Carlos], Horta-Júnior, J.D.C.[José De_Anchieta C.], López, D.E.[Dolores E.],
A Note on Gradient-Based Intensity Normalization,
IbPRIA19(I:161-169).
Springer DOI 1910
Builds on work of Sintorn.
See also Gradient based intensity normalization. BibRef

Tran, D.H.[Duc Hoa], Meunier, M.[Michel], Cheriet, F.[Farida],
WaveM-CNN for Automatic Recognition of Sub-cellular Organelles,
ICIAR19(I:186-194).
Springer DOI 1909
BibRef

Zafari, S.[Sahar], Eerola, T.[Tuomas], Ferreira, P.[Paulo], Kälviäinen, H.[Heikki], Bovik, A.[Alan],
Automated Segmentation of Nanoparticles in BF TEM Images by U-Net Binarization and Branch and Bound,
CAIP19(I:113-125).
Springer DOI 1909
BibRef

Bhugra, S.[Swati], Mishra, D.[Deepak], Anupama, A.[Anupama], Chaudhury, S.[Santanu], Lall, B.[Brejesh], Chugh, A.[Archana], Chinnusamy, V.[Viswanathan],
Deep Convolutional Neural Networks Based Framework for Estimation of Stomata Density and Structure from Microscopic Images,
BioIm18(VI:412-423).
Springer DOI 1905
BibRef

Basu, S., Rexhepaj, E., Spassky, N., Genovesio, A., Paulsen, R.R., Shihavuddin, A.S.M.,
FastSME: Faster and Smoother Manifold Extraction from 3D Stack,
Microscopy18(2362-23628)
IEEE DOI 1812
Manifolds, Indexes, Biology, Microscopy, Optimization BibRef

Aziz, A., Pande, H., Cheluvaraju, B., Dastidar, T.R.,
Improved Extraction of Objects from Urine Microscopy Images with Unsupervised Thresholding and Supervised U-net Techniques,
Microscopy18(2311-23118)
IEEE DOI 1812
Image segmentation, Microscopy, Image edge detection, Shape, Transforms, Microorganisms BibRef

Levis, A., Schechner, Y.Y., Talmon, R.,
Statistical Tomography of Microscopic Life,
CVPR18(6411-6420)
IEEE DOI 1812
Microscopy, Tomography, Estimation, Organisms BibRef

Zhang, T., Carvajal, J., Smith, D.F., Zhao, K., Wiliem, A., Hobson, P., Jennings, A., Lovell, B.C.,
SlideNet: Fast and Accurate Slide Quality Assessment Based on Deep Neural Networks,
ICPR18(2314-2319)
IEEE DOI 1812
Task analysis, Quality assessment, Microscopy, Neural networks, Microorganisms, Image analysis, Support vector machines BibRef

Yang, C., Bu, X., Ma, H., Zhang, L., Cao, X., Yue, T., Hua, X., Yan, F.,
Resolution-Enhanced Lensless Color Shadow Imaging Microscopy Based on Large Field-of-View Submicron-Pixel Imaging Sensors,
Microscopy18(2327-23277)
IEEE DOI 1812
Microscopy, Sensors, Image sensors, Spatial resolution, Lighting BibRef

Chen, X., Xie, Q., Shen, L., Han, H.,
Morphology-Retained Non-Linear Image Registration of Serial Electron Microscopy Sections,
ICIP18(3833-3837)
IEEE DOI 1809
Image reconstruction, Biological tissues, Image registration, Reliability, Scanning electron microscopy, Image registration, correspondence extraction BibRef

Liu, D.N.[Dong-Nan], Zhang, D.H.[Dong-Hao], Song, Y.[Yang], Zhang, C.Y.[Chao-Yi], Huang, H.[Heng], Chen, M.[Mei], Cai, W.D.[Wei-Dong],
Large Kernel Refine Fusion Net for Neuron Membrane Segmentation,
Microscopy18(2293-22938)
IEEE DOI 1812
Kernel, Image segmentation, Task analysis, Image resolution, Neurons, Decoding, Biomembranes BibRef

Liu, D.N.[Dong-Nan], Zhang, D.H.[Dong-Hao], Liu, S.Q.[Si-Qi], Song, Y.[Yang], Jia, H.Z.[Hao-Zhe], Feng, D.D.[David Dagan], Xia, Y.[Yong], Cai, W.D.[Wei-Dong],
Densely Connected Large Kernel Convolutional Network for Semantic Membrane Segmentation in Microscopy Images,
ICIP18(2461-2465)
IEEE DOI 1809
Kernel, Image segmentation, Decoding, Neurons, Image resolution, Semantics, Microscopy, neuronal boundary segmentation, deep neural network BibRef

Luo, Y., Andersson, S.B.,
Sampling pattern design algorithm for atomic force microscopy images,
ICIP17(2109-2113)
IEEE DOI 1803
Force, Image reconstruction, Imaging, Indexes, Matching pursuit algorithms, Surface topography, sampling pattern design BibRef

Cheng, H.C., Cardone, A., Varshney, A.,
Interactive exploration of microstructural features in gigapixel microscopy images,
ICIP17(335-339)
IEEE DOI 1803
Image color analysis, Image resolution, Image segmentation, Intestines, Microscopy, Muscles, Visualization, Image segmentation, gigapixel images BibRef

Saponaro, P., Treible, W., Kolagunda, A., Rhein, S., Caplan, J., Kambhamettu, C., Wisser, R.,
Three-dimensional segmentation of vesicular networks of fungal hyphae in macroscopic microscopy image stacks,
ICIP17(3285-3289)
IEEE DOI 1803
Feature extraction, Generators, Image edge detection, Image segmentation, Microscopy, Skeleton, Fungal Hyphae, Skeletonization BibRef

Sadanandan, S.K.[Sajith Kecheril], Karlsson, J.[Johan], Wählby, C.[Carolina],
Spheroid Segmentation Using Multiscale Deep Adversarial Networks,
BioIm17(36-41)
IEEE DOI 1802
Convolution, Feature extraction, Image segmentation, Neural networks, Shape, Training BibRef

Hast, A.[Anders], Kylberg, G.[Gustaf], Sintorn, I.M.[Ida-Maria],
An Efficient Descriptor Based on Radial Line Integration for Fast Non-invariant Matching and Registration of Microscopy Images,
ACIVS17(723-734).
Springer DOI 1712
BibRef

Han, L.[Liang], Yin, Z.Z.[Zhao-Zheng],
Transferring Microscopy Image Modalities with Conditional Generative Adversarial Networks,
Microscopy17(851-859)
IEEE DOI 1709
Biology, Generators, Image segmentation, Interference, Microscopy, Visualization BibRef

Saponaro, P., Treible, W., Kolagunda, A., Chaya, T., Caplan, J., Kambhamettu, C., Wisser, R.,
DeepXScope: Segmenting Microscopy Images with a Deep Neural Network,
Microscopy17(843-850)
IEEE DOI 1709
Computer architecture, Image segmentation, Measurement, Microscopy, Training, Training, data BibRef

Bao, J., Fan, J., Hu, X., Wang, J., Wang, L.,
An effective consistency correction and blending method for camera-array-based microscopy imaging,
WSSIP17(1-5)
IEEE DOI 1707
Cameras, Distribution functions, Lenses, Lighting, Mathematical model, Microscopy, Camera Array, Consistency Correction, Illumination Compensation, Improved Alpha Blending, Response Function, Vignetting, Compensation BibRef

Koos, K.[Krisztian], Molnár, J.[József], Horvath, P.[Peter],
Pipette Hunter: Patch-Clamp Pipette Detection,
SCIA17(I: 172-183).
Springer DOI 1706
detect the tip of glass pipettes in microscopy images BibRef

Godehardt, M.[Michael], Schladitz, K.[Katja], Dietrich, S.[Sascha], Meyndt, R.[Renate], Schulz, H.[Haiko],
Segmentation of Collagen Fiber Bundles in 3D by Waterfall on Orientations,
ISMM17(447-454).
Springer DOI 1706
BibRef

Zelenka, C., Koch, R.,
Restoration of images with wavefront aberrations,
ICPR16(1388-1393)
IEEE DOI 1705
Adaptive optics, Fourier transforms, Image restoration, Microscopy, Optical distortion, Optical, imaging BibRef

Ambikumar, A.S., Bailey, D.G., Gupta, G.S.,
Extending the depth of field in microscopy: A review,
ICVNZ16(1-6)
IEEE DOI 1701
Image fusion BibRef

Almutairi, Y., Cootes, T., Kadler, K.,
Analysing the Structure of Collagen Fibres in SBFSEM Images,
Microscopy16(1342-1349)
IEEE DOI 1612
BibRef

Roels, J.[Joris], de Vylder, J.[Jonas], Saeys, Y.[Yvan], Goossens, B.[Bart], Philips, W.[Wilfried],
Decreasing Time Consumption of Microscopy Image Segmentation Through Parallel Processing on the GPU,
ACIVS16(147-159).
Springer DOI 1611
BibRef

Akram, S.U., Kannala, J., Eklund, L., Heikkilä, J.,
Cell proposal network for microscopy image analysis,
ICIP16(3199-3203)
IEEE DOI 1610
Feature extraction BibRef

Cossairt, O., He, K., Shang, R., Matsuda, N., Sharma, M., Huang, X., Katsaggelos, A.K., Spinoulas, L., Yoo, S.,
Compressive reconstruction for 3D incoherent holographic microscopy,
ICIP16(958-962)
IEEE DOI 1610
Coherence BibRef

Maska, M.[Martin], Ederra, C.[Cristina], Fernandez-Marques, J.[Javier], Munoz-Barrutia, A.[Arrate], Kozubek, M.[Michal], Ortiz-de-Solorzano, C.[Carlos],
Quantification of the 3D collagen network geometry in confocal reflection microscopy,
ICIP15(1791-1794)
IEEE DOI 1512
Collagen network geometry BibRef

Kharma, N.[Nawwaf], Ebne-Alian, M.[Mohammad], Charbonneau, L.[Louis],
Evolution of Programs for Segmentation of Microscopic Images,
CRV15(253-260)
IEEE DOI 1507
Atmospheric measurements BibRef

Kromwijk, S.[Sander], Lefkimmiatis, S.[Stamatios], Unser, M.[Michael],
High-performance 3D deconvolution of fluorescence micrographs,
ICIP14(1718-1722)
IEEE DOI 1502
Deconvolution BibRef

Lukes, T.[Tomas], Kehrt, D.[Daniel], Fliegel, K.[Karel], Klima, M.[Milos],
Binarization of noisy microscopy images through signal reconstruction using iterative detection network,
ICIP14(3949-3952)
IEEE DOI 1502
Cameras BibRef

Svoboda, D.[David], Ulman, V.[Vladimir], Matyska, L.[Ludek], Maska, M.[Martin], Bella, J.[Jan], Stejskal, S.[Stanislav],
On proper simulation of phenomena influencing image formation in fluorescence microscopy,
ICIP14(3944-3948)
IEEE DOI 1502
Biological system modeling BibRef

Seelamantula, C.S.[Chandra Sekhar], Shenoy, B.A.[Basty Ajay], Coquoz, S.[Severine], Lasser, T.[Theo],
Exact reconstruction in Quantitative Phase Microscopy,
ICIP14(3934-3938)
IEEE DOI 1502
Fourier transforms BibRef

Soubies, E.[Emmanuel], Blanc-Feraud, L.[Laure], Schaub, S.[Sebastien], Aubert, G.[Gilles],
Sparse reconstruction from Multiple-Angle Total Internal Reflection fluorescence Microscopy,
ICIP14(2844-2848)
IEEE DOI 1502
Approximation methods BibRef

Martinez-Corral, M., Llavador, A., Sanchez-Ortiga, E., Saavedra, G.,
Time-multiplexing Integral Microscopy,
3DTV-CON14(1-4)
IEEE DOI 1409
cameras BibRef

Fan, Y.L.[Yi-Lun], Gal, Y., Bradley, A.P.,
Performance Analysis of Three Microscope Slide Scanning Techniques,
DICTA13(1-6)
IEEE DOI 1402
biomedical optical imaging BibRef

Ramesh, N.[Nisha], Otsuna, H.[Hideo], Tasdizen, T.[Tolga],
Three-dimensional alignment and merging of confocal microscopy stacks,
ICIP13(1447-1450)
IEEE DOI 1402
Confocal Microscopy BibRef

Altinay, D.[Doreen], Bradley, A.P.[Andrew P.],
Illumination Effects in Quantitative Virtual Microscopy,
CAIP13(II:449-456).
Springer DOI 1311
BibRef

Rana, M.S., Pota, H.R., Petersen, I.R.,
High performance control of atomic force microscope for high-speed image scanning,
ICARCV12(1187-1192).
IEEE DOI 1304
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Kemmler, M.[Michael], Denzler, J.[Joachim],
Finding discriminative features for Raman spectroscopy,
ICPR12(1823-1826).
WWW Link. 1302
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Ponti, M.P.[Moacir P.],
Improving restoration of microscopy images using iterative prototypes and a sequence of support constraints,
ICIP12(3065-3068).
IEEE DOI 1302
BibRef

Lou, X.H.[Xing-Hua], Fiaschi, L.[Luca], Koethe, U.[Ullrich], Hamprecht, F.A.[Fred A.],
Quality Classification of Microscopic Imagery with Weakly Supervised Learning,
MLMI12(176-183).
Springer DOI 1211
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Cao, T.[Tian], Zach, C.[Christopher], Modla, S.[Shannon], Powell, D.[Debbie], Czymmek, K.[Kirk], Niethammer, M.[Marc],
Registration for Correlative Microscopy Using Image Analogies,
WBIR12(296-306).
Springer DOI 1208
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Altinay, D., Bradley, A.P.,
An Evaluation of Multi-resolution Microscope Slide Scanning Algorithms,
DICTA11(319-324).
IEEE DOI 1205
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Pietschmann, J.F.[Jan-Frederik], Schlake, B.[Barbel],
Lane formation in a microscopic model and the corresponding partial differential equation,
MSVALC11(173-179).
IEEE DOI 1201
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Kim, H.C.[Hee-Chang], Stamon, G.[Georges], Genovesio, A.[Auguste],
A method for discontinuous neurite reconstruction based on diffusion tensor, Hessian eigenvector, and diffused gradient vector fields,
ICIP11(1401-1404).
IEEE DOI 1201
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Burton, D.R., Murphy, M.F., Lilley, F., Gdeisat, M.A.,
The formulation of a non-linear hertzian model in order to assess the mechanical strength of human cells based on data from an atomic force microscope,
ICIP11(3061-3064).
IEEE DOI 1201
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Ahtaiba, A., Gdeisat, M.A., Burton, D.R., Lilley, F., Murphy, M.F., Johnston, G.,
A novel technique for the restoration of atomic force microscope images enabling an approximation of AFM impulse response,
ICIP11(3065-3068).
IEEE DOI 1201
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Johnston, G., Burton, D.R., Lilley, F., Doyle, A., Murphy, M.F., Madden, G., Gdeisat, M.A., Moore, C.J., Marchant, T.E., Matuszewski, B.,
Analysis of microscopy and reconstructive images for applications in medicine and biology,
ICIP11(3069-3072).
IEEE DOI 1201
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Pop, S.[Sorin], Dufour, A.[Alexandre], Olivo-Marin, J.C.[Jean-Christophe],
Image filtering using anisotropic structure tensor for cell membrane enhancement in 3D microscopy,
ICIP11(2041-2044).
IEEE DOI 1201
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Makkapati, V.V.[Vishnu V.], Pathangay, V.,
Adaptive Color Illumination for Microscopes,
NCC11(1-5).
IEEE DOI BibRef 1100

Lalys, F.[Florent], Riffaud, L.[Laurent], Morandi, X.[Xavier], Jannin, P.[Pierre],
Surgical Phases Detection from Microscope Videos by Combining SVM and HMM,
MCV10(54-62).
Springer DOI 1009
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Altinay, D.[Doreen], Bradley, A.P.[Andrew P.], Mehnert, A.J.H.[Andrew J.H.],
On the Estimation of Extrinsic and Intrinsic Parameters of Optical Microscope Calibration,
DICTA10(190-195).
IEEE DOI 1012
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Yang, H.F.[Huei-Fang], Choe, Y.S.[Yoon-Suck],
Ground Truth Estimation by Maximizing Topological Agreements in Electron Microscopy Data,
ISVC11(I: 371-380).
Springer DOI 1109
BibRef
Earlier:
Electron Microscopy Image Segmentation with Graph Cuts Utilizing Estimated Symmetric Three-Dimensional Shape Prior,
ISVC10(II: 322-331).
Springer DOI 1011
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Suksmono, A.B.[Andriyan B.], Hirose, A.[Akira],
Numerical reconstruction of holographic microscopy images based on matching pursuits on a pair of domains,
ICIP10(4197-4200).
IEEE DOI 1009
BibRef

Zhang, X.[Xin], Lam, E.Y.[Edmund Y.],
Edge detection of three-dimensional objects by manipulating pupil functions in an optical scanning holography system,
ICIP10(3661-3664).
IEEE DOI 1009
BibRef

Wortmann, T.[Tim], Fatikow, S.[Sergej],
Carbon Nanotube Detection by Scanning Electron Microscopy,
MVA09(370-).
PDF File. 0905
BibRef

Usenik, P.[Peter], Vrtovec, T.[Tomaz], Pernus, F.[Franjo], Likar, B.[Bostjan],
Automated Tracking of Vesicles in Phase Contrast Microscopy Images,
ICPR10(2520-2523).
IEEE DOI 1008
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Kemmler, M.[Michael], Denzler, J.[Joachim], Rösch, P.[Petra], Popp, J.[Jürgen],
Classification of Microorganisms via Raman Spectroscopy Using Gaussian Processes,
DAGM10(81-90).
Springer DOI 1009

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Mele, K.[Katarina], Coster, A.[Adelle], Burchfield, J.G.[James G.], Lopez, J.[Jamie], James, D.E.[David E.], Hughes, W.E.[William E.], Vallotton, P.[Pascal],
Automatic identification of fusion events in TIRF microscopy image sequences,
ObjectEvent09(578-584).
IEEE DOI 0910
BibRef

Adeyemi, A.A., Darcie, T.E.,
Programmable Point-Source Digital In-Line Holographic Microscope with Enhanced Field of View,
CISP09(1-4).
IEEE DOI 0910
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Marim, M.M.[Marcio M.], Angelini, E.D.[Elsa D.], Olivo-Marin, J.C.,
Compressed Sensing in microscopy with random projections in the Fourier domain,
ICIP09(2121-2124).
IEEE DOI 0911
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Gadgil, N.J., Salama, P., Dunn, K.W., Delp, E.J.,
Nuclei segmentation of fluorescence microscopy images based on midpoint analysis and marked point process,
Southwest16(37-40)
IEEE DOI 1605
Biology BibRef

Lorenz, K.S.[Kevin S.], Serrano, F.[Francisco], Salama, P.[Paul], Delp, E.J.[Edward J.],
Segmentation and registration based analysis of microscopy images,
ICIP09(4213-4216).
IEEE DOI 0911
BibRef

Wurflinger, T.[Thomas], Sechi, A.S.[Antonio S.], Aach, T.[Til],
Segmentation, tracking, and analysis of focal adhesion dynamics in cellular microscopy imaging,
ICIP09(4209-4212).
IEEE DOI 0911
BibRef

Zhao, Y.[Yang], Xiong, H.K.[Hong-Kai], Zhang, K.[Kai], Zhou, X.B.[Xiao-Bo],
Equilibrium modeling for 3D curvilinear structure tracking of confocal microscopy images,
ICIP09(2533-2536).
IEEE DOI 0911
BibRef

Misiak, D.[Danny], Posch, S.[Stefan], Stohr, N.[Nadine], Huttelmaier, S.[Stefan], Moller, B.[Birgit],
Automatic analysis of flourescence labeled neurites in microscope images,
WACV09(1-7).
IEEE DOI 0912
BibRef

Rong, W.[Wen], Chen, H.[Hui], Liu, J.J.[Jia-Ju], Xu, Y.Y.[Yan-Yan], Haeusler, R.,
Mosaicing of microscope images based on SURF,
IVCNZ09(271-275).
IEEE DOI 0911
BibRef

Zhang, H.Z.[Huai-Zhong], Rodriguez, E.P.[E. Patricia], Morrow, P.J.[Philip J.], McClean, S.[Sally], Saetzler, K.[Kurt],
MCMC-Based Algorithm to Adjust Scale Bias in Large Series of Electron Microscopical Ultrathin Sections,
CAIP09(557-564).
Springer DOI 0909
BibRef

Zhang, H.Z.[Huai-Zhong], Morrow, P.J.[Philip J.], McClean, S.[Sally], Saetzler, K.[Kurt],
Contour Detection of Labelled Cellular Structures from Serial Ultrathin Electron Microscopy Sections using GAC and Prior Analysis,
IPTA08(1-7).
IEEE DOI 0811
BibRef

Vazquez, Y., Bravo, A., Mantilla, J., Alayon, M.,
Automatic Approach for Emulsions Stability Assessment in Microscope Images,
DICTA08(162-167).
IEEE DOI 0812
BibRef

Hamey, L.G.C.[Leonard G. C.], Connally, R.E.[Russell E.], Yen, S.W.T.[Simon Wong Too], Lawson, T.S.[Thomas S.], Piper, J.A.[James A.], Iredell, J.[Jon],
Luminescent Microspheres Resolved from Strong Background on an Automated Time-Gated Luminescence Microscopy Workstation,
DICTA09(223-228).
IEEE DOI 0912
BibRef

Alexander, S.K., Azencott, R., Bodmann, B.G., Bouamrani, A., Chiappini, C., Ferrari, M., Liu, X., Tasciotti, E.,
SEM Image Analysis for Quality Control of Nanoparticles,
CAIP09(590-597).
Springer DOI 0909
SEM: scanning electronic microscopy. BibRef

Tsaftaris, S.A., Zujovic, J., Katsaggelos, A.K.,
Automated line flattening of Atomic Force Microscopy images,
ICIP08(2968-2971).
IEEE DOI 0810
BibRef

Karlsson, A.[Adam], Overgaard, N.C.[Niels C.], Heyden, A.[Anders],
A Two-Step Area Based Method for Automatic Tight Segmentation of Zona Pellucida in HMC Images of Human Embryos,
ScaleSpace05(503-514).
Springer DOI 0505
BibRef
And:
Automatic segmentation of Zona pellucida in HMC images of human embryos,
ICPR04(III: 518-521).
IEEE DOI 0409
Hoffman modulation contrast microscopy BibRef

van Kempen, G.M.P., van den Brink, N., van Vliet, L.J., van Ginkel, M., Verbeek, P.W., Blonk, H.,
The Application of a Local Dimensionality Estimator to the Analysis of 3-D Microscopic Network Structures,
SCIA99(Biological Applications II). BibRef 9900

Brugal, G.,
Pattern recognition, image processing, related data analysis and expert systems integrated in medical microscopy,
ICPR88(I: 286-293).
IEEE DOI 8811
BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Super Resolution in Microscope Image Analysis .


Last update:Mar 16, 2024 at 20:36:19